Free Google Cloud Associate Data Practitioner Exam Associate-Data-Practitioner Exam Practice Test

Your organization stores highly personal data in BigQuery and needs to comply with strict data privacy regulations. You need to ensure that sensitive data values are rendered unreadable whenever an employee leaves the organization. What should you do?

• Use AEAD functions and delete keys when employees leave the organization.
• Use dynamic data masking and revoke viewer permissions when employees leave the organization.
• Use customer-managed encryption keys (CMEK) and delete keys when employees leave the organization.
• Use column-level access controls with policy tags and revoke viewer permissions when employees leave the organization.

You need to design a data pipeline that ingests data from CSV, Avro, and Parquet files into Cloud Storage. The data includes raw user input. You need to remove all malicious SQL injections before storing the data in BigQuery. Which data manipulation methodology should you choose?

• EL
• ELT
• ETL
• ETLT

You are using your own data to demonstrate the capabilities of BigQuery to your organization’s leadership team. You need to perform a one- time load of the files stored on your local machine into BigQuery using as little effort as possible. What should you do?

• Write and execute a Python script using the BigQuery Storage Write API library.
• Create a Dataproc cluster, copy the files to Cloud Storage, and write an Apache Spark job using the spark-bigquery-connector.
• Execute the bq load command on your local machine.
• Create a Dataflow job using the Apache Beam FileIO and BigQueryIO connectors with a local runner.

You created a customer support application that sends several forms of data to Google Cloud. Your application is sending:

• Audio files from phone interactions with support agents that will be accessed during trainings.
• CSV files of users’ personally identifiable information (Pll) that will be analyzed with SQL.
• A large volume of small document files that will power other applications.You need to select the appropriate tool for each data type given the required use case, while following Google-recommended practices. Which should you choose?
• Cloud Storage
• CloudSQL for PostgreSQL
• Bigtable B.
• Filestore
• Cloud SQL for PostgreSQL
• Datastore C.
• Cloud Storage
• BigQuery
• Firestore D.
• Filestore
• Bigtable
• BigQuery

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?

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

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?

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

You are a Looker analyst. You need to add a new field to your Looker report that generates SQL that will run against your company's database. You do not have the Develop permission. What should you do?

• Create a new field in the LookML layer, refresh your report, and select your new field from the field picker.
• Create a calculated field using the Add a field option in Looker Studio, and add it to your report.
• Create a table calculation from the field picker in Looker, and add it to your report.
• Create a custom field from the field picker in Looker, and add it to your report.

You manage a web application that stores data in a Cloud SQL database. You need to improve the read performance of the application by ofloading read traffic from the primary database instance. You want to implement a solution that minimizes effort and cost. What should you do?

• Use Cloud CDN to cache frequently accessed data.
• Store frequently accessed data in a Memorystore instance.
• Migrate the database to a larger Cloud SQL instance.
• Enable automatic backups, and create a read replica of the Cloud SQL instance.

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?

• 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.
• Use the Cloud Data Fusion web interface to build data pipelines. Create a directed acyclic graph (DAG) that facilitates pipeline orchestration.
• Use the existing data pipeline tool’s BigQuery connector to reconfigure the data mapping.
• Use the BigQuery Data Transfer Service to recreate the data pipeline and migrate the data into BigQuery.

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?

• Use a Pub/Sub to BigQuery subscription, write results directly to BigQuery, and schedule a transformation query to run every five minutes.
• 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.
• Use the “Pub/Sub to BigQuery” Dataflow template with a UDF, and write the results to BigQuery.
• Use a Pub/Sub push subscription, write a Cloud Run service that accepts the messages, performs the transformations, and writes the results to BigQuery.