Improved AWS-Certified-Data-Engineer-Associate Exam Dumps For AWS Certified Data Engineer - Associate (DEA-C01) Certification

NEW QUESTION 1
A data engineer needs to join data from multiple sources to perform a one-time analysis job. The data is stored in Amazon DynamoDB, Amazon RDS, Amazon Redshift, and Amazon S3.
Which solution will meet this requirement MOST cost-effectively?

• A. Use an Amazon EMR provisioned cluster to read from all source
• B. Use Apache Spark to join the data and perform the analysis.
• C. Copy the data from DynamoDB, Amazon RDS, and Amazon Redshift into Amazon S3. Run Amazon Athena queries directly on the S3 files.
• D. Use Amazon Athena Federated Query to join the data from all data sources.
• E. Use Redshift Spectrum to query data from DynamoDB, Amazon RDS, and Amazon S3 directly from Redshift.

Answer: C

Explanation:
Amazon Athena Federated Query is a feature that allows you to query data from multiple sources using standard SQL. You can use Athena Federated Query to join data from Amazon DynamoDB, Amazon RDS, Amazon Redshift, and Amazon S3, as well as other data sources such as MongoDB, Apache HBase, and Apache Kafka1. Athena Federated Query is a serverless and interactive service, meaning you do not need to provision or manage any infrastructure, and you only pay for the amount of data scanned by your queries. Athena Federated Query is the most cost-effective solution for performing a one-time analysis job on data from multiple sources, as it eliminates the need to copy or move data, and allows you to query data directly from the source.
The other options are not as cost-effective as Athena Federated Query, as they involve additional steps or costs. Option A requires you to provision and pay for an Amazon EMR cluster, which can be expensive and time-consuming for a one-time job. Option B requires you to copy or move data from DynamoDB, RDS, and Redshift to S3, which can incur additional costs for data transfer and storage, and also introduce latency and complexity. Option D requires you to have an existing Redshift cluster, which can be costly and may not be necessary for a one-time job. Option D also does not supportquerying data from RDS directly, so you would need to use Redshift Federated Query to access RDS data, which adds another layer of complexity2. References:
✑ Amazon Athena Federated Query
✑ Redshift Spectrum vs Federated Query

NEW QUESTION 2
A company uses Amazon Athena to run SQL queries for extract, transform, and load (ETL) tasks by using Create Table As Select (CTAS). The company must use Apache Spark instead of SQL to generate analytics.
Which solution will give the company the ability to use Spark to access Athena?

• A. Athena query settings
• B. Athena workgroup
• C. Athena data source
• D. Athena query editor

Answer: C

Explanation:
Athena data source is a solution that allows you to use Spark to access Athena by using the Athena JDBC driver and the Spark SQL interface. You can use the Athena data source to create Spark DataFrames from Athena tables, run SQL queries on the DataFrames, and write the results back to Athena. The Athena data source supports various data formats, such as CSV, JSON, ORC, and Parquet, and also supports partitioned and bucketed tables. The Athena data source is a cost-effective and scalable way to use Spark to access Athena, as it does not require any additional infrastructure or services, and you only pay for the data scanned by Athena.
The other options are not solutions that give the company the ability to use Spark to access Athena. Option A, Athena query settings, is a feature that allows you to configure various parameters for your Athena queries, such as the output location, the encryption settings, the query timeout, and the workgroup. Option B, Athena workgroup, is a feature that allows you to isolate and manage your Athena queries and resources, such as the query history, the query notifications, the query concurrency, and the query cost. Option D, Athena query editor, is a feature that allows you to write and run SQL queries on Athena using the web console or the API. None of these options enable you to use Spark instead of SQL to generate analytics on Athena. References:
✑ Using Apache Spark in Amazon Athena
✑ Athena JDBC Driver
✑ Spark SQL
✑ Athena query settings
✑ [Athena workgroups]
✑ [Athena query editor]

NEW QUESTION 3
A data engineer needs to create an AWS Lambda function that converts the format of data from .csv to Apache Parquet. The Lambda function must run only if a user uploads a .csv file to an Amazon S3 bucket.
Which solution will meet these requirements with the LEAST operational overhead?

• A. Create an S3 event notification that has an event type of s3:ObjectCreated:*. Use a filter rule to generate notifications only when the suffix includes .cs
• B. Set the Amazon Resource Name (ARN) of the Lambda function as the destination for the event notification.
• C. Create an S3 event notification that has an event type of s3:ObjectTagging:* for objects that have a tag set to .cs
• D. Set the Amazon Resource Name (ARN) of the Lambda function as the destination for the event notification.
• E. Create an S3 event notification that has an event type of s3:*. Use a filter rule to generate notifications only when the suffix includes .cs
• F. Set the Amazon Resource Name (ARN) of the Lambda function as the destination for the event notification.
• G. Create an S3 event notification that has an event type of s3:ObjectCreated:*. Use a filter rule to generate notifications only when the suffix includes .cs
• H. Set an Amazon Simple Notification Service (Amazon SNS) topic as the destination for the event notificatio
• I. Subscribe the Lambda function to the SNS topic.

Answer: A

Explanation:
Option A is the correct answer because it meets the requirements with the least operational overhead. Creating an S3 event notification that has an event type of s3:ObjectCreated:* will trigger the Lambda function whenever a new object is created in the S3 bucket. Using a filter rule to generate notifications only when the suffix includes .csv will ensure that the Lambda function only runs for .csv files. Setting the ARN of the Lambda function as the destination for the event notification will directly invoke the Lambda function without any additional steps.
Option B is incorrect because it requires the user to tag the objects with .csv, which adds an extra step and increases the operational overhead.
Option C is incorrect because it uses an event type of s3:*, which will trigger the Lambda function for any S3 event, not just object creation. This could result in unnecessary invocations and increased costs.
Option D is incorrect because it involves creating and subscribing to an SNS topic, which adds an extra layer of complexity and operational overhead.
References:
✑ AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide, Chapter 3: Data Ingestion and Transformation, Section 3.2: S3 Event Notifications and Lambda Functions, Pages 67-69
✑ Building Batch Data Analytics Solutions on AWS, Module 4: Data Transformation, Lesson 4.2: AWS Lambda, Pages 4-8
✑ AWS Documentation Overview, AWS Lambda Developer Guide, Working with AWS Lambda Functions, Configuring Function Triggers, Using AWS Lambda with Amazon S3, Pages 1-5

NEW QUESTION 4
A data engineer runs Amazon Athena queries on data that is in an Amazon S3 bucket. The Athena queries use AWS Glue Data Catalog as a metadata table.
The data engineer notices that the Athena query plans are experiencing a performance bottleneck. The data engineer determines that the cause of the performance bottleneck is the large number of partitions that are in the S3 bucket. The data engineer must resolve the performance bottleneck and reduce Athena query planning time.
Which solutions will meet these requirements? (Choose two.)

• A. Create an AWS Glue partition inde
• B. Enable partition filtering.
• C. Bucketthe data based on a column thatthe data have in common in a WHERE clause of the user query
• D. Use Athena partition projection based on the S3 bucket prefix.
• E. Transform the data that is in the S3 bucket to Apache Parquet format.
• F. Use the Amazon EMR S3DistCP utility to combine smaller objects in the S3 bucket into larger objects.

Answer: AC

Explanation:
The best solutions to resolve the performance bottleneck and reduce Athena query planning time are to create an AWS Glue partition index and enable partition filtering, and to use Athena partition projection based on the S3 bucket prefix.
AWS Glue partition indexes are a feature that allows you to speed up query processing of highly partitioned tables cataloged in AWS Glue Data Catalog. Partition indexes are available for queries in Amazon EMR, Amazon Redshift Spectrum, and AWS Glue ETL jobs. Partition indexes are sublists of partition keys defined in the table. When you create a partition index, you specify a list of partition keys that already exist on a given table. AWS Glue then creates an index for the specified keys and stores it in the Data Catalog. When you run a query that filters on the partition keys, AWS Glue uses the partition index to quickly identify the relevant partitions without scanning the entiretable metadata. This reduces the query planning time and improves the query performance1.
Athena partition projection is a feature that allows you to speed up query processing of highly partitioned tables and automate partition management. In partition projection, Athena calculates partition values and locations using the table properties that you configure directly on your table in AWS Glue. The table properties allow Athena to ‘project’, or determine, the necessary partition information instead of having to do a more time- consuming metadata lookup in the AWS Glue Data Catalog. Because in-memory operations are often faster than remote operations, partition projection can reduce the runtime of queries against highly partitioned tables. Partition projection also automates partition management because it removes the need to manually create partitions in Athena, AWS Glue, or your external Hive metastore2.
Option B is not the best solution, as bucketing the data based on a column that the data have in common in a WHERE clause of the user query would not reduce the query planning time. Bucketing is a technique that divides data into buckets based on a hash function applied to a column. Bucketing can improve the performance of join queries by
reducing the amount of data that needs to be shuffled between nodes. However, bucketing does not affect the partition metadata retrieval, which is the main cause of the performance bottleneck in this scenario3.
Option D is not the best solution, as transforming the data that is in the S3 bucket to Apache Parquet format would not reduce the query planning time. Apache Parquet is a columnar storage format that can improve the performance of analytical queries by reducing the amount of data that needs to be scanned and providing efficient compression and encoding schemes. However, Parquet does not affect the partition metadata retrieval, which is the main cause of the performance bottleneck in this scenario4.
Option E is not the best solution, as using the Amazon EMR S3DistCP utility to combine smaller objects in the S3 bucket into larger objects would not reduce the query planning time. S3DistCP is a tool that can copy large amounts of data between Amazon S3 buckets or from HDFS to Amazon S3. S3DistCP can also aggregate smaller files into larger files to improve the performance of sequential access. However, S3DistCP does not affect the partition metadata retrieval, which is the main cause of the performance bottleneck in this scenario5. References:
✑ Improve query performance using AWS Glue partition indexes
✑ Partition projection with Amazon Athena
✑ Bucketing vs Partitioning
✑ Columnar Storage Formats
✑ S3DistCp
✑ AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide

NEW QUESTION 5
A manufacturing company collects sensor data from its factory floor to monitor and enhance operational efficiency. The company uses Amazon Kinesis Data Streams to publish the data that the sensors collect to a data stream. Then Amazon Kinesis Data Firehose writes the data to an Amazon S3 bucket.
The company needs to display a real-time view of operational efficiency on a large screen in the manufacturing facility.
Which solution will meet these requirements with the LOWEST latency?

• A. Use Amazon Managed Service for Apache Flink (previously known as Amazon Kinesis Data Analytics) to process the sensor dat
• B. Use a connector for Apache Flink to write data to an Amazon Timestream databas
• C. Use the Timestream database as a source to create a Grafana dashboard.
• D. Configure the S3 bucket to send a notification to an AWS Lambda function when any new object is create
• E. Use the Lambda function to publish the data to Amazon Auror
• F. Use Aurora as a source to create an Amazon QuickSight dashboard.
• G. Use Amazon Managed Service for Apache Flink (previously known as Amazon Kinesis Data Analytics) to process the sensor dat
• H. Create a new Data Firehose delivery stream to publish data directly to an Amazon Timestream databas
• I. Use the Timestream database as a source to create an Amazon QuickSight dashboard.
• J. Use AWS Glue bookmarks to read sensor data from the S3 bucket in real tim
• K. Publish the data to an Amazon Timestream databas
• L. Use the Timestream database as a source to create a Grafana dashboard.

Answer: C

Explanation:
This solution will meet the requirements with the lowest latency because it uses Amazon Managed Service for Apache Flink to process the sensor data in real time and write it to Amazon Timestream, a fast, scalable, and serverless time series database. Amazon Timestream is optimized for storing and analyzing time series data, such as sensor data, and can handle trillions of events per day with millisecond latency. By using AmazonTimestream as a source, you can create an Amazon QuickSight dashboard that displays a real-time view of operational efficiency on a large screen in the manufacturing facility. Amazon QuickSight is a fully managed business intelligence service that can connect to various data sources, including Amazon Timestream, and provide interactive visualizations and insights123.
The other options are not optimal for the following reasons:
✑ A. Use Amazon Managed Service for Apache Flink (previously known as Amazon Kinesis Data Analytics) to process the sensor data. Use a connector for Apache Flink to write data to an Amazon Timestream database. Use the Timestream database as a source to create a Grafana dashboard. This option is similar to option C, but it uses Grafana instead of Amazon QuickSight to create the dashboard. Grafana is an open source visualization tool that can also connect to Amazon Timestream, but it requires additional steps to set up and configure, such as deploying a Grafana server on Amazon EC2, installing the Amazon Timestream plugin, and creating an IAM role for Grafana to access Timestream. These steps can increase the latency and complexity of the solution.
✑ B. Configure the S3 bucket to send a notification to an AWS Lambda function when any new object is created. Use the Lambda function to publish the data to Amazon Aurora. Use Aurora as a source to create an Amazon QuickSight dashboard. This option is not suitable for displaying a real-time view of operational efficiency, as it introduces unnecessary delays and costs in the data pipeline. First, the sensor data is written to an S3 bucket by Amazon Kinesis Data Firehose, which can have a buffering interval of up to 900 seconds. Then, the S3 bucket sends a notification to a Lambda function, which can incur additional invocation and execution time. Finally, the Lambda function publishes the data to Amazon Aurora, a relational database that is not optimized for time series data and can have higher storage and performance costs than Amazon Timestream .
✑ D. Use AWS Glue bookmarks to read sensor data from the S3 bucket in real time.
Publish the data to an Amazon Timestream database. Use the Timestream database as a source to create a Grafana dashboard. This option is also not suitable for displaying a real-time view of operational efficiency, as it uses AWS Glue bookmarks to read sensor data from the S3 bucket. AWS Glue bookmarks are a feature that helps AWS Glue jobs and crawlers keep track of the data that has already been processed, so that they can resume from where they left off. However, AWS Glue jobs and crawlers are not designed for real-time data processing, as they can have a minimum frequency of 5 minutes and a variable start-up time. Moreover, this option also uses Grafana instead of Amazon QuickSight to create the dashboard, which can increase the latency and complexity of the solution .
References:
✑ 1: Amazon Managed Streaming for Apache Flink
✑ 2: Amazon Timestream
✑ 3: Amazon QuickSight
✑ : Analyze data in Amazon Timestream using Grafana
✑ : Amazon Kinesis Data Firehose
✑ : Amazon Aurora
✑ : AWS Glue Bookmarks
✑ : AWS Glue Job and Crawler Scheduling

NEW QUESTION 6
A financial company wants to implement a data mesh. The data mesh must support centralized data governance, data analysis, and data access control. The company has decided to use AWS Glue for data catalogs and extract, transform, and load (ETL) operations.
Which combination of AWS services will implement a data mesh? (Choose two.)

• A. Use Amazon Aurora for data storag
• B. Use an Amazon Redshift provisioned cluster for data analysis.
• C. Use Amazon S3 for data storag
• D. Use Amazon Athena for data analysis.
• E. Use AWS Glue DataBrewfor centralized data governance and access control.
• F. Use Amazon RDS for data storag
• G. Use Amazon EMR for data analysis.
• H. Use AWS Lake Formation for centralized data governance and access control.

Answer: BE

Explanation:
A data mesh is an architectural framework that organizes data into domains and treats data as products that are owned and offered for consumption by different teams1. A data mesh requires a centralized layer for data governance and access control, as well as a distributed layer for data storage and analysis. AWS Glue can provide data catalogs and ETL operations for the data mesh, but it cannot provide data governance and access control by itself2. Therefore, the company needs to use another AWS service for this purpose. AWS Lake Formation is a service that allows you to create, secure, and manage data lakes on AWS3. It integrates with AWS Glue and other AWS services to provide centralized data governance and access control for the data mesh. Therefore, option E is correct.
For data storage and analysis, the company can choose from different AWS services depending on their needs and preferences. However, one of the benefits of a data mesh is that it enables data to be stored and processed in a decoupled and scalable way1. Therefore, using serverless or managed services that can handle large volumes and varieties of data is preferable. Amazon S3 is a highly scalable, durable, and secure object storage service that can store any type of data. Amazon Athena is a serverless interactive query service that can analyze data in Amazon S3 using standard SQL. Therefore, option B is a good choice for data storage and analysis in a data mesh. Option A, C, and D are not optimal because they either use relational databases that are not suitable for storing diverse and unstructured data, or they require more management and provisioning than serverless services. References:
✑ 1: What is a Data Mesh? - Data Mesh Architecture Explained - AWS
✑ 2: AWS Glue - Developer Guide
✑ 3: AWS Lake Formation - Features
✑ [4]: Design a data mesh architecture using AWS Lake Formation and AWS Glue
✑ [5]: Amazon S3 - Features
✑ [6]: Amazon Athena - Features

NEW QUESTION 7
A company uses an Amazon QuickSight dashboard to monitor usage of one of the company's applications. The company uses AWS Glue jobs to process data for the dashboard. The company stores the data in a single Amazon S3 bucket. The company adds new data every day.
A data engineer discovers that dashboard queries are becoming slower over time. The data engineer determines that the root cause of the slowing queries is long-running AWS Glue jobs.
Which actions should the data engineer take to improve the performance of the AWS Glue jobs? (Choose two.)

• A. Partition the data that is in the S3 bucke
• B. Organize the data by year, month, and day.
• C. Increase the AWS Glue instance size by scaling up the worker type.
• D. Convert the AWS Glue schema to the DynamicFrame schema class.
• E. Adjust AWS Glue job scheduling frequency so the jobs run half as many times each day.
• F. Modify the 1AM role that grants access to AWS glue to grant access to all S3 features.

Answer: AB

Explanation:
Partitioning the data in the S3 bucket can improve the performance of AWS Glue jobs by reducing the amount of data that needs to be scanned and processed. By organizingthe data by year, month, and day, the AWS Glue job can use partition pruning to filter out irrelevant data and only read the data that matches the query criteria. This can speed up the data processing and reduce the cost of running the AWS Glue job. Increasing the AWS Glue instance size by scaling up the worker type can also improve the performance of AWS Glue jobs by providing more memory and CPU resources for the Spark execution engine. This can help the AWS Glue job handle larger data sets and complex transformations more efficiently. The other options are either incorrect or irrelevant, as they do not affect the performance of the AWS Glue jobs. Converting the AWS Glue schema to the DynamicFrame schema class does not improve the performance, but rather provides additional functionality and flexibility for data manipulation. Adjusting the AWS Glue job scheduling frequency does not improve the performance, but rather reduces the frequency of data updates. Modifying the IAM role that grants access to AWS Glue does not improve the performance, but rather affects the security and permissions of the AWS Glue service. References:
✑ Optimising Glue Scripts for Efficient Data Processing: Part 1 (Section: Partitioning Data in S3)
✑ Best practices to optimize cost and performance for AWS Glue streaming ETL jobs (Section: Development tools)
✑ Monitoring with AWS Glue job run insights (Section: Requirements)
✑ AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide (Chapter 5, page 133)

NEW QUESTION 8
A retail company has a customer data hub in an Amazon S3 bucket. Employees from many countries use the data hub to support company-wide analytics. A governance team must ensure that the company's data analysts can access data only for customers who are within the same country as the analysts.
Which solution will meet these requirements with the LEAST operational effort?

• A. Create a separate table for each country's customer dat
• B. Provide access to each analyst based on the country that the analyst serves.
• C. Register the S3 bucket as a data lake location in AWS Lake Formatio
• D. Use the Lake Formation row-level security features to enforce the company's access policies.
• E. Move the data to AWS Regions that are close to the countries where the customers ar
• F. Provide access to each analyst based on the country that the analyst serves.
• G. Load the data into Amazon Redshif
• H. Create a view for each countr
• I. Create separate 1AM roles for each country to provide access to data from each countr
• J. Assign the appropriate roles to the analysts.

Answer: B

Explanation:
AWS Lake Formation is a service that allows you to easily set up, secure, and manage data lakes. One of the features of Lake Formation is row-level security, which enables you to control access to specific rows or columns of data based on the identity or role of the user. This feature is useful for scenarios where you need to restrict access to sensitive or regulated data, such as customer data from different countries. By registering the S3 bucket as a data lake location in Lake Formation, you can use the Lake Formation console or APIs to define and apply row-level security policies to the data in the bucket. You can also use Lake Formation blueprints to automate the ingestion and transformation of data from various sources into the data lake. This solution requires the least operational effort compared to the other options, as it does not involve creating or moving data, or managing multiple tables, views, or roles. References:
✑ AWS Lake Formation
✑ Row-Level Security
✑ AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide, Chapter 4: Data Lakes and Data Warehouses, Section 4.2: AWS Lake Formation

NEW QUESTION 9
A company stores daily records of the financial performance of investment portfolios in .csv format in an Amazon S3 bucket. A data engineer uses AWS Glue crawlers to crawl the S3 data.
The data engineer must make the S3 data accessible daily in the AWS Glue Data Catalog. Which solution will meet these requirements?

• A. Create an IAM role that includes the AmazonS3FullAccess polic
• B. Associate the role with the crawle
• C. Specify the S3 bucket path of the source data as the crawler's data stor
• D. Create a daily schedule to run the crawle
• E. Configure the output destination to a new path in the existing S3 bucket.
• F. Create an IAM role that includes the AWSGlueServiceRole polic
• G. Associate the role with the crawle
• H. Specify the S3 bucket path of the source data as the crawler's data stor
• I. Create a daily schedule to run the crawle
• J. Specify a database name for the output.
• K. Create an IAM role that includes the AmazonS3FullAccess polic
• L. Associate the role with the crawle
• M. Specify the S3 bucket path of the source data as the crawler's data stor
• N. Allocate data processing units (DPUs) to run the crawler every da
• O. Specify a database name for the output.
• P. Create an IAM role that includes the AWSGlueServiceRole polic
• Q. Associate the role with the crawle
• R. Specify the S3 bucket path of the source data as the crawler's data stor
• S. Allocate data processing units (DPUs) to run the crawler every da
• T. Configure the output destination to a new path in the existing S3 bucket.

Answer: B

Explanation:
To make the S3 data accessible daily in the AWS Glue Data Catalog, the data engineer needs to create a crawler that can crawl the S3 data and write the metadata to the Data Catalog. The crawler also needs to run on a daily schedule to keep the Data Catalog updated with the latest data. Therefore, the solution must include the following steps:
✑ Create an IAM role that has the necessary permissions to access the S3 data and
the Data Catalog. The AWSGlueServiceRole policy is a managed policy that grants these permissions1.
✑ Associate the role with the crawler.
✑ Specify the S3 bucket path of the source data as the crawler’s data store. The crawler will scan the data and infer the schema and format2.
✑ Create a daily schedule to run the crawler. The crawler will run at the specified time every day and update the Data Catalog with any changes in the data3.
✑ Specify a database name for the output. The crawler will create or update a table in the Data Catalog under the specified database. The table will contain the metadata about the data in the S3 bucket, such as the location, schema, and classification.
Option B is the only solution that includes all these steps. Therefore, option B is the correct answer.
Option A is incorrect because it configures the output destination to a new path in the existing S3 bucket. This is unnecessary and may cause confusion, as the crawler does not write any data to the S3 bucket, only metadata to the Data Catalog.
Option C is incorrect because it allocates data processing units (DPUs) to run the crawler every day. This is also unnecessary, as DPUs are only used for AWS Glue ETL jobs, not crawlers.
Option D is incorrect because it combines the errors of option A and C. It configures the output destination to a new path in the existing S3 bucket and allocates DPUs to run the crawler every day, both of which are irrelevant for the crawler.
References:
✑ 1: AWS managed (predefined) policies for AWS Glue - AWS Glue
✑ 2: Data Catalog and crawlers in AWS Glue - AWS Glue
✑ 3: Scheduling an AWS Glue crawler - AWS Glue
✑ [4]: Parameters set on Data Catalog tables by crawler - AWS Glue
✑ [5]: AWS Glue pricing - Amazon Web Services (AWS)

NEW QUESTION 10
A data engineer must use AWS services to ingest a dataset into an Amazon S3 data lake. The data engineer profiles the dataset and discovers that the dataset contains personally identifiable information (PII). The data engineer must implement a solution to profile the dataset and obfuscate the PII.
Which solution will meet this requirement with the LEAST operational effort?

• A. Use an Amazon Kinesis Data Firehose delivery stream to process the datase
• B. Create an AWS Lambda transform function to identify the PI
• C. Use an AWS SDK to obfuscate the PI
• D. Set the S3 data lake as the target for the delivery stream.
• E. Use the Detect PII transform in AWS Glue Studio to identify the PI
• F. Obfuscate the PI
• G. Use an AWS Step Functions state machine to orchestrate a data pipeline to ingest the data into the S3 data lake.
• H. Use the Detect PII transform in AWS Glue Studio to identify the PI
• I. Create a rule in AWS Glue Data Quality to obfuscate the PI
• J. Use an AWS Step Functions state machine to orchestrate a data pipeline to ingest the data into the S3 data lake.
• K. Ingest the dataset into Amazon DynamoD
• L. Create an AWS Lambda function to identify and obfuscate the PII in the DynamoDB table and to transform the dat
• M. Use the same Lambda function to ingest the data into the S3 data lake.

Answer: C

Explanation:
AWS Glue is a fully managed service that provides a serverless data integration platform for data preparation, data cataloging, and data loading. AWS Glue Studio is a graphical interface that allows you to easily author, run, and monitor AWS Glue ETL jobs. AWS Glue Data Quality is a feature that enables you to validate, cleanse, and enrich your data using predefined or custom rules. AWS Step Functions is a service that allows you to coordinate multiple AWS services into serverless workflows.
Using the Detect PII transform in AWS Glue Studio, you can automatically identify and label the PII in your dataset, such as names, addresses, phone numbers, email addresses, etc. You can then create a rule in AWS Glue Data Quality to obfuscate the PII, such as masking, hashing, or replacing the values with dummy data. You can also use other rules to validate and cleanse your data, such as checking for null values, duplicates, outliers, etc. You can then use an AWS Step Functions state machine to orchestrate a data pipeline to ingest the data into the S3 data lake. You can use AWS Glue DataBrew to visually explore and transform the data, AWS Glue crawlers to discover and catalog the data, and AWS Glue jobs to load the data into the S3 data lake.
This solution will meet the requirement with the least operational effort, as it leverages the serverless and managed capabilities of AWS Glue, AWS Glue Studio, AWS Glue Data Quality, and AWS Step Functions. You do not need to write any code to identify or obfuscate the PII, as you can use the built-in transforms and rules in AWS Glue Studio and AWS Glue Data Quality. You also do not need to provision or manage any servers or clusters, as AWS Glue and AWS Step Functions scale automatically based on the demand. The other options are not as efficient as using the Detect PII transform in AWS Glue Studio, creating a rule in AWS Glue Data Quality, and using an AWS Step Functions state machine. Using an Amazon Kinesis Data Firehose delivery stream to process the dataset, creating an AWS Lambda transform function to identify the PII, using an AWS SDK to obfuscate the PII, and setting the S3 data lake as the target for the delivery stream will require more operational effort, as you will need to write and maintain code to identifyand obfuscate the PII, as well as manage the Lambda function and its resources. Using the Detect PII transform in AWS Glue Studio to identify the PII, obfuscating the PII, and using an AWS Step Functions state machine to orchestrate a data pipeline to ingest the data into the S3 data lake will not be as effective as creating a rule in AWS Glue Data Quality to obfuscate the PII, as you will need to manually obfuscate the PII after identifying it, which can be error-prone and time-consuming. Ingesting the dataset into Amazon DynamoDB, creating an AWS Lambda function to identify and obfuscate the PII in the DynamoDB table and to transform the data, and using the same Lambda function to ingest the data into the S3 data lake will require more operational effort, as you will need to write and maintain code to identify and obfuscate the PII, as well as manage the Lambda function and its resources. You will also incur additional costs and complexity by using DynamoDB as an intermediate data store, which may not be necessary for your use case. References:
✑ AWS Glue
✑ AWS Glue Studio
✑ AWS Glue Data Quality
✑ [AWS Step Functions]
✑ [AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide], Chapter 6: Data Integration and Transformation, Section 6.1: AWS Glue

NEW QUESTION 11
A company is migrating a legacy application to an Amazon S3 based data lake. A data engineer reviewed data that is associated with the legacy application. The data engineer found that the legacy data contained some duplicate information.
The data engineer must identify and remove duplicate information from the legacy application data.
Which solution will meet these requirements with the LEAST operational overhead?

• A. Write a custom extract, transform, and load (ETL) job in Pytho
• B. Use the DataFramedrop duplicatesf) function by importingthe Pandas library to perform data deduplication.
• C. Write an AWS Glue extract, transform, and load (ETL) jo
• D. Usethe FindMatches machine learning(ML) transform to transform the data to perform data deduplication.
• E. Write a custom extract, transform, and load (ETL) job in Pytho
• F. Import the Python dedupe librar
• G. Use the dedupe library to perform data deduplication.
• H. Write an AWS Glue extract, transform, and load (ETL) jo
• I. Import the Python dedupe librar
• J. Use the dedupe library to perform data deduplication.

Answer: B

Explanation:
AWS Glue is a fully managed serverless ETL service that can handle data deduplication with minimal operational overhead. AWS Glue provides a built-in ML transform called FindMatches, which can automatically identify and group similar records in a dataset. FindMatches can also generate a primary key for each group of records and remove duplicates. FindMatches does not require any coding or prior ML experience, as it can learn from a sample of labeled data provided by the user. FindMatches can also scale to handle large datasets and optimize the cost and performance of the ETL job. References:
✑ AWS Glue
✑ FindMatches ML Transform
✑ AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide

NEW QUESTION 12
A company uses Amazon Redshift for its data warehouse. The company must automate refresh schedules for Amazon Redshift materialized views.
Which solution will meet this requirement with the LEAST effort?

• A. Use Apache Airflow to refresh the materialized views.
• B. Use an AWS Lambda user-defined function (UDF) within Amazon Redshift to refresh the materialized views.
• C. Use the query editor v2 in Amazon Redshift to refresh the materialized views.
• D. Use an AWS Glue workflow to refresh the materialized views.

Answer: C

Explanation:
The query editor v2 in Amazon Redshift is a web-based tool that allows users to run SQL queries and scripts on Amazon Redshift clusters. The query editor v2 supports creating and managing materialized views, which are precomputed results of a query that can improve the performance of subsequent queries. The query editor v2 also supports scheduling queries to run at specified intervals, which can be used to refresh materialized views automatically. This solution requires the least effort, as it does not involve any additional services, coding, or configuration. The other solutions are more complex and require more operational overhead. Apache Airflow is an open-source platform for orchestrating workflows, which can be used to refresh materialized views, but it requires setting up and managing an Airflow environment, creating DAGs (directed acyclic graphs) to define the workflows, and integrating with Amazon Redshift. AWS Lambda is a serverless compute service that can run code in response to events, which can be used to refresh materialized views, but it requires creating and deploying Lambda functions, defining UDFs within Amazon Redshift, and triggering the functions using events or schedules. AWS Glue is a fully managed ETL service that can run jobs to transform and load data, which can be used to refresh materialized views, but it requires creating and configuring Glue jobs, defining Glue workflows to orchestrate the jobs, and scheduling the workflows using triggers. References:
✑ Query editor V2
✑ Working with materialized views
✑ Scheduling queries
✑ [AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide]

NEW QUESTION 13
A media company uses software as a service (SaaS) applications to gather data by using third-party tools. The company needs to store the data in an Amazon S3 bucket. The company will use Amazon Redshift to perform analytics based on the data.
Which AWS service or feature will meet these requirements with the LEAST operational overhead?

• A. Amazon Managed Streaming for Apache Kafka (Amazon MSK)
• B. Amazon AppFlow
• C. AWS Glue Data Catalog
• D. Amazon Kinesis

Answer: B

Explanation:
Amazon AppFlow is a fully managed integration service that enables you to securely transfer data between SaaS applications and AWS services like Amazon S3 and AmazonRedshift. Amazon AppFlow supports many SaaS applications as data sources and targets, and allows you to configure data flows with a few clicks. Amazon AppFlow also provides features such as data transformation, filtering, validation, and encryption to prepare and protect your data. Amazon AppFlow meets the requirements of the media company with the least operational overhead, as it eliminates the need to write code, manage infrastructure, or monitor data pipelines. References:
✑ Amazon AppFlow
✑ Amazon AppFlow | SaaS Integrations List
✑ Get started with data integration from Amazon S3 to Amazon Redshift using AWS Glue interactive sessions

NEW QUESTION 14
A company uses an Amazon Redshift cluster that runs on RA3 nodes. The company wants to scale read and write capacity to meet demand. A data engineer needs to identify a solution that will turn on concurrency scaling.
Which solution will meet this requirement?

• A. Turn on concurrency scaling in workload management (WLM) for Redshift Serverless workgroups.
• B. Turn on concurrency scaling at the workload management (WLM) queue level in the Redshift cluster.
• C. Turn on concurrency scaling in the settings duringthe creation of andnew Redshift cluster.
• D. Turn on concurrency scaling for the daily usage quota for the Redshift cluster.

Answer: B

Explanation:
Concurrency scaling is a feature that allows you to support thousands of concurrent users and queries, with consistently fast query performance. When you turn on concurrency scaling, Amazon Redshift automatically adds query processing power in seconds to process queries without any delays. You can manage which queries are sent to the concurrency-scaling cluster by configuring WLM queues. To turn on concurrency scaling for a queue, set the Concurrency Scaling mode value to auto. The other options are either incorrect or irrelevant, as they do not enable concurrency scaling for the existing Redshift cluster on RA3 nodes. References:
✑ Working with concurrency scaling - Amazon Redshift
✑ Amazon Redshift Concurrency Scaling - Amazon Web Services
✑ Configuring concurrency scaling queues - Amazon Redshift
✑ AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide (Chapter 6, page 163)

NEW QUESTION 15
A company currently stores all of its data in Amazon S3 by using the S3 Standard storage class.
A data engineer examined data access patterns to identify trends. During the first 6 months, most data files are accessed several times each day. Between 6 months and 2 years, most data files are accessed once or twice each month. After 2 years, data files are accessed only once or twice each year.
The data engineer needs to use an S3 Lifecycle policy to develop new data storage rules. The new storage solution must continue to provide high availability.
Which solution will meet these requirements in the MOST cost-effective way?

• A. Transition objects to S3 One Zone-Infrequent Access (S3 One Zone-IA) after 6 month
• B. Transfer objects to S3 Glacier Flexible Retrieval after 2 years.
• C. Transition objects to S3 Standard-Infrequent Access (S3 Standard-IA) after 6 month
• D. Transfer objects to S3 Glacier Flexible Retrieval after 2 years.
• E. Transition objects to S3 Standard-Infrequent Access (S3 Standard-IA) after 6 month
• F. Transfer objects to S3 Glacier Deep Archive after 2 years.
• G. Transition objects to S3 One Zone-Infrequent Access (S3 One Zone-IA) after 6 month
• H. Transfer objects to S3 Glacier Deep Archive after 2 years.

Answer: C

Explanation:
To achieve the most cost-effective storage solution, the data engineer needs to use an S3 Lifecycle policy that transitions objects to lower-cost storage classes based on their access patterns, and deletes them when they are no longer needed. The storage classes should also provide high availability, which means they should be resilient to the loss of data in a single Availability Zone1. Therefore, the solution must include the following steps:
✑ Transition objects to S3 Standard-Infrequent Access (S3 Standard-IA) after 6 months. S3 Standard-IA is designed for data that is accessed less frequently, but requires rapid access when needed. It offers the same high durability, throughput, and low latency as S3 Standard, but with a lower storage cost and a retrieval fee2.
Therefore, it is suitablefor data files that are accessed once or twice each month. S3 Standard-IA also provides high availability, as it stores data redundantly across multiple Availability Zones1.
✑ Transfer objects to S3 Glacier Deep Archive after 2 years. S3 Glacier Deep Archive is the lowest-cost storage class that offers secure and durable storage for data that is rarely accessed and can tolerate a 12-hour retrieval time. It is ideal for long-term archiving and digital preservation3. Therefore, it is suitable for data files that are accessed only once or twice each year. S3 Glacier Deep Archive also provides high availability, as it stores data across at least three geographically dispersed Availability Zones1.
✑ Delete objects when they are no longer needed. The data engineer can specify an expiration action in the S3 Lifecycle policy to delete objects after a certain period of time. This will reduce the storage cost and comply with any data retention policies.
Option C is the only solution that includes all these steps. Therefore, option C is the correct answer.
Option A is incorrect because it transitions objects to S3 One Zone-Infrequent Access (S3 One Zone-IA) after 6 months. S3 One Zone-IA is similar to S3 Standard-IA, but it stores data in a single Availability Zone. This means it has a lower availability and durability than S3 Standard-IA, and it is not resilient to the loss of data in a single Availability Zone1. Therefore, it does not provide high availability as required.
Option B is incorrect because it transfers objects to S3 Glacier Flexible Retrieval after 2 years. S3 Glacier Flexible Retrieval is a storage class that offers secure and durable storage for data that is accessed infrequently and can tolerate a retrieval time of minutes to hours. It is more expensive than S3 Glacier Deep Archive, and it is not suitable for data that is accessed only once or twice each year3. Therefore, it is not the most cost-effective option.
Option D is incorrect because it combines the errors of option A and B. It transitions objects to S3 One Zone-IA after 6 months, which does not provide high availability, and it transfers objects to S3 Glacier Flexible Retrieval after 2 years, which is not the most cost-effective option.
References:
✑ 1: Amazon S3 storage classes - Amazon Simple Storage Service
✑ 2: Amazon S3 Standard-Infrequent Access (S3 Standard-IA) - Amazon Simple Storage Service
✑ 3: Amazon S3 Glacier and S3 Glacier Deep Archive - Amazon Simple Storage Service
✑ [4]: Expiring objects - Amazon Simple Storage Service
✑ [5]: Managing your storage lifecycle - Amazon Simple Storage Service
✑ [6]: Examples of S3 Lifecycle configuration - Amazon Simple Storage Service
✑ [7]: Amazon S3 Lifecycle further optimizes storage cost savings with new features
- What’s New with AWS

NEW QUESTION 16
A company receives .csv files that contain physical address data. The data is in columns that have the following names: Door_No, Street_Name, City, and Zip_Code. The company wants to create a single column to store these values in the following format:

Which solution will meet this requirement with the LEAST coding effort?

• A. Use AWS Glue DataBrew to read the file
• B. Use the NEST TO ARRAY transformation to create the new column.
• C. Use AWS Glue DataBrew to read the file
• D. Use the NEST TO MAP transformation to create the new column.
• E. Use AWS Glue DataBrew to read the file
• F. Use the PIVOT transformation to create the new column.
• G. Write a Lambda function in Python to read the file
• H. Use the Python data dictionary type to create the new column.

Answer: B

Explanation:
The NEST TO MAP transformation allows you to combine multiple columns into a single column that contains a JSON object with key-value pairs. This is the easiest way to achieve the desired format for the physical address data, as you can simply select the columns to nest and specify the keys for each column. The NEST TO ARRAY transformation creates a single column that contains an array of values, which is not thesame as the JSON object format. The PIVOT transformation reshapes the data by creating new columns from unique values in a selected column, which is not applicable for this use case. Writing a Lambda function in Python requires more coding effort than using AWS Glue DataBrew, which provides a visual and interactive interface for data transformations. References:
✑ 7 most common data preparation transformations in AWS Glue DataBrew (Section: Nesting and unnesting columns)
✑ NEST TO MAP - AWS Glue DataBrew (Section: Syntax)

NEW QUESTION 17
A data engineer needs to maintain a central metadata repository that users access through Amazon EMR and Amazon Athena queries. The repository needs to provide the schema and properties of many tables. Some of the metadata is stored in Apache Hive. The data engineer needs to import the metadata from Hive into the central metadata repository.
Which solution will meet these requirements with the LEAST development effort?

• A. Use Amazon EMR and Apache Ranger.
• B. Use a Hive metastore on an EMR cluster.
• C. Use the AWS Glue Data Catalog.
• D. Use a metastore on an Amazon RDS for MySQL DB instance.

Answer: C

Explanation:
The AWS Glue Data Catalog is an Apache Hive metastore-compatible catalog that provides a central metadata repository for various data sources and formats. You can use the AWS Glue Data Catalog as an external Hive metastore for Amazon EMR and Amazon Athena queries, and import metadata from existing Hive metastores into the Data Catalog. This solution requires the least development effort, as you can use AWS Glue crawlers to automatically discover and catalog the metadata from Hive, and use the AWS Glue console, AWS CLI, or Amazon EMR API to configure the Data Catalog as the Hive metastore. The other options are either more complex or require additional steps, such as setting up Apache Ranger for security, managing a Hive metastore on an EMR cluster or an RDS instance, or migrating the metadata manually. References:
✑ Using the AWS Glue Data Catalog as the metastore for Hive (Section: Specifying
AWS Glue Data Catalog as the metastore)
✑ Metadata Management: Hive Metastore vs AWS Glue (Section: AWS Glue Data Catalog)
✑ AWS Glue Data Catalog support for Spark SQL jobs (Section: Importing metadata from an existing Hive metastore)
✑ AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide (Chapter 5, page 131)

NEW QUESTION 18
A company receives a daily file that contains customer data in .xls format. The company stores the file in Amazon S3. The daily file is approximately 2 GB in size.
A data engineer concatenates the column in the file that contains customer first names and the column that contains customer last names. The data engineer needs to determine the number of distinct customers in the file.
Which solution will meet this requirement with the LEAST operational effort?

• A. Create and run an Apache Spark job in an AWS Glue noteboo
• B. Configure the job to read the S3 file and calculate the number of distinct customers.
• C. Create an AWS Glue crawler to create an AWS Glue Data Catalog of the S3 fil
• D. Run SQL queries from Amazon Athena to calculate the number of distinct customers.
• E. Create and run an Apache Spark job in Amazon EMR Serverless to calculate the number of distinct customers.
• F. Use AWS Glue DataBrew to create a recipe that uses the COUNT_DISTINCT aggregate function to calculate the number of distinct customers.

Answer: D

Explanation:
AWS Glue DataBrew is a visual data preparation tool that allows you to clean, normalize, and transform data without writing code. You can use DataBrew to create recipes that define the steps to apply to your data, such as filtering, renaming, splitting, or aggregating columns. You can also use DataBrew to run jobs that execute the recipes on your data sources, such as Amazon S3, Amazon Redshift, or Amazon Aurora. DataBrew integrates with AWS Glue Data Catalog, which is a centralized metadata repository for your data assets1.
The solution that meets the requirement with the least operational effort is to use AWS Glue DataBrew to create a recipe that uses the COUNT_DISTINCT aggregate function to calculate the number of distinct customers. This solution has the following advantages:
✑ It does not require you to write any code, as DataBrew provides a graphical user
interface that lets you explore, transform, and visualize your data. You can use DataBrewto concatenate the columns that contain customer first names and last names, and then use the COUNT_DISTINCT aggregate function to count the number of unique values in the resulting column2.
✑ It does not require you to provision, manage, or scale any servers, clusters, or notebooks, as DataBrew is a fully managed service that handles all the infrastructure for you. DataBrew can automatically scale up or down the compute resources based on the size and complexity of your data and recipes1.
✑ It does not require you to create or update any AWS Glue Data Catalog entries, as
DataBrew can automatically create and register the data sources and targets in the Data Catalog. DataBrew can also use the existing Data Catalog entries to access the data in S3 or other sources3.
Option A is incorrect because it suggests creating and running an Apache Spark job in an AWS Glue notebook. This solution has the following disadvantages:
✑ It requires you to write code, as AWS Glue notebooks are interactive development environments that allow you to write, test, and debug Apache Spark code using Python or Scala. You need to use the Spark SQL or the Spark DataFrame API to read the S3 file and calculate the number of distinct customers.
✑ It requires you to provision and manage a development endpoint, which is a serverless Apache Spark environment that you can connect to your notebook. You need to specify the type and number of workers for your development endpoint, and monitor its status and metrics.
✑ It requires you to create or update the AWS Glue Data Catalog entries for the S3 file, either manually or using a crawler. You need to use the Data Catalog as a metadata store for your Spark job, and specify the database and table names in your code.
Option B is incorrect because it suggests creating an AWS Glue crawler to create an AWS Glue Data Catalog of the S3 file, and running SQL queries from Amazon Athena to calculate the number of distinct customers. This solution has the following disadvantages:
✑ It requires you to create and run a crawler, which is a program that connects to your data store, progresses through a prioritized list of classifiers to determine the schema for your data, and then creates metadata tables in the Data Catalog. You need to specify the data store, the IAM role, the schedule, and the output database for your crawler.
✑ It requires you to write SQL queries, as Amazon Athena is a serverless interactive query service that allows you to analyze data in S3 using standard SQL. You need to use Athena to concatenate the columns that contain customer first names and last names, and then use the COUNT(DISTINCT) aggregate function to count the number of unique values in the resulting column.
Option C is incorrect because it suggests creating and running an Apache Spark job in Amazon EMR Serverless to calculate the number of distinct customers. This solution has the following disadvantages:
✑ It requires you to write code, as Amazon EMR Serverless is a service that allows you to run Apache Spark jobs on AWS without provisioning or managing any infrastructure. You need to use the Spark SQL or the Spark DataFrame API to read the S3 file and calculate the number of distinct customers.
✑ It requires you to create and manage an Amazon EMR Serverless cluster, which is a fully managed and scalable Spark environment that runs on AWS Fargate. You need to specify the cluster name, the IAM role, the VPC, and the subnet for your cluster, and monitor its status and metrics.
✑ It requires you to create or update the AWS Glue Data Catalog entries for the S3 file, either manually or using a crawler. You need to use the Data Catalog as a metadata store for your Spark job, and specify the database and table names in your code.
References:
✑ 1: AWS Glue DataBrew - Features
✑ 2: Working with recipes - AWS Glue DataBrew
✑ 3: Working with data sources and data targets - AWS Glue DataBrew
✑ [4]: AWS Glue notebooks - AWS Glue
✑ [5]: Development endpoints - AWS Glue
✑ [6]: Populating the AWS Glue Data Catalog - AWS Glue
✑ [7]: Crawlers - AWS Glue
✑ [8]: Amazon Athena - Features
✑ [9]: Amazon EMR Serverless - Features
✑ [10]: Creating an Amazon EMR Serverless cluster - Amazon EMR
✑ [11]: Using the AWS Glue Data Catalog with Amazon EMR Serverless - Amazon EMR

NEW QUESTION 19
A company stores data in a data lake that is in Amazon S3. Some data that the company stores in the data lake contains personally identifiable information (PII). Multiple user groups need to access the raw data. The company must ensure that user groups can access only the PII that they require.
Which solution will meet these requirements with the LEAST effort?

• A. Use Amazon Athena to query the dat
• B. Set up AWS Lake Formation and create data filters to establish levels of access for the company's IAM role
• C. Assign each user to the IAM role that matches the user's PII access requirements.
• D. Use Amazon QuickSight to access the dat
• E. Use column-level security features in QuickSight to limit the PII that users can retrieve from Amazon S3 by using Amazon Athen
• F. Define QuickSight access levels based on the PII access requirements of the users.
• G. Build a custom query builder UI that will run Athena queries in the background to access the dat
• H. Create user groups in Amazon Cognit
• I. Assign access levels to the user groups based on the PII access requirements of the users.
• J. Create IAM roles that have different levels of granular acces
• K. Assign the IAM roles to IAM user group
• L. Use an identity-based policy to assign access levels to user groups at the column level.

Answer: A

Explanation:
Amazon Athena is a serverless, interactive query service that enables you to analyze data in Amazon S3 using standard SQL. AWS Lake Formation is a service that helps you build, secure, and manage data lakes on AWS. You can use AWS Lake Formation to create data filters that define the level of access for different IAM roles based on the columns, rows, or tags of the data. By using Amazon Athena to query the data and AWS Lake Formation to create data filters, the company can meet the requirements of ensuring that user groups can access only the PII that they require with the least effort. The solution is to use Amazon Athena to query the data in the data lake that is in Amazon S3. Then, set up AWS Lake Formation and create data filters to establish levels of access for the company’s IAM roles. For example, a data filter can allow a user group to access only the columns that contain the PII that they need, such as name and email address, and deny access to the columns that contain the PII that they do not need, such as phone number and social security number. Finally, assign each user to the IAM role that matches the user’s PII access requirements. This way, the user groups can access the data in the data lake securely and efficiently. The other options are either not feasible or not optimal. Using Amazon QuickSight to access the data (option B) would require the company to pay for the QuickSight service and to configure the column-level security features for each user. Building a custom query builder UI that will run Athena queries in the background to access the data (option C) would require the company to develop and maintain the UI and to integrate it with Amazon Cognito. Creating IAM roles that have different levels of granular access (option D) would require the company to manage multiple IAM roles and policies and to ensure that they are aligned with the data schema. References:
✑ Amazon Athena
✑ AWS Lake Formation
✑ AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide, Chapter 4: Data Analysis and Visualization, Section 4.3: Amazon Athena

NEW QUESTION 20
A company uses Amazon RDS for MySQL as the database for a critical application. The database workload is mostly writes, with a small number of reads.
A data engineer notices that the CPU utilization of the DB instance is very high. The high CPU utilization is slowing down the application. The data engineer must reduce the CPU utilization of the DB Instance.
Which actions should the data engineer take to meet this requirement? (Choose two.)

• A. Use the Performance Insights feature of Amazon RDS to identify queries that have high CPU utilizatio
• B. Optimize the problematic queries.
• C. Modify the database schema to include additional tables and indexes.
• D. Reboot the RDS DB instance once each week.
• E. Upgrade to a larger instance size.
• F. Implement caching to reduce the database query load.

Answer: AE

Explanation:
Amazon RDS is a fully managed service that provides relational databases in the cloud. Amazon RDS for MySQL is one of the supported database engines that you can use to run your applications. Amazon RDS provides various features and tools to monitor and optimize the performance of your DB instances, such as Performance Insights, Enhanced Monitoring, CloudWatch metrics and alarms, etc.
Using the Performance Insights feature of Amazon RDS to identify queries that have high CPU utilization and optimizing the problematic queries will help reduce the CPU utilization of the DB instance. Performance Insights is a feature that allows you to analyze the load on your DB instance and determine what is causing performance issues. Performance Insights collects, analyzes, and displays database performance data using an interactive dashboard. You can use Performance Insights to identify the top SQL statements, hosts, users, or processes that are consuming the most CPU resources. You can also drill down into the details of each query and see the execution plan, wait events, locks, etc. By using Performance Insights, you can pinpoint the root cause of the high CPU utilization and optimize the queries accordingly. For example, you can rewrite the queries to make them more efficient, add or remove indexes, use prepared statements, etc.
Implementing caching to reduce the database query load will also help reduce the CPU utilization of the DB instance. Caching is a technique that allows you to store frequently accessed data in a fast and scalable storage layer, such as Amazon ElastiCache. By using caching, you can reduce the number of requests that hit your database, which in turn reduces the CPU load on your DB instance. Caching also improves the performance and availability of your application, as it reduces the latency and increases the throughput of your data access. You can use caching for various scenarios, such as storing session data, user preferences, application configuration, etc. You can also use caching for read-heavy workloads, such as displaying product details, recommendations, reviews, etc.
The other options are not as effective as using Performance Insights and caching. Modifying the database schema to include additional tables and indexes may or may not improve the CPU utilization, depending on the nature of the workload and the queries. Adding more tables and indexes may increase the complexity and overhead of the database, which may negatively affect the performance. Rebooting the RDS DB instance once each week will not reduce the CPU utilization, as it will not address the underlying cause of the high CPU load. Rebooting may also cause downtime and disruption to your application. Upgrading to a larger instance size may reduce the CPUutilization, but it will also increase the cost and complexity of your solution. Upgrading may also not be necessary if you can optimize the queries and reduce the database load by using caching. References:
✑ Amazon RDS
✑ Performance Insights
✑ Amazon ElastiCache
✑ [AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide], Chapter 3: Data Storage and Management, Section 3.1: Amazon RDS

NEW QUESTION 21
......