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Google Professional-Data-Engineer Exam Overview:

Certification Vendor:	Google Cloud
Exam Name:	Google Cloud Professional Data Engineer Exam
Exam Number:	Professional-Data-Engineer
Certificate Validity Period:	2 years
Passing Score:	700 / 1000
Related Certifications:	Google Cloud Professional Cloud Architect Google Cloud Professional Data Analyst Google Cloud Associate Cloud Engineer
Available Languages:	Japanese, English
Real Exam Qty:	40 - 50
Exam Price:	USD 200 (plus tax where applicable)
Exam Duration:	120 minutes
Exam Format:	Multiple select, Multiple choice
Recommended Training:	Google Cloud Professional Data Engineer Learning Path Official Exam Guide
Exam Registration:	Google Cloud Certification Registration
Sample Questions:	Google Professional-Data-Engineer Sample Questions
Exam Way:	Online-proctored or onsite-proctored
Pre Condition:	No mandatory prerequisites; recommended 3+ years industry experience, including 1+ year designing and managing Google Cloud data solutions
Official Syllabus URL:	https://cloud.google.com/learn/certification/data-engineer

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Google Professional-Data-Engineer Certification Exam is an online exam that tests the candidate's knowledge and skills in various areas such as data engineering, data analysis, machine learning, and big data processing. Professional-Data-Engineer exam consists of multiple-choice questions and requires a thorough understanding of the Google Cloud Platform, its services, and its features. Candidates need to demonstrate their proficiency in designing and implementing data processing systems that meet business requirements.

Google Certified Professional Data Engineer Exam Sample Questions (Q306-Q311):

NEW QUESTION # 306
Which of the following statements about Legacy SQL and Standard SQL is not true?

A. One difference between the two query languages is how you specify fully-qualified table names (i.e. table names that include their associated project name).
B. If you write a query in Legacy SQL, it might generate an error if you try to run it with Standard SQL.
C. You need to set a query language for each dataset and the default is Standard SQL.
D. Standard SQL is the preferred query language for BigQuery.

Answer: C

Explanation:
You do not set a query language for each dataset. It is set each time you run a query and the default query language is Legacy SQL.
Standard SQL has been the preferred query language since BigQuery 2.0 was released. In legacy SQL, to query a table with a project-qualified name, you use a colon, :, as a separator. In standard SQL, you use a period, ., instead.
Due to the differences in syntax between the two query languages (such as with project-qualified table names), if you write a query in Legacy SQL, it might generate an error if you try to run it with Standard SQL.
Reference:
https://cloud.google.com/bigquery/docs/reference/standard-sql/migrating-from-legacy-sql

NEW QUESTION # 307
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 to Pub/Sub, applies some custom business logic in a DoFn 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. 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.
B. Use an exception handling block in your Dataflow's DoFn 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_unacked_messages_by_region metric on this new topic.
C. 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.
D. Create a snapshot of your Pub/Sub pull subscription. Use Cloud Monitoring to monitor the snapshot/ num_messages metric on this snapshot.

Answer: B

NEW QUESTION # 308
You have a data pipeline that writes data to Cloud Bigtable using well-designed row keys. You want to monitor your pipeline to determine when to increase the size of you Cloud Bigtable cluster. Which two actions can you take to accomplish this? Choose 2 answers.

A. Monitor latency of read operations. Increase the size of the Cloud Bigtable cluster of read operations take longer than 100 ms.
B. Monitor storage utilization. Increase the size of the Cloud Bigtable cluster when utilization increases above 70% of max capacity.
C. Review Key Visualizer metrics. Increase the size of the Cloud Bigtable cluster when the Read pressure index is above 100.
D. Review Key Visualizer metrics. Increase the size of the Cloud Bigtable cluster when the Write pressure index is above 100.
E. Monitor the latency of write operations. Increase the size of the Cloud Bigtable cluster when there is a sustained increase in write latency.

Answer: C,E

NEW QUESTION # 309
How would you query specific partitions in a BigQuery table?

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

Answer: B

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')

NEW QUESTION # 310
You stream order data by using a Dataflow pipeline, and write the aggregated result to Memorystore. You provisioned a Memorystore for Redis instance with Basic Tier. 4 GB capacity, which is used by 40 clients for read-only access. You are expecting the number of read-only clients to increase significantly to a few hundred and you need to be able to support the demand. You want to ensure that read and write access availability is not impacted, and any changes you make can be deployed quickly. What should you do?

A. Create a new Memorystore for Memcached instance Set a minimum of three nodes, and memory per node to 4 GB. Modify the Dataflow pipeline and all clients to use the Memcached instance Delete the old instance.
B. Create a new Memorystore for Redis instance with Standard Tier Set capacity to 5 GB and create multiple read replicas Delete the old instance.
C. Create a new Memorystore for Redis instance with Standard Tier Set capacity to 4 GB and read replica to No read replicas (high availability only). Delete the old instance.
D. Create multiple new Memorystore for Redis instances with Basic Tier (4 GB capacity) Modify the Dataflow pipeline and new clients to use all instances

Answer: B

Explanation:
The Basic Tier of Memorystore for Redis provides a standalone Redis instance that is not replicated and does not support read replicas. This means that it cannot scale horizontally to handle more read requests, and it does not provide high availability or automatic failover. If thenumber of read-only clients increases significantly, the Basic Tier instance may not be able to handle the demand and may impact the read and write access availability. Therefore, option A is not a good solution, as it would require creating multiple Basic Tier instances and modifying the Dataflow pipeline and the clients to distribute the load among them. This would increase the complexity and the management overhead of the solution.
The Standard Tier of Memorystore for Redis provides a highly available Redis instance that supports replication and read replicas. Replication ensures that the data is backed up in another zone and can fail over automatically in case of a primary node failure. Read replicas allow scaling the read throughput by adding up to five replicas to an instance and using them for read-only queries. The Standard Tier also supports in-transit encryption and maintenance windows. Therefore, option D is the best solution, as it would create a new Standard Tier instance with a higher capacity (5 GB) and multiple read replicas to handle the increased demand. The old instance can be deleted after migrating the data to the new instance.
Option B is not a good solution, as it would create a new Standard Tier instance with the same capacity (4 GB) and no read replicas. This would not improve the read throughput or the availability of the solution.
Option C is not a good solution, as it would create a new Memorystore for Memcached instance, which is a different service that uses a different protocol and data model than Redis. This would require changing the code of the Dataflow pipeline and the clients to use the Memcached protocol and data structures, which would take more time and effort than migrating to a new Redis instance. References: Redis tier capabilities | Memorystore for Redis | Google Cloud, Pricing | Memorystore for Redis | Google Cloud, What is Memorystore? | Google Cloud Blog, Working with GCP Memorystore - Simple Talk - Redgate Software

NEW QUESTION # 311
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