Question 1

Your organization has a petabyte of application logs stored as Parquet files in Cloud Storage. You need to quickly perform a one-time SQL-based analysis of the files and join them to data that already resides in BigQuery. What should you do?

Options :

A : Create a Dataproc cluster, and write a PySpark job to join the data from BigQuery to the files in Cloud Storage.

B : Launch a Cloud Data Fusion environment, use plugins to connect to BigQuery and Cloud Storage, and use the SQL join operation to analyze the data.

C : Create external tables over the files in Cloud Storage, and perform SQL joins to tables in BigQuery to analyze the data

D : Use the bq load command to load the Parquet files into BigQuery, and perform SQL joins to analyze the data.

Answer: C

Question 2

An organization is collaborating with multiple external vendors and wants to share only anonymized, aggregated user behavior data for analysis. They need a method that will prevent vendors from downloading the raw data but will allow them to access updated aggregates and perform limited analyses. Which solution should the organization use?

Options :

A : Cloud Pub/Sub

B : BigQuery Data Transfer Service

C : BigQuery Omni

D : Analytics Hub

Answer: D

Question 3

A financial services company stores large volumes of data in Cloud Storage and wants to minimize storage costs. They aim to automatically move older, infrequently accessed data to a lower-cost storage class and eventually delete it after a certain period. Which feature should they configure to achieve this cost-effective data management?

Options :

A : Cloud Pub/Sub

B : Bucket Versioning

C : Access Transparency

D : Object Lifecycle Management

Answer: D

Question 4

Your organization processes data collected from IoT sensors every day to generate analytics reports. This data is valuable for only 90 days and then becomes obsolete. Which lifecycle policy is the most efficient for managing this data in Cloud Storage?

Options :

A : Store data in the Standard class, then delete it after 90 days.

B : Store data in the Archive class and delete it after 90 days.

C : Store data in the Standard class and transition it to Archive after 30 days.

D : Store data in the Nearline class and delete it after 90 days.

Answer: A

Question 5

You have a Dataflow pipeline that processes website traffic logs stored in Cloud Storage and writes the processed data to BigQuery. You noticed that the pipeline is failing intermittently. You need to troubleshoot the issue. What should you do?

Options :

A : Use Cloud Logging to identify error groups in the pipeline-s logs. Use Cloud Monitoring to create a dashboard that tracks the number of errors in each group.

B : Use Cloud Logging to create a chart displaying the pipelines error logs. Use Metrics Explorer to validate the findings from the chart.

C : Use Cloud Logging to view error messages in the pipeline-s logs. Use Cloud Monitoring to analyze the pipeline-s metrics, such as CPU utilization and memory usage.

D : Use the Dataflow job monitoring interface to check the pipeline-s status every hour. Use Cloud Profiler to analyze the pipelines metrics, such as CPU utilization and memory usage.

Answer: C

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