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Google Associate Data Practitioner Sample Questions:

1. 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 Vertex Al Workbench to develop a custom model for purchase prediction.
C) Use BigQuery ML to create a logistic regression model for purchase prediction.
D) Use Colab Enterprise to develop a custom model for purchase prediction.

2. You manage a Cloud Storage bucket that stores temporary files created during data processing. These temporary files are only needed for seven days, after which they are no longer needed. To reduce storage costs and keep your bucket organized, you want to automatically delete these files once they are older than seven days. What should you do?

A) Develop a batch process using Dataflow that runs weekly and deletes files based on their age.
B) Create a Cloud Run function that runs daily and deletes files older than seven days.
C) Set up a Cloud Scheduler job that invokes a weekly Cloud Run function to delete files older than seven days.
D) Configure a Cloud Storage lifecycle rule that automatically deletes objects older than seven days.

3. Your company is migrating their batch transformation pipelines to Google Cloud. You need to choose a solution that supports programmatic transformations using only SQL. You also want the technology to support Git integration for version control of your pipelines. What should you do?

A) Use Cloud Data Fusion pipelines.
B) Use Dataflow pipelines.
C) Use Cloud Composer operators.
D) Use Dataform workflows.

4. You work for a healthcare company. You have a daily ETL pipeline that extracts patient data from a legacy system, transforms it, and loads it into BigQuery for analysis. The pipeline currently runs manually using a shell script. You want to automate this process and add monitoring to ensure pipeline observability and troubleshooting insights. You want one centralized solution, using open-source tooling, without rewriting the ETL code. What should you do?

A) Configure Cloud Dataflow to implement the ETL pipeline, and use Cloud Scheduler to trigger the Dataflow pipeline daily. Monitor the pipelines execution using the Dataflow job monitoring interface and Cloud Monitoring.
B) Create a Cloud Run function that runs the pipeline daily. Monitor the functions execution using Cloud Monitoring.
C) Use Cloud Scheduler to trigger a Dataproc job to execute the pipeline daily. Monitor the job's progress using the Dataproc job web interface and Cloud Monitoring.
D) Create a direct acyclic graph (DAG) in Cloud Composer to orchestrate a pipeline trigger daily. Monitor the pipeline's execution using the Apache Airflow web interface and Cloud Monitoring.

5. 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 Monitoring to track key metrics. Create alerting policies in Cloud Monitoring to trigger notifications when metrics exceed thresholds or when errors occur.
B) Use Cloud Logging to view the pipeline logs and check for errors. Set up alerts based on specific keywords in the logs.
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 BigQuery to analyze the processed data in Cloud Storage and identify anomalies or inconsistencies. Set up scheduled alerts based when anomalies or inconsistencies occur.

Solutions: