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Google Associate Data Practitioner Sample Questions (Q11-Q16):
NEW QUESTION # 11
You work for an ecommerce company that has a BigQuery dataset that contains customer purchase history, demographics, and website interactions. You need to build a machine learning (ML) model to predict which customers are most likely to make a purchase in the next month. You have limited engineering resources and need to minimize the ML expertise required for the solution. What should you do?
- A. Export the data to Cloud Storage, and use AutoML Tables to build a classification model for purchase prediction.
- B. Use Colab Enterprise to develop a custom model for purchase prediction.
- C. Use Vertex Al Workbench to develop a custom model for purchase prediction.
- D. Use BigQuery ML to create a logistic regression model for purchase prediction.
Answer: D
NEW QUESTION # 12
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. Use the BigQuery Data Transfer Service to recreate the data pipeline and migrate the data into BigQuery.
- B. Use the existing data pipeline tool's BigQuery connector to reconfigure the data mapping.
- C. Use the Cloud Data Fusion web interface to build data pipelines. Create a directed acyclic graph (DAG) that facilitates pipeline orchestration.
- D. 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.
Answer: B
NEW QUESTION # 13
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?
- 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 view error messages in the pipeline's logs. Use Cloud Monitoring to analyze the pipeline's metrics, such as CPU utilization and memory usage.
- C. Use Cloud Logging to create a chart displaying the pipeline's error logs. Use Metrics Explorer to validate the findings from the chart.
- D. Use the Dataflow job monitoring interface to check the pipeline's status every hour. Use Cloud Profiler to analyze the pipeline's metrics, such as CPU utilization and memory usage.
Answer: B
NEW QUESTION # 14
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.
Answer: D
NEW QUESTION # 15
You have a Dataproc cluster that performs batch processing on data stored in Cloud Storage. You need to schedule a daily Spark job to generate a report that will be emailed to stakeholders. You need a fully-managed solution that is easy to implement and minimizes complexity. What should you do?
- A. Use Cloud Scheduler to trigger the Spark job. and use Cloud Run functions to email the report.
- B. Use Dataproc workflow templates to define and schedule the Spark job, and to email the report.
- C. Use Cloud Composer to orchestrate the Spark job and email the report.
- D. Use Cloud Run functions to trigger the Spark job and email the report.
Answer: B
NEW QUESTION # 16
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