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Google ADP Exam Syllabus Topics:

Google Associate Data Practitioner Sample Questions:

1. Your organization uses scheduled queries to perform transformations on data stored in BigQuery. You discover that one of your scheduled queries has failed. You need to troubleshoot the issue as quickly as possible. What should you do?

A) Navigate to the Logs Explorer page in Cloud Logging. Use filters to find the failed job, and analyze the error details.
B) Set up a log sink using the gcloud CLI to export BigQuery audit logs to BigQuery. Query those logs to identify the error associated with the failed job I
C) Request access from your admin to the BigQuery information_schema. Query the jobs view with the failed job ID, and analyze error details.
D) Navigate to the Scheduled queries page in the Google Cloud console. Select the failed job, and analyze the error details.

2. You are predicting customer churn for a subscription-based service. You have a 50 PB historical customer dataset in BigQuery that includes demographics, subscription information, and engagement metrics. You want to build a churn prediction model with minimal overhead. You want to follow the Google-recommended approach. What should you do?

A) Use the BigQuery Python client library in a Jupyter notebook to query and preprocess the data in BigQuery. Use the CREATE MODEL statement in BigQueryML to train the churn prediction model.
B) Export the data from BigQuery to a local machine. Use scikit- learn in a Jupyter notebook to build the churn prediction model.
C) Use Dataproc to create a Spark cluster. Use the Spark MLlib within the cluster to build the churn prediction model.
D) Create a Looker dashboard that is connected to BigQuery. Use LookML to predict churn.

3. 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) Navigate to the Dataflow Jobs page in the Google Cloud console. Use the job logs and worker logs to identify the error.
B) Use the gcloud CLI tool to retrieve job metrics and logs, and analyze them for errors and performance bottlenecks.
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) Set up a Cloud Monitoring dashboard to track key Dataflow metrics, such as data throughput, error rates, and resource utilization.

4. You manage data at an ecommerce company. You have a Dataflow pipeline that processes order data from Pub/Sub, enriches the data with product information from Bigtable, and writes the processed data to BigQuery for analysis. The pipeline runs continuously and processes thousands of orders every minute. You need to monitor the pipeline's performance and be alerted if errors occur. What should you do?

A) Use Cloud Logging to view the pipeline logs and check for errors. Set up alerts based on specific keywords in the logs.
B) Use BigQuery to analyze the processed data in Cloud Storage and identify anomalies or inconsistencies. Set up scheduled alerts based when anomalies or inconsistencies occur.
C) Use the Dataflow job monitoring interface to visually inspect the pipeline graph, check for errors, and configure notifications when critical errors occur.
D) Use Cloud Monitoring to track key metrics. Create alerting policies in Cloud Monitoring to trigger notifications when metrics exceed thresholds or when errors occur.

5. Your company is building a near real-time streaming pipeline to process JSON telemetry data from small appliances. You need to process messages arriving at a Pub/Sub topic, capitalize letters in the serial number field, and write results to BigQuery. You want to use a managed service and write a minimal amount of code for underlying transformations. What should you do?

A) Use a Pub/Sub push subscription, write a Cloud Run service that accepts the messages, performs the transformations, and writes the results to BigQuery.
B) Use the "Pub/Sub to BigQuery" Dataflow template with a UDF, and write the results to BigQuery.
C) Use a Pub/Sub to BigQuery subscription, write results directly to BigQuery, and schedule a transformation query to run every five minutes.
D) Use a Pub/Sub to Cloud Storage subscription, write a Cloud Run service that is triggered when objects arrive in the bucket, performs the transformations, and writes the results to BigQuery.

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