The Abreast Of The Times Guide To DAS-C01 Latest Exam

NEW QUESTION 1
An online retail company is migrating its reporting system to AWS. The company’s legacy system runs data processing on online transactions using a complex series of nested Apache Hive queries. Transactional data is exported from the online system to the reporting system several times a day. Schemas in the files are stable between updates.
A data analyst wants to quickly migrate the data processing to AWS, so any code changes should be minimized. To keep storage costs low, the data analyst decides to store the data in Amazon S3. It is vital that the data from the reports and associated analytics is completely up to date based on the data in Amazon S3.
Which solution meets these requirements?

• A. Create an AWS Glue Data Catalog to manage the Hive metadat
• B. Create an AWS Glue crawler over Amazon S3 that runs when data is refreshed to ensure that data changes are update
• C. Create an Amazon EMR cluster and use the metadata in the AWS Glue Data Catalog to run Hive processing queries in Amazon EMR.
• D. Create an AWS Glue Data Catalog to manage the Hive metadat
• E. Create an Amazon EMR cluster with consistent view enable
• F. Run emrfs sync before each analytics step to ensure data changes are update
• G. Create an EMR cluster and use the metadata in the AWS Glue Data Catalog to run Hive processing queries in Amazon EMR.
• H. Create an Amazon Athena table with CREATE TABLE AS SELECT (CTAS) to ensure data is refreshed from underlying queries against the raw datase
• I. Create an AWS Glue Data Catalog to manage the Hive metadata over the CTAS tabl
• J. Create an Amazon EMR cluster and use the metadata in the AWS Glue Data Catalog to run Hive processing queries in Amazon EMR.
• K. Use an S3 Select query to ensure that the data is properly update
• L. Create an AWS Glue Data Catalog to manage the Hive metadata over the S3 Select tabl
• M. Create an Amazon EMR cluster and use the metadata in the AWS Glue Data Catalog to run Hive processing queries in Amazon EMR.

Answer: A

NEW QUESTION 2
A company launched a service that produces millions of messages every day and uses Amazon Kinesis Data Streams as the streaming service.
The company uses the Kinesis SDK to write data to Kinesis Data Streams. A few months after launch, a data analyst found that write performance is significantly reduced. The data analyst investigated the metrics and determined that Kinesis is throttling the write requests. The data analyst wants to address this issue without significant changes to the architecture.
Which actions should the data analyst take to resolve this issue? (Choose two.)

• A. Increase the Kinesis Data Streams retention period to reduce throttling.
• B. Replace the Kinesis API-based data ingestion mechanism with Kinesis Agent.
• C. Increase the number of shards in the stream using the UpdateShardCount API.
• D. Choose partition keys in a way that results in a uniform record distribution across shards.
• E. Customize the application code to include retry logic to improve performance.

Answer: CD

Explanation:
https://aws.amazon.com/blogs/big-data/under-the-hood-scaling-your-kinesis-data-streams/

NEW QUESTION 3
An airline has .csv-formatted data stored in Amazon S3 with an AWS Glue Data Catalog. Data analysts want to join this data with call center data stored in Amazon Redshift as part of a dally batch process. The Amazon Redshift cluster is already under a heavy load. The solution must be managed, serverless, well-functioning, and minimize the load on the existing Amazon Redshift cluster. The solution should also require minimal effort and development activity.
Which solution meets these requirements?

• A. Unload the call center data from Amazon Redshift to Amazon S3 using an AWS Lambda function.Perform the join with AWS Glue ETL scripts.
• B. Export the call center data from Amazon Redshift using a Python shell in AWS Glu
• C. Perform the join with AWS Glue ETL scripts.
• D. Create an external table using Amazon Redshift Spectrum for the call center data and perform the join with Amazon Redshift.
• E. Export the call center data from Amazon Redshift to Amazon EMR using Apache Sqoo
• F. Perform the join with Apache Hive.

Answer: C

Explanation:
https://docs.aws.amazon.com/redshift/latest/dg/c-spectrum-external-tables.html

NEW QUESTION 4
A company has collected more than 100 TB of log files in the last 24 months. The files are stored as raw text in a dedicated Amazon S3 bucket. Each object has a key of the form year-month-day_log_HHmmss.txt where HHmmss represents the time the log file was initially created. A table was created in Amazon Athena that points to the S3 bucket. One-time queries are run against a subset of columns in the table several times an hour.
A data analyst must make changes to reduce the cost of running these queries. Management wants a solution with minimal maintenance overhead.
Which combination of steps should the data analyst take to meet these requirements? (Choose three.)

• A. Convert the log files to Apace Avro format.
• B. Add a key prefix of the form date=year-month-day/ to the S3 objects to partition the data.
• C. Convert the log files to Apache Parquet format.
• D. Add a key prefix of the form year-month-day/ to the S3 objects to partition the data.
• E. Drop and recreate the table with the PARTITIONED BY claus
• F. Run the ALTER TABLE ADD PARTITION statement.
• G. Drop and recreate the table with the PARTITIONED BY claus
• H. Run the MSCK REPAIR TABLE statement.

Answer: BCF

NEW QUESTION 5
A company has a data warehouse in Amazon Redshift that is approximately 500 TB in size. New data is imported every few hours and read-only queries are run throughout the day and evening. There is a particularly heavy load with no writes for several hours each morning on business days. During those hours, some queries are queued and take a long time to execute. The company needs to optimize query execution and avoid any downtime.
What is the MOST cost-effective solution?

• A. Enable concurrency scaling in the workload management (WLM) queue.
• B. Add more nodes using the AWS Management Console during peak hour
• C. Set the distribution style to ALL.
• D. Use elastic resize to quickly add nodes during peak time
• E. Remove the nodes when they are not needed.
• F. Use a snapshot, restore, and resize operatio
• G. Switch to the new target cluster.

Answer: A

Explanation:
https://docs.aws.amazon.com/redshift/latest/dg/cm-c-implementing-workload-management.html

NEW QUESTION 6
A data analytics specialist is setting up workload management in manual mode for an Amazon Redshift environment. The data analytics specialist is defining query monitoring rules to manage system performance and user experience of an Amazon Redshift cluster.
Which elements must each query monitoring rule include?

• A. A unique rule name, a query runtime condition, and an AWS Lambda function to resubmit any failed queries in off hours
• B. A queue name, a unique rule name, and a predicate-based stop condition
• C. A unique rule name, one to three predicates, and an action
• D. A workload name, a unique rule name, and a query runtime-based condition

Answer: C

NEW QUESTION 7
A large university has adopted a strategic goal of increasing diversity among enrolled students. The data analytics team is creating a dashboard with data visualizations to enable stakeholders to view historical trends. All access must be authenticated using Microsoft Active Directory. All data in transit and at rest must be encrypted.
Which solution meets these requirements?

• A. Amazon QuickSight Standard edition configured to perform identity federation using SAML 2.0. and the default encryption settings.
• B. Amazon QuickSight Enterprise edition configured to perform identity federation using SAML 2.0 and the default encryption settings.
• C. Amazon QuckSight Standard edition using AD Connector to authenticate using Active Directory.Configure Amazon QuickSight to use customer-provided keys imported into AWS KMS.
• D. Amazon QuickSight Enterprise edition using AD Connector to authenticate using Active Directory.Configure Amazon QuickSight to use customer-provided keys imported into AWS KMS.

Answer: D

NEW QUESTION 8
A data engineering team within a shared workspace company wants to build a centralized logging system for all weblogs generated by the space reservation system. The company has a fleet of Amazon EC2 instances that process requests for shared space reservations on its website. The data engineering team wants to ingest all weblogs into a service that will provide a near-real-time search engine. The team does not want to manage the maintenance and operation of the logging system.
Which solution allows the data engineering team to efficiently set up the web logging system within AWS?

• A. Set up the Amazon CloudWatch agent to stream weblogs to CloudWatch logs and subscribe the Amazon Kinesis data stream to CloudWatc
• B. Choose Amazon Elasticsearch Service as the end destination of the weblogs.
• C. Set up the Amazon CloudWatch agent to stream weblogs to CloudWatch logs and subscribe the Amazon Kinesis Data Firehose delivery stream to CloudWatc
• D. Choose Amazon Elasticsearch Service as the end destination of the weblogs.
• E. Set up the Amazon CloudWatch agent to stream weblogs to CloudWatch logs and subscribe the Amazon Kinesis data stream to CloudWatc
• F. Configure Splunk as the end destination of the weblogs.
• G. Set up the Amazon CloudWatch agent to stream weblogs to CloudWatch logs and subscribe the Amazon Kinesis Firehose delivery stream to CloudWatc
• H. Configure Amazon DynamoDB as the end destinationof the weblog

Answer: B

Explanation:
https://docs.aws.amazon.com/AmazonCloudWatch/latest/logs/CWL_ES_Stream.html

NEW QUESTION 9
Three teams of data analysts use Apache Hive on an Amazon EMR cluster with the EMR File System (EMRFS) to query data stored within each teams Amazon S3 bucket. The EMR cluster has Kerberos enabled and is configured to authenticate users from the corporate Active Directory. The data is highly sensitive, so access must be limited to the members of each team.
Which steps will satisfy the security requirements?

• A. For the EMR cluster Amazon EC2 instances, create a service role that grants no access to Amazon S3.Create three additional IAM roles, each granting access to each team’s specific bucke
• B. Add the additional IAM roles to the cluster’s EMR role for the EC2 trust polic
• C. Create a security configuration mapping for the additional IAM roles to Active Directory user groups for each team.
• D. For the EMR cluster Amazon EC2 instances, create a service role that grants no access to Amazon S3.Create three additional IAM roles, each granting access to each team's specific bucke
• E. Add the service role for the EMR cluster EC2 instances to the trust policies for the additional IAM role
• F. Create a security configuration mapping for the additional IAM roles to Active Directory user groups for each team.
• G. For the EMR cluster Amazon EC2 instances, create a service role that grants full access to Amazon S3.Create three additional IAM roles, each granting access to each team’s specific bucke
• H. Add the service role for the EMR cluster EC2 instances to the trust polices for the additional IAM role
• I. Create a security configuration mapping for the additional IAM roles to Active Directory user groups for each team.
• J. For the EMR cluster Amazon EC2 instances, create a service role that grants full access to Amazon S3.Create three additional IAM roles, each granting access to each team's specific bucke
• K. Add the service role for the EMR cluster EC2 instances to the trust polices for the base IAM role
• L. Create a security configuration mapping for the additional IAM roles to Active Directory user groups for each team.

Answer: C

NEW QUESTION 10
A data analyst is using Amazon QuickSight for data visualization across multiple datasets generated by applications. Each application stores files within a separate Amazon S3 bucket. AWS Glue Data Catalog is used as a central catalog across all application data in Amazon S3. A new application stores its data within a separate S3 bucket. After updating the catalog to include the new application data source, the data analyst created a new Amazon QuickSight data source from an Amazon Athena table, but the import into SPICE failed.
How should the data analyst resolve the issue?

• A. Edit the permissions for the AWS Glue Data Catalog from within the Amazon QuickSight console.
• B. Edit the permissions for the new S3 bucket from within the Amazon QuickSight console.
• C. Edit the permissions for the AWS Glue Data Catalog from within the AWS Glue console.
• D. Edit the permissions for the new S3 bucket from within the S3 console.

Answer: B

NEW QUESTION 11
A retail company wants to use Amazon QuickSight to generate dashboards for web and in-store sales. A group of 50 business intelligence professionals will develop and use the dashboards. Once ready, the dashboards will be shared with a group of 1,000 users.
The sales data comes from different stores and is uploaded to Amazon S3 every 24 hours. The data is partitioned by year and month, and is stored in Apache Parquet format. The company is using the AWS Glue Data Catalog as its main data catalog and Amazon Athena for querying. The total size of the uncompressed data that the dashboards query from at any point is 200 GB.
Which configuration will provide the MOST cost-effective solution that meets these requirements?

• A. Load the data into an Amazon Redshift cluster by using the COPY comman
• B. Configure 50 author users and 1,000 reader user
• C. Use QuickSight Enterprise editio
• D. Configure an Amazon Redshift data source with a direct query option.
• E. Use QuickSight Standard editio
• F. Configure 50 author users and 1,000 reader user
• G. Configure an Athena data source with a direct query option.
• H. Use QuickSight Enterprise editio
• I. Configure 50 author users and 1,000 reader user
• J. Configure an Athena data source and import the data into SPIC
• K. Automatically refresh every 24 hours.
• L. Use QuickSight Enterprise editio
• M. Configure 1 administrator and 1,000 reader user
• N. Configure an S3 data source and import the data into SPIC
• O. Automatically refresh every 24 hours.

Answer: C

NEW QUESTION 12
A manufacturing company has been collecting IoT sensor data from devices on its factory floor for a year and is storing the data in Amazon Redshift for daily analysis. A data analyst has determined that, at an expected ingestion rate of about 2 TB per day, the cluster will be undersized in less than 4 months. A long-term solution is needed. The data analyst has indicated that most queries only reference the most recent 13 months of data, yet there are also quarterly reports that need to query all the data generated from the past 7 years. The chief technology officer (CTO) is concerned about the costs, administrative effort, and performance of a long-term solution.
Which solution should the data analyst use to meet these requirements?

• A. Create a daily job in AWS Glue to UNLOAD records older than 13 months to Amazon S3 and delete those records from Amazon Redshif
• B. Create an external table in Amazon Redshift to point to the S3 locatio
• C. Use Amazon Redshift Spectrum to join to data that is older than 13 months.
• D. Take a snapshot of the Amazon Redshift cluste
• E. Restore the cluster to a new cluster using dense storage nodes with additional storage capacity.
• F. Execute a CREATE TABLE AS SELECT (CTAS) statement to move records that are older than 13 months to quarterly partitioned data in Amazon Redshift Spectrum backed by Amazon S3.
• G. Unload all the tables in Amazon Redshift to an Amazon S3 bucket using S3 Intelligent-Tierin
• H. Use AWS Glue to crawl the S3 bucket location to create external tables in an AWS Glue Data Catalo
• I. Create an Amazon EMR cluster using Auto Scaling for any daily analytics needs, and use Amazon Athena for the quarterly reports, with both using the same AWS Glue Data Catalog.

Answer: A

NEW QUESTION 13
A transportation company uses IoT sensors attached to trucks to collect vehicle data for its global delivery fleet. The company currently sends the sensor data in small .csv files to Amazon S3. The files are then loaded into a 10-node Amazon Redshift cluster with two slices per node and queried using both Amazon Athena and Amazon Redshift. The company wants to optimize the files to reduce the cost of querying and also improve the speed of data loading into the Amazon Redshift cluster.
Which solution meets these requirements?

• A. Use AWS Glue to convert all the files from .csv to a single large Apache Parquet fil
• B. COPY the file into Amazon Redshift and query the file with Athena from Amazon S3.
• C. Use Amazon EMR to convert each .csv file to Apache Avr
• D. COPY the files into Amazon Redshift and query the file with Athena from Amazon S3.
• E. Use AWS Glue to convert the files from .csv to a single large Apache ORC fil
• F. COPY the file into Amazon Redshift and query the file with Athena from Amazon S3.
• G. Use AWS Glue to convert the files from .csv to Apache Parquet to create 20 Parquet file
• H. COPY the files into Amazon Redshift and query the files with Athena from Amazon S3.

Answer: D

NEW QUESTION 14
A banking company is currently using an Amazon Redshift cluster with dense storage (DS) nodes to store sensitive data. An audit found that the cluster is unencrypted. Compliance requirements state that a database with sensitive data must be encrypted through a hardware security module (HSM) with automated key rotation.
Which combination of steps is required to achieve compliance? (Choose two.)

• A. Set up a trusted connection with HSM using a client and server certificate with automatic key rotation.
• B. Modify the cluster with an HSM encryption option and automatic key rotation.
• C. Create a new HSM-encrypted Amazon Redshift cluster and migrate the data to the new cluster.
• D. Enable HSM with key rotation through the AWS CLI.
• E. Enable Elliptic Curve Diffie-Hellman Ephemeral (ECDHE) encryption in the HSM.

Answer: BD

NEW QUESTION 15
An ecommerce company stores customer purchase data in Amazon RDS. The company wants a solution to store and analyze historical data. The most recent 6 months of data will be queried frequently for analytics workloads. This data is several terabytes large. Once a month, historical data for the last 5 years must be accessible and will be joined with the more recent data. The company wants to optimize performance and cost.
Which storage solution will meet these requirements?

• A. Create a read replica of the RDS database to store the most recent 6 months of dat
• B. Copy the historical data into Amazon S3. Create an AWS Glue Data Catalog of the data in Amazon S3 and Amazon RD
• C. Run historical queries using Amazon Athena.
• D. Use an ETL tool to incrementally load the most recent 6 months of data into an Amazon Redshift cluste
• E. Run more frequent queries against this cluste
• F. Create a read replica of the RDS database to run queries on the historical data.
• G. Incrementally copy data from Amazon RDS to Amazon S3. Create an AWS Glue Data Catalog of the data in Amazon S3. Use Amazon Athena to query the data.
• H. Incrementally copy data from Amazon RDS to Amazon S3. Load and store the most recent 6 months of data in Amazon Redshif
• I. Configure an Amazon Redshift Spectrum table to connect to all historical data.

Answer: D

NEW QUESTION 16
A retail company leverages Amazon Athena for ad-hoc queries against an AWS Glue Data Catalog. The data analytics team manages the data catalog and data access for the company. The data analytics team wants to separate queries and manage the cost of running those queries by different workloads and teams. Ideally, the data analysts want to group the queries run by different users within a team, store the query results in individual Amazon S3 buckets specific to each team, and enforce cost constraints on the queries run against the Data Catalog.
Which solution meets these requirements?

• A. Create IAM groups and resource tags for each team within the compan
• B. Set up IAM policies that controluser access and actions on the Data Catalog resources.
• C. Create Athena resource groups for each team within the company and assign users to these group
• D. Add S3 bucket names and other query configurations to the properties list for the resource groups.
• E. Create Athena workgroups for each team within the compan
• F. Set up IAM workgroup policies that control user access and actions on the workgroup resources.
• G. Create Athena query groups for each team within the company and assign users to the groups.

Answer: C

Explanation:
https://aws.amazon.com/about-aws/whats-new/2019/02/athena_workgroups/

NEW QUESTION 17
A team of data scientists plans to analyze market trend data for their company’s new investment strategy. The trend data comes from five different data sources in large volumes. The team wants to utilize Amazon Kinesis to support their use case. The team uses SQL-like queries to analyze trends and wants to send notifications based on certain significant patterns in the trends. Additionally, the data scientists want to save the data to Amazon S3 for archival and historical re-processing, and use AWS managed services wherever possible. The team wants to implement the lowest-cost solution.
Which solution meets these requirements?

• A. Publish data to one Kinesis data strea
• B. Deploy a custom application using the Kinesis Client Library (KCL) for analyzing trends, and send notifications using Amazon SN
• C. Configure Kinesis Data Firehose on the Kinesis data stream to persist data to an S3 bucket.
• D. Publish data to one Kinesis data strea
• E. Deploy Kinesis Data Analytic to the stream for analyzing trends, and configure an AWS Lambda function as an output to send notifications using Amazon SN
• F. Configure Kinesis Data Firehose on the Kinesis data stream to persist data to an S3 bucket.
• G. Publish data to two Kinesis data stream
• H. Deploy Kinesis Data Analytics to the first stream for analyzing trends, and configure an AWS Lambda function as an output to send notifications using Amazon SN
• I. Configure Kinesis Data Firehose on the second Kinesis data stream to persist data to an S3 bucket.
• J. Publish data to two Kinesis data stream
• K. Deploy a custom application using the Kinesis Client Library (KCL) to the first stream for analyzing trends, and send notifications using Amazon SN
• L. Configure Kinesis Data Firehose on the second Kinesis data stream to persist data to an S3 bucket.

Answer: B

NEW QUESTION 18
A company is building a service to monitor fleets of vehicles. The company collects IoT data from a device in each vehicle and loads the data into Amazon Redshift in near-real time. Fleet owners upload .csv files containing vehicle reference data into Amazon S3 at different times throughout the day. A nightly process loads the vehicle reference data from Amazon S3 into Amazon Redshift. The company joins the IoT data from the device and the vehicle reference data to power reporting and dashboards. Fleet owners are frustrated by waiting a day for the dashboards to update.
Which solution would provide the SHORTEST delay between uploading reference data to Amazon S3 and the change showing up in the owners’ dashboards?

• A. Use S3 event notifications to trigger an AWS Lambda function to copy the vehicle reference data into Amazon Redshift immediately when the reference data is uploaded to Amazon S3.
• B. Create and schedule an AWS Glue Spark job to run every 5 minute
• C. The job inserts reference data into Amazon Redshift.
• D. Send reference data to Amazon Kinesis Data Stream
• E. Configure the Kinesis data stream to directly load the reference data into Amazon Redshift in real time.
• F. Send the reference data to an Amazon Kinesis Data Firehose delivery strea
• G. Configure Kinesis with a buffer interval of 60 seconds and to directly load the data into Amazon Redshift.

Answer: A

NEW QUESTION 19
A company is hosting an enterprise reporting solution with Amazon Redshift. The application provides reporting capabilities to three main groups: an executive group to access financial reports, a data analyst group to run long-running ad-hoc queries, and a data engineering group to run stored procedures and ETL processes. The executive team requires queries to run with optimal performance. The data engineering team expects queries to take minutes.
Which Amazon Redshift feature meets the requirements for this task?

• A. Concurrency scaling
• B. Short query acceleration (SQA)
• C. Workload management (WLM)
• D. Materialized views

Answer: D

Explanation:

Materialized views:

NEW QUESTION 20
A manufacturing company wants to create an operational analytics dashboard to visualize metrics from equipment in near-real time. The company uses Amazon Kinesis Data Streams to stream the data to other applications. The dashboard must automatically refresh every 5 seconds. A data analytics specialist must design a solution that requires the least possible implementation effort.
Which solution meets these requirements?

• A. Use Amazon Kinesis Data Firehose to store the data in Amazon S3. Use Amazon QuickSight to build the dashboard.
• B. Use Apache Spark Streaming on Amazon EMR to read the data in near-real tim
• C. Develop a custom application for the dashboard by using D3.js.
• D. Use Amazon Kinesis Data Firehose to push the data into an Amazon Elasticsearch Service (Amazon ES) cluste
• E. Visualize the data by using a Kibana dashboard.
• F. Use AWS Glue streaming ETL to store the data in Amazon S3. Use Amazon QuickSight to build the dashboard.

Answer: B

NEW QUESTION 21
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