Free Access Google.Professional-Data-Engineer.v2025-03-18.q108 Practice Test (Page 4)

Question 11

Your company is migrating its on-premises data warehousing solution to BigQuery. The existing data warehouse uses trigger-based change data capture (CDC) to apply daily updates from transactional database sources Your company wants to use BigQuery to improve its handling of CDC and to optimize the performance of the data warehouse Source system changes must be available for query m near-real time using tog-based CDC streams You need to ensure that changes in the BigQuery reporting table are available with minimal latency and reduced overhead. What should you do? Choose 2 answers

A.Periodically DELETE outdated records from the reporting table
Periodically use a DML MERGE to simultaneously perform DML INSERT. UPDATE, and DELETE operations in the reporting table

B.Insert each new CDC record and corresponding operation type into the reporting table in real time and use a materialized view to expose only the current version of each unique record.

C.Insert each new CDC record and corresponding operation type into a staging table in real time

D.Perform a DML INSERT UPDATE, or DELETE to replicate each CDC record in the reporting table m real time.

Question 12

You work for a shipping company that uses handheld scanners to read shipping labels. Your company has strict data privacy standards that require scanners to only transmit recipients' personally identifiable information (PII) to analytics systems, which violates user privacy rules. You want to quickly build a scalable solution using cloud-native managed services to prevent exposure of PII to the analytics systems.
What should you do?

A.Build a Cloud Function that reads the topics and makes a call to the Cloud Data Loss Prevention API.
Use the tagging and confidence levels to either pass or quarantine the data in a bucket for review.

B.Use Stackdriver logging to analyze the data passed through the total pipeline to identify transactions that may contain sensitive information.

C.Create an authorized view in BigQuery to restrict access to tables with sensitive data.

D.Install a third-party data validation tool on Compute Engine virtual machines to check the incoming data for sensitive information.

Question 13

You are managing a Cloud Dataproc cluster. You need to make a job run faster while minimizing costs, without losing work in progress on your clusters. What should you do?

A.Increase the cluster size with preemptible worker nodes, and configure them to forcefully decommission.

B.Increase the cluster size with preemptible worker nodes, and configure them to use graceful decommissioning.

C.Increase the cluster size with more non-preemptible workers.

D.Increase the cluster size with preemptible worker nodes, and use Cloud Stackdriver to trigger a script to preserve work.

Question 14

You are building new real-time data warehouse for your company and will use Google BigQuery streaming inserts. There is no guarantee that data will only be sent in once but you do have a unique ID for each row of data and an event timestamp. You want to ensure that duplicates are not included while interactively querying data. Which query type should you use?

A.Include ORDER BY DESK on timestamp column and LIMIT to 1.

B.Use the ROW_NUMBER window function with PARTITION by unique ID along with WHERE row equals 1.

C.Use the LAG window function with PARTITION by unique ID along with WHERE LAG IS NOT NULL.

D.Use GROUP BY on the unique ID column and timestamp column and SUM on the values.

Question 15

You operate an IoT pipeline built around Apache Kafka that normally receives around 5000 messages per second. You want to use Google Cloud Platform to create an alert as soon as the moving average over 1 hour drops below 4000 messages per second. What should you do?

A.Use Kafka Connect to link your Kafka message queue to Cloud Pub/Sub. Use a Cloud Dataflow template to write your messages from Cloud Pub/Sub to BigQuery. Use Cloud Scheduler to run a script every five minutes that counts the number of rows created in BigQuery in the last hour. If that number falls below 4000, send an alert.

B.Consume the stream of data in Cloud Dataflow using Kafka IO. Set a fixed time window of 1 hour. Compute the average when the window closes, and send an alert if the average is less than 4000 messages.

C.Use Kafka Connect to link your Kafka message queue to Cloud Pub/Sub. Use a Cloud Dataflow template to write your messages from Cloud Pub/Sub to Cloud Bigtable. Use Cloud Scheduler to run a script every hour that counts the number of rows created in Cloud Bigtable in the last hour. If that number falls below 4000, send an alert.

D.Consume the stream of data in Cloud Dataflow using Kafka IO. Set a sliding time window of 1 hour every 5 minutes. Compute the average when the window closes, and send an alert if the average is less than 4000 messages.

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