Google ADP - Associate Data Practitioner

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

Your organization's ecommerce website collects user activity logs using a Pub/Sub topic. Your organization's leadership team wants a dashboard that contains aggregated user engagement metrics. You need to create a solution that transforms the user activity logs into aggregated metrics, while ensuring that the raw data can be easily queried. What should you do?

• A. Create a Cloud Storage subscription to the Pub/Sub topic. Load the activity logs into a bucket using the Avro file format. Use Dataflow to transform the data, and load it into a BigQuery table for reporting.
• B. Create a Dataflow subscription to the Pub/Sub topic, and transform the activity logs. Load the transformed data into a BigQuery table for reporting.
• C. Create an event-driven Cloud Run function to trigger a data transformation pipeline to run. Load the transformed activity logs into a BigQuery table for reporting.
• D. Create a BigQuery subscription to the Pub/Sub topic, and load the activity logs into the table. Create a materialized view in BigQuery using SQL to transform the data for reporting

Your company's customer support audio files are stored in a Cloud Storage bucket. You plan to analyze the audio files' metadata and file content within BigQuery to create inference by using BigQuery ML. You need to create a corresponding table in BigQuery that represents the bucket containing the audio files. What should you do?

• A. Create a native table.
• B. Create a temporary table.
• C. Create an external table.
• D. Create an object table.

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?

• A. Create a Dataproc cluster, and write a PySpark job to join the data from BigQuery to the files in Cloud Storage.
• B. Use the bq load command to load the Parquet files into BigQuery, and perform SQL joins 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. 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.

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 DATE_DIFF() function.
• B. Create a materialized view in BigQuery that uses the SUM() function and the DATE_SUB() function.
• C. 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.
• D. Create a BigQuery table that uses the SUM() function and the _PARTITIONDATE filter.