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Google Professional-Data-Engineer certification exam tests a candidate's proficiency in using Google Cloud Platform tools and services for data processing, such as Google Cloud Dataflow, Google BigQuery, Google Cloud Dataproc, and Google Cloud Pub/Sub. Professional-Data-Engineer Exam also assesses a candidate's ability to design and implement data processing systems that are secure, reliable, and cost-effective.

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Google Certified Professional Data Engineer Exam Sample Questions (Q295-Q300):

NEW QUESTION # 295
You have several Spark jobs that run on a Cloud Dataproc cluster on a schedule. Some of the jobs run in sequence, and some of the jobs run concurrently. You need to automate this process. What should you do?

A. Create an initialization action to execute the jobs
B. Create a Cloud Dataproc Workflow Template
C. Create a Directed Acyclic Graph in Cloud Composer
D. Create a Bash script that uses the Cloud SDK to create a cluster, execute jobs, and then tear down the cluster

Answer: B

NEW QUESTION # 296
What are the minimum permissions needed for a service account used with Google Dataproc?

A. Write to Google Cloud Storage; read to Google Cloud Logging
B. Execute to Google Cloud Storage; execute to Google Cloud Logging
C. Read and write to Google Cloud Storage; write to Google Cloud Logging
D. Execute to Google Cloud Storage; write to Google Cloud Logging

Answer: C

Explanation:
Service accounts authenticate applications running on your virtual machine instances to other Google Cloud Platform services. For example, if you write an application that reads and writes files on Google Cloud Storage, it must first authenticate to the Google Cloud Storage API. At a minimum, service accounts used with Cloud Dataproc need permissions to read and write to Google Cloud Storage, and to write to Google Cloud Logging.

NEW QUESTION # 297
You are designing a system that requires an ACID-compliant database. You must ensure that the system requires minimal human intervention in case of a failure. What should you do?

A. Configure a BJgQuery table with a multi-region configuration.
B. Configure a Bigtable instance with more than one cluster.
C. Configure a Cloud SQL for PostgreSQL instance with high availability enabled.
D. Configure a Cloud SQL for MySQL instance with point-in-time recovery enabled.

Answer: C

Explanation:
The best option to meet the ACID compliance and minimal human intervention requirements is to configure a Cloud SQL for PostgreSQL instance with high availability enabled. Key reasons: Cloud SQL for PostgreSQL provides full ACID compliance, unlike Bigtable which provides only atomicity and consistency guarantees.
Enabling high availability removes the need for manual failover as Cloud SQL will automatically failover to a standby replica if the leader instance goes down. Point-in-time recovery in MySQL requires manual intervention to restore data if needed. BigQuery does not provide transactional guarantees required for an ACID database. Therefore, a Cloud SQL for PostgreSQL instance with high availability meets the ACID and minimal intervention requirements best. The automatic failover will ensure availability and uptime without administrative effort.

NEW QUESTION # 298
Cloud Bigtable is Google's ______ Big Data database service.

A. NoSQL
B. SQL Server
C. Relational
D. mySQL

Answer: A

Explanation:
Explanation
Cloud Bigtable is Google's NoSQL Big Data database service. It is the same database that Google uses for services, such as Search, Analytics, Maps, and Gmail.
It is used for requirements that are low latency and high throughput including Internet of Things (IoT), user analytics, and financial data analysis.
Reference: https://cloud.google.com/bigtable/

NEW QUESTION # 299
You are designing storage for two relational tables that are part of a 10-TB database on Google Cloud. You want to support transactions that scale horizontally. You also want to optimize data for range queries on nonkey columns. What should you do?

A. Use Cloud Spanner for storage. Add secondary indexes to support query patterns.
B. Use Cloud SQL for storage. Use Cloud Dataflow to transform data to support query patterns.
C. Use Cloud Spanner for storage. Use Cloud Dataflow to transform data to support query patterns.
D. Use Cloud SQL for storage. Add secondary indexes to support query patterns.

Answer: C

NEW QUESTION # 300
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Dan Miller Dan Miller

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