Question 1

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 2 answers.

Options :

A : Publisher throughput quota is too small.

B : Total outstanding messages exceed the 10-MB maximum.

C : Error handling in the subscriber code is not handling run-time errors properly.

D : The subscriber code cannot keep up with the messages.

E : The subscriber code does not acknowledge the messages that it pulls.

Answer: C,D

Question 2

You have historical data covering the last three years in BigQuery and a data pipeline that delivers new data to BigQuery daily. You have noticed that when the Data Science team runs a query filtered on a date column and limited to 30–90 days of data, the query scans the entire table. You also noticed that your bill is increasing more quickly than you expected. You want to resolve the issue as cost-effectively as possible while maintaining the ability to conduct SQL queries. What should you do?

Options :

A : Re-create the tables using DDL. Partition the tables by a column containing a TIMESTAMP or DATE Type.

B : Recommend that the Data Science team export the table to a CSV file on Cloud Storage and use Cloud Datalab to explore the data by reading the files directly.

C : Modify your pipeline to maintain the last 30–90 days of data in one table and the longer history in a different table to minimize full table scans over the entire history.

D : Write an Apache Beam pipeline that creates a BigQuery table per day. Recommend that the Data Science team use wildcards on the table name suffixes to select the data they need.

Answer: C

Question 3

You are designing storage for very large text files for a data pipeline on Google Cloud. You want to support ANSI SQL queries. You also want to support compression and parallel load from the input locations using Google recommended practices. What should you do?

Options :

A : Transform text files to compressed Avro using Cloud Dataflow. Use BigQuery for storage and query.

B : Transform text files to compressed Avro using Cloud Dataflow. Use Cloud Storage and BigQuery permanent linked tables for query.

C : Compress text files to gzip using the Grid Computing Tools. Use BigQuery for storage and query.

D : Compress text files to gzip using the Grid Computing Tools. Use Cloud Storage, and then import into Cloud Bigtable for query.

Answer: D

Question 4

You create a new report for your large team in Google Data Studio 360. The report uses Google BigQuery as

its data source. It is company policy to ensure employees can view only the data associated with their region,

so you create and populate a table for each region. You need to enforce the regional access policy to the data.

Which two actions should you take? (Choose two.)

Options :

A : Ensure all the tables are included in global dataset.

B : Ensure each table is included in a dataset for a region.

C : Adjust the settings for each table to allow a related region-based security group view access.

D : Adjust the settings for each view to allow a related region-based security group view access.

E : Adjust the settings for each dataset to allow a related region-based security group view access.

Answer: A,D

Question 5

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

Options :

A : You expect to store at least 10 TB of data.

B : You will mostly run batch workloads with scans and writes, rather than frequently executing random reads of a small number of rows.

C : You need to integrate with Google BigQuery.

D : You will not use the data to back a user-facing or latency-sensitive application.

Answer: C