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Google Certified Professional Data Engineer Exam Sample Questions (Q275-Q280):

NEW QUESTION # 275
You have Cloud Functions written in Node.js that pull messages from Cloud Pub/Sub and send the data to BigQuery. You observe that the message processing rate on the Pub/Sub topic is orders of magnitude higher than anticipated, but there is no error logged in Stackdriver Log Viewer. What are the two most likely causes of this problem? (Choose two.)

A. Publisher throughput quota is too small.
B. The subscriber code cannot keep up with the messages.
C. Total outstanding messages exceed the 10-MB maximum.
D. Error handling in the subscriber code is not handling run-time errors properly.
E. The subscriber code does not acknowledge the messages that it pulls.

Answer: B,D

NEW QUESTION # 276
Which of the following is NOT a valid use case to select HDD (hard disk drives) as the storage for Google Cloud Bigtable?

A. You expect to store at least 10 TB of data.
B. You will not use the data to back a user-facing or latency-sensitive application.
C. You will mostly run batch workloads with scans and writes, rather than frequently executing random reads of a small number of rows.
D. You need to integrate with Google BigQuery.

Answer: D

Explanation:
For example, if you plan to store extensive historical data for a large number of remote- sensing devices and then use the data to generate daily reports, the cost savings for HDD storage may justify the performance tradeoff. On the other hand, if you plan to use the data to display a real-time dashboard, it probably would not make sense to use HDD storage-reads would be much more frequent in this case, and reads are much slower with HDD storage.
Reference: https://cloud.google.com/bigtable/docs/choosing-ssd-hdd

NEW QUESTION # 277
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. Run a scheduled query to pull the necessary data at specific intervals daily.
C. Use a cached query to accelerate time to results.
D. Limit the query columns being pulled in the final result.

Answer: B

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. Reference:
Introduction to materialized views
Create materialized views
BigQuery Materialized View Simplified: Steps to Create and 3 Best Practices Materialized view in Bigquery

NEW QUESTION # 278
You create an important report for your large team in Google Data Studio 360. The report uses Google BigQuery as its data source. You notice that visualizations are not showing data that is less than 1 hour old.
What should you do?

A. Clear your browser history for the past hour then reload the tab showing the virtualizations.
B. Disable caching by editing the report settings.
C. Disable caching in BigQuery by editing table details.
D. Refresh your browser tab showing the visualizations.

Answer: B

NEW QUESTION # 279
What are two of the benefits of using denormalized data structures in BigQuery?

A. Reduces the amount of data processed, reduces the amount of storage required
B. Reduces the amount of data processed, increases query speed
C. Increases query speed, makes queries simpler
D. Reduces the amount of storage required, increases query speed

Answer: C

Explanation:
Denormalization increases query speed for tables with billions of rows because BigQuery's performance degrades when doing JOINs on large tables, but with a denormalized data structure, you don't have to use JOINs, since all of the data has been combined into one table.
Denormalization also makes queries simpler because you do not have to use JOIN clauses.
Denormalization increases the amount of data processed and the amount of storage required because it creates redundant data.
Reference:
https://cloud.google.com/solutions/bigquery-data-warehouse#denormalizing_data

NEW QUESTION # 280
......

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Carl Lee Carl Lee

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