A Review Of Precise Professional-Data-Engineer Study Guide

NEW QUESTION 1

Your company is implementing a data warehouse using BigQuery, and you have been tasked with designing the data model You move your on-premises sales data warehouse with a star data schema to BigQuery but notice performance issues when querying the data of the past 30 days Based on Google's recommended practices, what should you do to speed up the query without increasing storage costs?

• A. Denormalize the data
• B. Shard the data by customer ID
• C. Materialize the dimensional data in views
• D. Partition the data by transaction date

Answer: C

NEW QUESTION 2

As your organization expands its usage of GCP, many teams have started to create their own projects. Projects are further multiplied to accommodate different stages of deployments and target audiences. Each project requires unique access control configurations. The central IT team needs to have access to all projects. Furthermore, data from Cloud Storage buckets and BigQuery datasets must be shared for use in other projects in an ad hoc way. You want to simplify access control management by minimizing the number of policies. Which two steps should you take? Choose 2 answers.

• A. Use Cloud Deployment Manager to automate access provision.
• B. Introduce resource hierarchy to leverage access control policy inheritance.
• C. Create distinct groups for various teams, and specify groups in Cloud IAM policies.
• D. Only use service accounts when sharing data for Cloud Storage buckets and BigQuery datasets.
• E. For each Cloud Storage bucket or BigQuery dataset, decide which projects need acces
• F. Find all the active members who have access to these projects, and create a Cloud IAM policy to grant access to all these users.

Answer: AC

NEW QUESTION 3

You are developing an application that uses a recommendation engine on Google Cloud. Your solution should display new videos to customers based on past views. Your solution needs to generate labels for the entities in videos that the customer has viewed. Your design must be able to provide very fast filtering suggestions based on data from other customer preferences on several TB of data. What should you do?

• A. Build and train a complex classification model with Spark MLlib to generate labels and filter the results.Deploy the models using Cloud Datapro
• B. Call the model from your application.
• C. Build and train a classification model with Spark MLlib to generate label
• D. Build and train a second classification model with Spark MLlib to filter results to match customer preference
• E. Deploy themodels using Cloud Datapro
• F. Call the models from your application.
• G. Build an application that calls the Cloud Video Intelligence API to generate label
• H. Store data in Cloud Bigtable, and filter the predicted labels to match the user’s viewing history to generate preferences.
• I. Build an application that calls the Cloud Video Intelligence API to generate label
• J. Store data in Cloud SQL, and join and filter the predicted labels to match the user’s viewing history to generate preferences.

Answer: C

NEW QUESTION 4

Flowlogistic’s CEO wants to gain rapid insight into their customer base so his sales team can be better informed in the field. This team is not very technical, so they’ve purchased a visualization tool to simplify the creation of BigQuery reports. However, they’ve been overwhelmed by all the data in the table, and are spending a lot of money on queries trying to find the data they need. You want to solve their problem in the most cost-effective way. What should you do?

• A. Export the data into a Google Sheet for virtualization.
• B. Create an additional table with only the necessary columns.
• C. Create a view on the table to present to the virtualization tool.
• D. Create identity and access management (IAM) roles on the appropriate columns, so only they appear in a query.

Answer: C

NEW QUESTION 5

Which of the following statements is NOT true regarding Bigtable access roles?

• A. Using IAM roles, you cannot give a user access to only one table in a project, rather than all tables in a project.
• B. To give a user access to only one table in a project, grant the user the Bigtable Editor role for that table.
• C. You can configure access control only at the project level.
• D. To give a user access to only one table in a project, you must configure access through your application.

Answer: B

Explanation:
For Cloud Bigtable, you can configure access control at the project level. For example, you can grant the ability to:
Read from, but not write to, any table within the project.
Read from and write to any table within the project, but not manage instances. Read from and write to any table within the project, and manage instances. Reference: https://cloud.google.com/bigtable/docs/access-control

NEW QUESTION 6

Which of the following is not possible using primitive roles?

• A. Give a user viewer access to BigQuery and owner access to Google Compute Engine instances.
• B. Give UserA owner access and UserB editor access for all datasets in a project.
• C. Give a user access to view all datasets in a project, but not run queries on them.
• D. Give GroupA owner access and GroupB editor access for all datasets in a project.

Answer: C

Explanation:
Primitive roles can be used to give owner, editor, or viewer access to a user or group, but they can't be used to separate data access permissions from job-running permissions.
Reference: https://cloud.google.com/bigquery/docs/access-control#primitive_iam_roles

NEW QUESTION 7

You are integrating one of your internal IT applications and Google BigQuery, so users can query BigQuery from the application’s interface. You do not want individual users to authenticate to BigQuery and you do not want to give them access to the dataset. You need to securely access BigQuery from your IT application.
What should you do?

• A. Create groups for your users and give those groups access to the dataset
• B. Integrate with a single sign-on (SSO) platform, and pass each user’s credentials along with the query request
• C. Create a service account and grant dataset access to that accoun
• D. Use the service account’s private key to access the dataset
• E. Create a dummy user and grant dataset access to that use
• F. Store the username and password for that user in a file on the files system, and use those credentials to access the BigQuery dataset

Answer: C

NEW QUESTION 8

Which of the following is NOT one of the three main types of triggers that Dataflow supports?

• A. Trigger based on element size in bytes
• B. Trigger that is a combination of other triggers
• C. Trigger based on element count
• D. Trigger based on time

Answer: A

Explanation:
There are three major kinds of triggers that Dataflow supports: 1. Time-based triggers 2. Data-driven triggers. You can set a trigger to emit results from a window when that window has received a certain number of data elements. 3. Composite triggers. These triggers combine multiple time-based or data-driven triggers in some logical way
Reference: https://cloud.google.com/dataflow/model/triggers

NEW QUESTION 9

You want to migrate an on-premises Hadoop system to Cloud Dataproc. Hive is the primary tool in use, and the data format is Optimized Row Columnar (ORC). All ORC files have been successfully copied to a Cloud Storage bucket. You need to replicate some data to the cluster’s local Hadoop Distributed File System (HDFS) to maximize performance. What are two ways to start using Hive in Cloud Dataproc? (Choose two.)

• A. Run the gsutil utility to transfer all ORC files from the Cloud Storage bucket to HDF
• B. Mount the Hive tables locally.
• C. Run the gsutil utility to transfer all ORC files from the Cloud Storage bucket to any node of the Dataproc cluste
• D. Mount the Hive tables locally.
• E. Run the gsutil utility to transfer all ORC files from the Cloud Storage bucket to the master node of the Dataproc cluste
• F. Then run the Hadoop utility to copy them do HDF
• G. Mount the Hive tables from HDFS.
• H. Leverage Cloud Storage connector for Hadoop to mount the ORC files as external Hive table
• I. Replicate external Hive tables to the native ones.
• J. Load the ORC files into BigQuer
• K. Leverage BigQuery connector for Hadoop to mount the BigQuery tables as external Hive table
• L. Replicate external Hive tables to the native ones.

Answer: BC

NEW QUESTION 10

You are building a new application that you need to collect data from in a scalable way. Data arrives continuously from the application throughout the day, and you expect to generate approximately 150 GB of JSON data per day by the end of the year. Your requirements are:
Decoupling producer from consumer
Space and cost-efficient storage of the raw ingested data, which is to be stored indefinitely
Near real-time SQL query
Maintain at least 2 years of historical data, which will be queried with SQ Which pipeline should you use to meet these requirements?

• A. Create an application that provides an AP
• B. Write a tool to poll the API and write data to Cloud Storage as gzipped JSON files.
• C. Create an application that writes to a Cloud SQL database to store the dat
• D. Set up periodic exports of the database to write to Cloud Storage and load into BigQuery.
• E. Create an application that publishes events to Cloud Pub/Sub, and create Spark jobs on Cloud Dataproc to convert the JSON data to Avro format, stored on HDFS on Persistent Disk.
• F. Create an application that publishes events to Cloud Pub/Sub, and create a Cloud Dataflow pipeline that transforms the JSON event payloads to Avro, writing the data to Cloud Storage and BigQuery.

Answer: A

NEW QUESTION 11

You have uploaded 5 years of log data to Cloud Storage A user reported that some data points in the log data are outside of their expected ranges, which indicates errors You need to address this issue and be able to run the process again in the future while keeping the original data for compliance reasons. What should you do?

• A. Import the data from Cloud Storage into BigQuery Create a new BigQuery table, and skip the rows with errors.
• B. Create a Compute Engine instance and create a new copy of the data in Cloud Storage Skip the rows with errors
• C. Create a Cloud Dataflow workflow that reads the data from Cloud Storage, checks for values outside the expected range, sets the value to an appropriate default, and writes the updated records to a new dataset in Cloud Storage
• D. Create a Cloud Dataflow workflow that reads the data from Cloud Storage, checks for values outside the expected range, sets the value to an appropriate default, and writes the updated records to the same dataset in Cloud Storage

Answer: D

NEW QUESTION 12

You are operating a streaming Cloud Dataflow pipeline. Your engineers have a new version of the pipeline with a different windowing algorithm and triggering strategy. You want to update the running pipeline with the new version. You want to ensure that no data is lost during the update. What should you do?

• A. Update the Cloud Dataflow pipeline inflight by passing the --update option with the --jobName set to the existing job name
• B. Update the Cloud Dataflow pipeline inflight by passing the --update option with the --jobName set to a new unique job name
• C. Stop the Cloud Dataflow pipeline with the Cancel optio
• D. Create a new Cloud Dataflow job with the updated code
• E. Stop the Cloud Dataflow pipeline with the Drain optio
• F. Create a new Cloud Dataflow job with the updated code

Answer: A

NEW QUESTION 13

An aerospace company uses a proprietary data format to store its night data. You need to connect this new data source to BigQuery and stream the data into BigQuery. You want to efficiency import the data into BigQuery where consuming as few resources as possible. What should you do?

• A. Use a standard Dataflow pipeline to store the raw data m BigQuery and then transform the format later when the data is used
• B. Write a she script that triggers a Cloud Function that performs periodic ETL batch jobs on the new data source
• C. Use Apache Hive to write a Dataproc job that streams the data into BigQuery in CSV format
• D. Use an Apache Beam custom connector to write a Dataflow pipeline that streams the data into BigQuery in Avro format

Answer: D

NEW QUESTION 14

The marketing team at your organization provides regular updates of a segment of your customer dataset. The marketing team has given you a CSV with 1 million records that must be updated in BigQuery. When you use the UPDATE statement in BigQuery, you receive a quotaExceeded error. What should you do?

• A. Reduce the number of records updated each day to stay within the BigQuery UPDATE DML statement limit.
• B. Increase the BigQuery UPDATE DML statement limit in the Quota management section of the Google Cloud Platform Console.
• C. Split the source CSV file into smaller CSV files in Cloud Storage to reduce the number of BigQuery UPDATE DML statements per BigQuery job.
• D. Import the new records from the CSV file into a new BigQuery tabl
• E. Create a BigQuery job that merges the new records with the existing records and writes the results to a new BigQuery table.

Answer: D

NEW QUESTION 15

Which of the following are feature engineering techniques? (Select 2 answers)

• A. Hidden feature layers
• B. Feature prioritization
• C. Crossed feature columns
• D. Bucketization of a continuous feature

Answer: CD

Explanation:
Selecting and crafting the right set of feature columns is key to learning an effective model.
Bucketization is a process of dividing the entire range of a continuous feature into a set of consecutive bins/buckets, and then converting the original numerical feature into a bucket ID (as a categorical feature) depending on which bucket that value falls into.
Using each base feature column separately may not be enough to explain the data. To learn the differences between different feature combinations, we can add crossed feature columns to the model.
Reference: https://www.tensorflow.org/tutorials/wide#selecting_and_engineering_features_for_the_model

NEW QUESTION 16

You have a data pipeline with a Cloud Dataflow job that aggregates and writes time series metrics to Cloud Bigtable. This data feeds a dashboard used by thousands of users across the organization. You need to support additional concurrent users and reduce the amount of time required to write the data. Which two actions should you take? (Choose two.)

• A. Configure your Cloud Dataflow pipeline to use local execution
• B. Increase the maximum number of Cloud Dataflow workers by setting maxNumWorkers in PipelineOptions
• C. Increase the number of nodes in the Cloud Bigtable cluster
• D. Modify your Cloud Dataflow pipeline to use the Flatten transform before writing to Cloud Bigtable
• E. Modify your Cloud Dataflow pipeline to use the CoGroupByKey transform before writing to Cloud Bigtable

Answer: BC

NEW QUESTION 17

What is the general recommendation when designing your row keys for a Cloud Bigtable schema?

• A. Include multiple time series values within the row key
• B. Keep the row keep as an 8 bit integer
• C. Keep your row key reasonably short
• D. Keep your row key as long as the field permits

Answer: C

Explanation:
A general guide is to, keep your row keys reasonably short. Long row keys take up additional memory and storage and increase the time it takes to get responses from the Cloud Bigtable server.
Reference: https://cloud.google.com/bigtable/docs/schema-design#row-keys

NEW QUESTION 18

You want to process payment transactions in a point-of-sale application that will run on Google Cloud Platform. Your user base could grow exponentially, but you do not want to manage infrastructure scaling.
Which Google database service should you use?

• A. Cloud SQL
• B. BigQuery
• C. Cloud Bigtable
• D. Cloud Datastore

Answer: A

NEW QUESTION 19

How would you query specific partitions in a BigQuery table?

• A. Use the DAY column in the WHERE clause
• B. Use the EXTRACT(DAY) clause
• C. Use the __PARTITIONTIME pseudo-column in the WHERE clause
• D. Use DATE BETWEEN in the WHERE clause

Answer: C

Explanation:
Partitioned tables include a pseudo column named _PARTITIONTIME that contains a date-based timestamp for data loaded into the table. To limit a query to particular partitions (such as Jan 1st and 2nd of 2017), use a clause similar to this:
WHERE _PARTITIONTIME BETWEEN TIMESTAMP('2017-01-01') AND TIMESTAMP('2017-01-02')
Reference: https://cloud.google.com/bigquery/docs/partitioned-tables#the_partitiontime_pseudo_column

NEW QUESTION 20
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