Free Google Associate-Data-Practitioner Exam Dumps Questions & Answers

Your retail organization stores sensitive application usage data in Cloud Storage. You need to encrypt the data without the operational overhead of managing encryption keys. What should you do?

• A.Use customer-supplied encryption keys (CSEK).
• B.Use Google-managed encryption keys (GMEK).
• C.Use customer-supplied encryption keys (CSEK) for the sensitive data and customer-managed encryption keys (CMEK) for the less sensitive data.
• D.Use customer-managed encryption keys (CMEK).

Your company wants to implement a data transformation (ETL) pipeline for their BigQuery data warehouse.
You need to identify a managed transformation solution that allows users to develop with SQL and JavaScript, has version control, allows for modular code, and has data quality checks. What should you do?

• A.Use Dataform to define the transformations in SQLX.
• B.Use Dataproc to create an Apache Spark cluster and implement the transformations by using PySpark SQL.
• C.Create BigQuery scheduled queries to define the transformations in SQL.
• D.Create a Cloud Composer environment, and orchestrate the transformations by using the BigQueryinsertJob operator.

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.Create a Looker dashboard that is connected to BigQuery. Use LookML to predict churn.
• C.Use Dataproc to create a Spark cluster. Use the Spark MLlib within the cluster to build the churn prediction model.
• D.Export the data from BigQuery to a local machine. Use scikit-learn in a Jupyter notebook to build the churn prediction model.

You are constructing a data pipeline to process sensitive customer data stored in a Cloud Storage bucket. You need to ensure that this data remains accessible, even in the event of a single-zone outage. What should you do?

• A.Store the data in a multi-region bucket.
• B.Set up a Cloud CDN in front of the bucket.
• C.Enable Object Versioning on the bucket.
• D.Store the data in Nearline storaqe.

You are designing a pipeline to process data files that arrive in Cloud Storage by 3:00 am each day. Data processing is performed in stages, where the output of one stage becomes the input of the next. Each stage takes a long time to run. Occasionally a stage fails, and you have to address the problem. You need to ensure that the final output is generated as quickly as possible. What should you do?

• A.Design the processing as a directed acyclic graph (DAG) in Cloud Composer. Clear the state of the failed task after correcting any stage output data errors.
• B.Design the workflow as a Cloud Workflow instance. Code the workflow to jump to a given stage based on an input parameter. Rerun the workflow after correcting any stage output data errors.
• C.Design the pipeline as a set of PTransforms in Dataflow. Restart the pipeline after correcting any stage output data errors.
• D.Design a Spark program that runs under Dataproc. Code the program to wait for user input when an error is detected. Rerun the last action after correcting any stage output data errors.