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Google Updated Professional-Data-Engineer Exam Questions and Answers by alara

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Google Professional-Data-Engineer Exam Overview :

Exam Name: Google Professional Data Engineer Exam
Exam Code: Professional-Data-Engineer Dumps
Vendor: Google Certification: Google Cloud Certified
Questions: 400 Q&A's Shared By: alara
Question 36

You want to encrypt the customer data stored in BigQuery. You need to implement for-user crypto-deletion on data stored in your tables. You want to adopt native features in Google Cloud to avoid custom solutions. What should you do?

Options:

A.

Create a customer-managed encryption key (CMEK) in Cloud KMS. Associate the key to the table while creating the table.

B.

Create a customer-managed encryption key (CMEK) in Cloud KMS. Use the key to encrypt data before storing in BigQuery.

C.

Implement Authenticated Encryption with Associated Data (AEAD) BigQuery functions while storing your data in BigQuery.

D.

Encrypt your data during ingestion by using a cryptographic library supported by your ETL pipeline.

Discussion
Question 37

You work for a large ecommerce company. You are using Pub/Sub to ingest the clickstream data to Google Cloud for analytics. You observe that when a new subscriber connects to an existing topic to analyze data, they are unable to subscribe to older data for an upcoming yearly sale event in two months, you need a solution that, once implemented, will enable any new subscriber to read the last 30 days of data. What should you do?

Options:

A.

Create a new topic, and publish the last 30 days of data each time a new subscriber connects to an existing topic.

B.

Set the topic retention policy to 30 days.

C.

Set the subscriber retention policy to 30 days.

D.

Ask the source system to re-push the data to Pub/Sub, and subscribe to it.

Discussion
Question 38

You want to migrate an Apache Spark 3 batch job from on-premises to Google Cloud. You need to minimally change the job so that the job reads from Cloud Storage and writes the result to BigQuery. Your job is optimized for Spark, where each executor has 8 vCPU and 16 GB memory, and you want to be able to choose similar settings. You want to minimize installation and management effort to run your job. What should you do?

Options:

A.

Execute the job in a new Dataproc cluster.

B.

Execute as a Dataproc Serverless job.

C.

Execute the job as part of a deployment in a new Google Kubernetes Engine cluster.

D.

Execute the job from a new Compute Engine VM.

Discussion
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Question 39

You are designing a cloud-native historical data processing system to meet the following conditions:

The data being analyzed is in CSV, Avro, and PDF formats and will be accessed by multiple analysis tools including Cloud Dataproc, BigQuery, and Compute Engine.

A streaming data pipeline stores new data daily.

Peformance is not a factor in the solution.

The solution design should maximize availability.

How should you design data storage for this solution?

Options:

A.

Create a Cloud Dataproc cluster with high availability. Store the data in HDFS, and peform analysis as needed.

B.

Store the data in BigQuery. Access the data using the BigQuery Connector or Cloud Dataproc and Compute Engine.

C.

Store the data in a regional Cloud Storage bucket. Aceess the bucket directly using Cloud Dataproc, BigQuery, and Compute Engine.

D.

Store the data in a multi-regional Cloud Storage bucket. Access the data directly using Cloud Dataproc, BigQuery, and Compute Engine.

Discussion
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