GOOGLE PROFESSIONAL-DATA-ENGINEER STUDY CENTER & PROFESSIONAL-DATA-ENGINEER BOOK FREE

Google Professional-Data-Engineer Study Center & Professional-Data-Engineer Book Free

Google Professional-Data-Engineer Study Center & Professional-Data-Engineer Book Free

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Google Professional-Data-Engineer certification exam is a tough test that measures the competency of candidates in a variety of areas related to data engineering. Professional-Data-Engineer exam consists of multiple-choice questions that cover topics such as data processing, data storage, data analysis, data visualization, and machine learning. Professional-Data-Engineer Exam also includes hands-on tasks that require candidates to demonstrate their ability to use Google Cloud Platform tools and services to solve real-world data engineering problems.

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Google Certified Professional Data Engineer Exam Sample Questions (Q65-Q70):

NEW QUESTION # 65
What is the general recommendation when designing your row keys for a Cloud Bigtable schema?

  • A. Keep the row keep as an 8 bit integer
  • B. Keep your row key as long as the field permits
  • C. Keep your row key reasonably short
  • D. Include multiple time series values within the row key

Answer: C

Explanation:
Explanation
A general guide is to, keep your row keys reasonably short. Long row keys take up additional memory and storage and increase the time it takes to get responses from the Cloud Bigtable server.
Reference: https://cloud.google.com/bigtable/docs/schema-design#row-keys


NEW QUESTION # 66
You are building a new data pipeline to share data between two different types of applications: jobs generators and job runners. Your solution must scale to accommodate increases in usage and must accommodate the addition of new applications without negatively affecting the performance of existing ones. What should you do?

  • A. Use a Cloud Pub/Sub topic to publish jobs, and use subscriptions to execute them
  • B. Create an API using App Engine to receive and send messages to the applications
  • C. Create a table on Cloud SQL, and insert and delete rows with the job information
  • D. Create a table on Cloud Spanner, and insert and delete rows with the job information

Answer: A

Explanation:
Pubsub is used to transmit data in real time and scale automatically.


NEW QUESTION # 67
Your weather app queries a database every 15 minutes to get the current temperature. The frontend is
powered by Google App Engine and server millions of users. How should you design the frontend to
respond to a database failure?

  • A. Issue a command to restart the database servers.
  • B. Retry the query every second until it comes back online to minimize staleness of data.
  • C. Retry the query with exponential backoff, up to a cap of 15 minutes.
  • D. Reduce the query frequency to once every hour until the database comes back online.

Answer: C


NEW QUESTION # 68
You set up a streaming data insert into a Redis cluster via a Kafka cluster. Both clusters are running on Compute Engine instances. You need to encrypt data at rest with encryption keys that you can create, rotate, and destroy as needed. What should you do?

  • A. Create a dedicated service account, and use encryption at rest to reference your data stored in your Compute Engine cluster instances as part of your API service calls.
  • B. Create encryption keys locally. Upload your encryption keys to Cloud Key Management Service. Use those keys to encrypt your data in all of the Compute Engine cluster instances.
  • C. Create encryption keys in Cloud Key Management Service. Reference those keys in your API service calls when accessing the data in your Compute Engine cluster instances.
  • D. Create encryption keys in Cloud Key Management Service. Use those keys to encrypt your data in all of the Compute Engine cluster instances.

Answer: D

Explanation:
https://cloud.google.com/compute/docs/disks/customer-managed-encryption


NEW QUESTION # 69
You need to look at BigQuery data from a specific table multiple times a day. The underlying table you are querying is several petabytes in size, but you want to filter your data and provide simple aggregations to downstream users. You want to run queries faster and get up-to-date insights quicker. What should you do?

  • A. Create a materialized view based off of the query being run.
  • B. Limit the query columns being pulled in the final result.
  • C. Run a scheduled query to pull the necessary data at specific intervals daily.
  • D. Use a cached query to accelerate time to results.

Answer: C

Explanation:
Materialized views are precomputed views that periodically cache the results of a query for increased performance and efficiency. BigQuery leverages precomputed results from materialized views and whenever possible reads only changes from the base tables to compute up-to-date results. Materialized views can significantly improve the performance of workloads that have the characteristic of common and repeated queries. Materialized views can also optimize queries with high computation cost and small dataset results, such as filtering and aggregating large tables. Materialized views are refreshed automatically when the base tables change, so they always return fresh data. Materialized views can also be used by the BigQuery optimizer to process queries to the base tables, if any part of the query can be resolved by querying the materialized view. References:
* Introduction to materialized views
* Create materialized views
* BigQuery Materialized View Simplified: Steps to Createand 3 Best Practices
* Materialized view in Bigquery


NEW QUESTION # 70
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