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Google ADP Exam Syllabus Topics:

Google Associate Data Practitioner Sample Questions:

1. You are developing a data ingestion pipeline to load small CSV files into BigQuery from Cloud Storage. You want to load these files upon arrival to minimize data latency. You want to accomplish this with minimal cost and maintenance. What should you do?

A) Create a Cloud Run function to load the data into BigQuery that is triggered when data arrives in Cloud Storage.
B) Use the bq command-line tool within a Cloud Shell instance to load the data into BigQuery.
C) Create a Dataproc cluster to pull CSV files from Cloud Storage, process them using Spark, and write the results to BigQuery.
D) Create a Cloud Composer pipeline to load new files from Cloud Storage to BigQuery and schedule it to run every 10 minutes.

2. Your company has an on-premises file server with 5 TB of data that needs to be migrated to Google Cloud.
The network operations team has mandated that you can only use up to 250 Mbps of the total available bandwidth for the migration. You need to perform an online migration to Cloud Storage. What should you do?

A) Use Storage Transfer Service to configure an agent-based transfer. Set the appropriate bandwidth limit for the agent pool.
B) Use the gcloud storage cp command to copy all files from on- premises to Cloud Storage using the -- daisy-chain option.
C) Request a Transfer Appliance, copy the data to the appliance, and ship it back to Google Cloud.
D) Use the gcloud storage cp command to copy all files from on- premises to Cloud Storage using the --no- clobber option.

3. Your organization has decided to migrate their existing enterprise data warehouse to BigQuery. The existing data pipeline tools already support connectors to BigQuery. You need to identify a data migration approach that optimizes migration speed. What should you do?

A) Create a temporary file system to facilitate data transfer from the existing environment to Cloud Storage. Use Storage Transfer Service to migrate the data into BigQuery.
B) Use the BigQuery Data Transfer Service to recreate the data pipeline and migrate the data into BigQuery.
C) Use the Cloud Data Fusion web interface to build data pipelines. Create a directed acyclic graph (DAG) that facilitates pipeline orchestration.
D) Use the existing data pipeline tool's BigQuery connector to reconfigure the data mapping.

4. Your organization uses Dataflow pipelines to process real-time financial transactions. You discover that one of your Dataflow jobs has failed. You need to troubleshoot the issue as quickly as possible. What should you do?

A) Use the gcloud CLI tool to retrieve job metrics and logs, and analyze them for errors and performance bottlenecks.
B) Set up a Cloud Monitoring dashboard to track key Dataflow metrics, such as data throughput, error rates, and resource utilization.
C) Create a custom script to periodically poll the Dataflow API for job status updates, and send email alerts if any errors are identified.
D) Navigate to the Dataflow Jobs page in the Google Cloud console. Use the job logs and worker logs to identify the error.

5. Your team wants to create a monthly report to analyze inventory data that is updated daily. You need to aggregate the inventory counts by using only the most recent month of data, and save the results to be used in a Looker Studio dashboard. What should you do?

A) Create a BigQuery table that uses the SUM() function and the _PARTITIONDATE filter.
B) Create a saved query in the BigQuery console that uses the SUM() function and the DATE_SUB() function. Re-run the saved query every month, and save the results to a BigQuery table.
C) Create a BigQuery table that uses the SUM() function and the DATE_DIFF() function.
D) Create a materialized view in BigQuery that uses the SUM() function and the DATE_SUB() function.

Solutions: