Google Associate Data Practitioner Exam Practice Test Instant Access

Question 1

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?

ANavigate to the Logs Explorer page in Cloud Logging. Use filters to find the failed job, and analyze the error details.

BSet 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 ID.

CRequest access from your admin to the BigQuery information_schema. Query the jobs view with the failed job ID, and analyze error details.

DNavigate to the Scheduled queries page in the Google Cloud console. Select the failed job, and analyze the error details.

Answer : D

Question 2

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?

ACreate 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.

BUse the Cloud Data Fusion web interface to build data pipelines. Create a directed acyclic graph (DAG) that facilitates pipeline orchestration.

CUse the existing data pipeline tool's BigQuery connector to reconfigure the data mapping.

DUse the BigQuery Data Transfer Service to recreate the data pipeline and migrate the data into BigQuery.

Answer : C

Since your existing data pipeline tools already support connectors to BigQuery, the most efficient approach is to use the existing data pipeline tool's BigQuery connector to reconfigure the data mapping. This leverages your current tools, reducing migration complexity and setup time, while optimizing migration speed. By reconfiguring the data mapping within the existing pipeline, you can seamlessly direct the data into BigQuery without needing additional services or intermediary steps.

Question 3

Your organization has several datasets in BigQuery. The datasets need to be shared with your external partners so that they can run SQL queries without needing to copy the data to their own projects. You have organized each partner's data in its own BigQuery dataset. Each partner should be able to access only their dat

a. You want to share the data while following Google-recommended practices. What should you do?

AUse Analytics Hub to create a listing on a private data exchange for each partner dataset. Allow each partner to subscribe to their respective listings.

BCreate a Dataflow job that reads from each BigQuery dataset and pushes the data into a dedicated Pub/Sub topic for each partner. Grant each partner the pubsub. subscriber IAM role.

CExport the BigQuery data to a Cloud Storage bucket. Grant the partners the storage.objectUser IAM role on the bucket.

DGrant the partners the bigquery.user IAM role on the BigQuery project.

Answer : A

Using Analytics Hub to create a listing on a private data exchange for each partner dataset is the Google-recommended practice for securely sharing BigQuery data with external partners. Analytics Hub allows you to manage data sharing at scale, enabling partners to query datasets directly without needing to copy the data into their own projects. By creating separate listings for each partner dataset and allowing only the respective partner to subscribe, you ensure that partners can access only their specific data, adhering to the principle of least privilege. This approach is secure, efficient, and designed for scenarios involving external data sharing.

Question 4

Your team needs to analyze large datasets stored in BigQuery to identify trends in user behavior. The analysis will involve complex statistical calculations, Python packages, and visualizations. You need to recommend a managed collaborative environment to develop and share the analysis. What should you recommend?

ACreate a Colab Enterprise notebook and connect the notebook to BigQuery. Share the notebook with your team. Analyze the data and generate visualizations in Colab Enterprise.

BCreate a statistical model by using BigQuery ML. Share the query with your team. Analyze the data and generate visualizations in Looker Studio.

CCreate a Looker Studio dashboard and connect the dashboard to BigQuery. Share the dashboard with your team. Analyze the data and generate visualizations in Looker Studio.

DConnect Google Sheets to BigQuery by using Connected Sheets. Share the Google Sheet with your team. Analyze the data and generate visualizations in Gooqle Sheets.

Answer : A

Using a Colab Enterprise notebook connected to BigQuery provides a managed, collaborative environment ideal for complex statistical calculations, Python packages, and visualizations. Colab Enterprise supports Python libraries for advanced analytics and offers seamless integration with BigQuery for querying large datasets. It allows teams to collaboratively develop and share analyses while taking advantage of its visualization capabilities. This approach is particularly suitable for tasks involving sophisticated computations and custom visualizations.

Question 5

You work for a financial services company that handles highly sensitive dat

a. Due to regulatory requirements, your company is required to have complete and manual control of data encryption. Which type of keys should you recommend to use for data storage?

AUse customer-supplied encryption keys (CSEK).

BUse a dedicated third-party key management system (KMS) chosen by the company.

CUse Google-managed encryption keys (GMEK).

DUse customer-managed encryption keys (CMEK).

Answer : A

For regulatory requirements that mandate complete and manual control of data encryption, you should use customer-supplied encryption keys (CSEK). With CSEK, your company provides the encryption keys for data storage, and Google Cloud does not store or manage these keys. This approach ensures that your organization retains full control and responsibility over the encryption process, meeting strict regulatory compliance requirements.

Question 6

Your company's customer support audio files are stored in a Cloud Storage bucket. You plan to analyze the audio files' metadata and file content within BigQuery to create inference by using BigQuery ML. You need to create a corresponding table in BigQuery that represents the bucket containing the audio files. What should you do?

ACreate an external table.

BCreate a temporary table.

CCreate a native table.

DCreate an object table.

Answer : D

To analyze audio files stored in a Cloud Storage bucket and represent them in BigQuery, you should create an object table. Object tables in BigQuery are designed to represent objects stored in Cloud Storage, including their metadata. This enables you to query the metadata of audio files directly from BigQuery without duplicating the data. Once the object table is created, you can use it in conjunction with other BigQuery ML workflows for inference and analysis.

Question 7

You are a database administrator managing sales transaction data by region stored in a BigQuery table. You need to ensure that each sales representative can only see the transactions in their region. What should you do?

AAdd a policy tag in BigQuery.

BCreate a row-level access policy.

CCreate a data masking rule.

DGrant the appropriate 1AM permissions on the dataset.

Answer : B

Creating a row-level access policy in BigQuery ensures that each sales representative can see only the transactions relevant to their region. Row-level access policies allow you to define fine-grained access control by filtering rows based on specific conditions, such as matching the sales representative's region. This approach enforces security while providing tailored data access, aligning with the principle of least privilege.

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