2025 Training Professional-Data-Engineer For Exam | Latest 100% Free Google Certified Professional Data Engineer Exam Reliable Test Duration

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Exam Overview

The Professional Data Engineer certification exam is a 2-hour test consisting of the multiple-choice and multiple-select questions. The students can take it in the English or Japanese languages. To register for and schedule the exam, you must pay the fee of $200. It is possible to sit for this test in an online proctored format at a remote location. You can also take it as an on-site proctored exam at a designated testing center.

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Google Professional-Data-Engineer test cram - Google Certified Professional Data Engineer Exam

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Google Certified Professional Data Engineer Exam Sample Questions (Q158-Q163):

NEW QUESTION # 158
You used Cloud Dataprep to create a recipe on a sample of data in a BigQuery table. You want to reuse this recipe on a daily upload of data with the same schema, after the load job with variable execution time completes. What should you do?

A. Export the Cloud Dataprep job as a Cloud Dataflow template, and incorporate it into a Cloud Composer job.
B. Create an App Engine cron job to schedule the execution of the Cloud Dataprep job.
C. Export the recipe as a Cloud Dataprep template, and create a job in Cloud Scheduler.
D. Create a cron schedule in Cloud Dataprep.

Answer: C

Explanation:
Explanation

NEW QUESTION # 159
Which of these operations can you perform from the BigQuery Web UI?

A. Upload multiple files using a wildcard.
B. Load data with nested and repeated fields.
C. Upload a 20 MB file.
D. Upload a file in SQL format.

Answer: B

Explanation:
You can load data with nested and repeated fields using the Web UI.
You cannot use the Web UI to:
- Upload a file greater than 10 MB in size
- Upload multiple files at the same time
- Upload a file in SQL format
All three of the above operations can be performed using the "bq" command.

NEW QUESTION # 160
Data Analysts in your company have the Cloud IAM Owner role assigned to them in their projects to allow them to work with multiple GCP products in their projects. Your organization requires that all BigQuery data access logs be retained for 6 months. You need to ensure that only audit personnel in your company can access the data access logs for all projects. What should you do?

A. Export the data access logs via an aggregated export sink to a Cloud Storage bucket in a newly created project for audit logs. Restrict access to the project that contains the exported logs.
B. Export the data access logs via a project-level export sink to a Cloud Storage bucket in a newly created projects for audit logs. Restrict access to the project with the exported logs.
C. Export the data access logs via a project-level export sink to a Cloud Storage bucket in the Data Analysts' projects. Restrict access to the Cloud Storage bucket.
D. Enable data access logs in each Data Analyst's project. Restrict access to Stackdriver Logging via Cloud IAM roles.

Answer: A

Explanation:
https://cloud.google.com/iam/docs/roles-audit-logging#scenario_external_auditors

NEW QUESTION # 161
You are planning to load some of your existing on-premises data into BigQuery on Google Cloud. You want to either stream or batch-load data, depending on your use case. Additionally, you want to mask some sensitive data before loading into BigQuery. You need to do this in a programmatic way while keeping costs to a minimum. What should you do?

A. Create your pipeline with Dataflow through the Apache Beam SDK for Python, customizing separate options within your code for streaming.
batch processing, and Cloud DLP Select BigQuery as your data sink.
B. Set up Datastream to replicate your on-premise data on BigQuery.
C. Use Cloud Data Fusion to design your pipeline, use the Cloud DLP plug-in to de-identify data within your pipeline, and then move the data into BigQuery.
D. Use the BigQuery Data Transfer Service to schedule your migration. After the data is populated in BigQuery. use the connection to the Cloud Data Loss Prevention {Cloud DLP} API to de-identify the necessary data.

Answer: A

Explanation:
To load on-premises data into BigQuery while masking sensitive data, we need a solution that offers flexibility for both streaming and batch processing, as well as data masking capabilities. Here's a detailed explanation of why option B is the best choice:
Apache Beam and Dataflow:
Apache Beam SDK provides a unified programming model for both batch and stream data processing.
Google Cloud Dataflow is a fully managed service for executing Apache Beam pipelines, offering scalability and ease of use.
Customization for Different Use Cases:
By using the Apache Beam SDK, you can write custom pipelines that can handle both streaming and batch processing within the same framework.
This allows you to switch between streaming and batch modes based on your use case without changing the core logic of your data pipeline.
Data Masking with Cloud DLP:
Google Cloud Data Loss Prevention (DLP) API can be integrated into your Apache Beam pipeline to de-identify and mask sensitive data programmatically before loading it into BigQuery.
This ensures that sensitive data is handled securely and complies with privacy requirements.
Cost Efficiency:
Using Dataflow can be cost-effective because it is a fully managed service, reducing the operational overhead associated with managing your own infrastructure.
The pay-as-you-go model ensures you only pay for the resources you consume, which can help keep costs under control.
Implementation Steps:
Set up Apache Beam Pipeline:
Write a pipeline using the Apache Beam SDK for Python that reads data from your on-premises storage.
Add transformations for data processing, including the integration with Cloud DLP for data masking.
Configure Dataflow:
Deploy the Apache Beam pipeline on Google Cloud Dataflow.
Customize the pipeline options for both streaming and batch use cases.
Load Data into BigQuery:
Set BigQuery as the sink for your data in the Apache Beam pipeline.
Ensure the processed and masked data is loaded into the appropriate BigQuery tables.
Reference:
Apache Beam Documentation
Google Cloud Dataflow Documentation
Google Cloud DLP Documentation
BigQuery Documentation

NEW QUESTION # 162
You have a job that you want to cancel. It is a streaming pipeline, and you want to ensure that any data that is in-flight is processed and written to the output. Which of the following commands can you use on the Dataflow monitoring console to stop the pipeline job?

A. Finish
B. Cancel
C. Drain
D. Stop

Answer: C

Explanation:
Using the Drain option to stop your job tells the Dataflow service to finish your job in its current state. Your job will immediately stop ingesting new data from input sources, but the Dataflow service will preserve any existing resources (such as worker instances) to finish processing and writing any buffered data in your pipeline.

NEW QUESTION # 163
......

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2025 Training Professional-Data-Engineer For Exam | Latest 100% Free Google Certified Professional Data Engineer Exam Reliable Test Duration

Exam Overview

Google Professional-Data-Engineer test cram - Google Certified Professional Data Engineer Exam

Google Certified Professional Data Engineer Exam Sample Questions (Q158-Q163):

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