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Google Associate Data Practitioner Sample Questions:

1. You recently inherited a task for managing Dataflow streaming pipelines in your organization and noticed that proper access had not been provisioned to you. You need to request a Google-provided IAM role so you can restart the pipelines. You need to follow the principle of least privilege. What should you do?

A) Request the Dataflow Developer role.
B) Request the Dataflow Admin role.
C) Request the Dataflow Viewer role.
D) Request the Dataflow Worker role.

2. You used BigQuery ML to build a customer purchase propensity model six months ago. You want to compare the current serving data with the historical serving data to determine whether you need to retrain the model.
What should you do?

A) Compare the confusion matrix.
B) Evaluate the data skewness.
C) Compare the two different models.
D) Evaluate data drift.

3. You are designing a BigQuery data warehouse with a team of experienced SQL developers. You need to recommend a cost- effective, fully-managed, serverless solution to build ELT processes with SQL pipelines.
Your solution must include source code control, environment parameterization, and data quality checks. What should you do?

A) Use Dataproc to run MapReduce jobs for distributed data processing.
B) Use Dataform to build, orchestrate, and monitor the pipelines.
C) Use Cloud Data Fusion to visually design and manage the pipelines.
D) Use Cloud Composer to orchestrate and run data workflows.

4. You are working on a project that requires analyzing daily social media dat a. You have 100 GB of JSON formatted data stored in Cloud Storage that keeps growing.
You need to transform and load this data into BigQuery for analysis. You want to follow the Google-recommended approach. What should you do?

A) Use Dataflow to transform the data and write the transformed data to BigQuery.
B) Manually download the data from Cloud Storage. Use a Python script to transform and upload the data into BigQuery.
C) Use Cloud Data Fusion to transfer the data into BigQuery raw tables, and use SQL to transform it.
D) Use Cloud Run functions to transform and load the data into BigQuery.

5. You are storing data in Cloud Storage for a machine learning project. The data is frequently accessed during the model training phase, minimally accessed after 30 days, and unlikely to be accessed after 90 days. You need to choose the appropriate storage class for the different stages of the project to minimize cost. What should you do?

A) Store the data in Standard storage during the model training phase. Transition the data to Nearline storage 30 days after model deployment, and to Coldline storage 90 days after model deployment.
B) Store the data in Nearline storage during the model training phase. Transition the data to Coldline storage 30 days after model deployment, and to Archive storage 90 days after model deployment.
C) Store the data in Nearline storage during the model training phase. Transition the data to Archive storage 30 days after model deployment, and to Coldline storage 90 days after model deployment.
D) Store the data in Standard storage during the model training phase. Transition the data to Durable Reduced Availability (DRA) storage 30 days after model deployment, and to Coldline storage 90 days after model deployment.

Solutions: