Google Professional Data Engineer Exam Questions

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Google Professional Data Engineer Exam Questions

Google Professional Data Engineer Exam Questions

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Description

Exam Name: Google Cloud Certified Professional Data Engineer

Exam Code: Professional Data Engineer

Related Certification(s): Google Cloud Certified Certification

Certification Provider: Google

Actual Exam Duration: 120 Minutes

Number of Professional Data Engineer practice questions in our database: 370

Expected Professional Data Engineer Exam Topics, as suggested by Google :

Module 1: Designing data processing systems: It delves into designing for security and compliance, reliability and fidelity, flexibility and portability, and data migrations.
Module 2: Ingesting and processing the data: The topic discusses planning of the data pipelines, building the pipelines, acquisition and import of data, and deploying and operationalizing the pipelines.
Module 3: Storing the data: This topic explains how to select storage systems and how to plan using a data warehouse. Additionally, it discusses how to design for a data mesh.
Module4: Preparing and using data for analysis: Questions about data for visualization, data sharing, and assessment of data may appear.
Module 5: Maintaining and automating data workloads: It discusses optimizing resources, automation and repeatability design, and organization of workloads as per business requirements. Lastly, the topic explains monitoring and troubleshooting processes and maintaining awareness of failures.

Description
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Description

Exam Name: Google Cloud Certified Professional Data Engineer

Exam Code: Professional Data Engineer

Related Certification(s): Google Cloud Certified Certification

Certification Provider: Google

Actual Exam Duration: 120 Minutes

Number of Professional Data Engineer practice questions in our database: 370

Expected Professional Data Engineer Exam Topics, as suggested by Google :

Module 1: Designing data processing systems: It delves into designing for security and compliance, reliability and fidelity, flexibility and portability, and data migrations.
Module 2: Ingesting and processing the data: The topic discusses planning of the data pipelines, building the pipelines, acquisition and import of data, and deploying and operationalizing the pipelines.
Module 3: Storing the data: This topic explains how to select storage systems and how to plan using a data warehouse. Additionally, it discusses how to design for a data mesh.
Module4: Preparing and using data for analysis: Questions about data for visualization, data sharing, and assessment of data may appear.
Module 5: Maintaining and automating data workloads: It discusses optimizing resources, automation and repeatability design, and organization of workloads as per business requirements. Lastly, the topic explains monitoring and troubleshooting processes and maintaining awareness of failures.

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Q1. Your car factory is pushing machine measurements as messages into a Pub/Sub topic in your Google Cloud project. A Dataflow streaming job. that you wrote with the Apache Beam SDK, reads these messages, sends acknowledgment lo Pub/Sub. applies some custom business logic in a Doffs instance, and writes the result to BigQuery. You want to ensure that if your business logic fails on a message, the message will be sent to a Pub/Sub topic that you want to monitor for alerting purposes. What should you do?

A.Use an exception handling block in your Data Flow's Doffs code to push the messages that failed to be transformed through a side output and to a new Pub/Sub topic. Use Cloud Monitoring to monitor the topic/num_jnacked_messages_by_region metric on this new topic.

B. Enable retaining of acknowledged messages in your Pub/Sub pull subscription. Use Cloud Monitoring to monitor the subscription/num_retained_acked_messages metric on this subscription.

C. Enable dead lettering in your Pub/Sub pull subscription, and specify a new Pub/Sub topic as the dead letter topic. Use Cloud Monitoring to monitor the subscription/dead_letter_message_count metric on your pull subscription.

D. Create a snapshot of your Pub/Sub pull subscription. Use Cloud Monitoring to monitor the snapshot/numessages metric on this snapshot.

Answer

Correct Answer: C

Q2. One of your encryption keys stored in Cloud Key Management Service (Cloud KMS) was exposed. You need to re-encrypt all of your CMEK-protected Cloud Storage data that used that key. and then delete the compromised key. You also want to reduce the risk of objects getting written without customer-managed encryption key (CMEK protection in the future. What should you do?

A.Rotate the Cloud KMS key version. Continue to use the same Cloud Storage bucket.

B. Create a new Cloud KMS key. Set the default CMEK key on the existing Cloud Storage bucket to the new one.

C. Create a new Cloud KMS key. Create a new Cloud Storage bucket. Copy all objects from the old bucket to the new one bucket while specifying the new Cloud KMS key in the copy command.

D. Create a new Cloud KMS key. Create a new Cloud Storage bucket configured to use the new key as the default CMEK key. Copy all objects from the old bucket to the new bucket without specifying a key.

Answer

Correct Answer: C

Q3. You are running your BigQuery project in the on-demand billing model and are executing a change data capture (CDC) process that ingests dat a. The CDC process loads 1 GB of data every 10 minutes into a temporary table, and then performs a merge into a 10 TB target table. This process is very scan intensive and you want to explore options to enable a predictable cost model. You need to create a BigQuery reservation based on utilization information gathered from BigQuery Monitoring and apply the reservation to the CDC process. What should you do?

A.Create a BigQuery reservation for the job.

B. Create a BigQuery reservation for the service account running the job.

C. Create a BigQuery reservation for the dataset.

D. Create a BigQuery reservation for the project.

Answer

Correct Answer: D

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