Try and practice the latest Google : Associate-Data-Practitioner real questions & answers

Another team in your organization is requesting access to a BigQuery dataset. You need to share the dataset with the team while minimizing the risk of unauthorized copying of data. You also want to create a reusable framework in case you need to share this data with other teams in the future. What should you do?

• A. Export the dataset to a Cloud Storage bucket in the team's Google Cloud project that is only accessible by the team.
• B. Enable domain restricted sharing on the project. Grant the team members the BigQuery Data Viewer IAM role on the dataset.
• C. Create authorized views in the team's Google Cloud project that is only accessible by the team.
• D. Create a private exchange using Analytics Hub with data egress restriction, and grant access to the team members.

You manage a large amount of data in Cloud Storage, including raw data, processed data, and backups. Your organization is subject to strict compliance regulations that mandate data immutability for specific data types.
You want to use an efficient process to reduce storage costs while ensuring that your storage strategy meets retention requirements. What should you do?

• A. Create a Cloud Run function to periodically check object metadata, and move objects to the appropriate storage class based on age and access patterns. Use object holds to enforce immutability for specific objects.
• B. Move objects to different storage classes based on their age and access patterns. Use Cloud Key Management Service (Cloud KMS) to encrypt specific objects with customer-managed encryption keys (CMEK) to meet immutability requirements.
• C. Configure lifecycle management rules to transition objects to appropriate storage classes based on access patterns. Set up Object Versioning for all objects to meet immutability requirements.
• D. Use object holds to enforce immutability for specific objects, and configure lifecycle management rules to transition objects to appropriate storage classes based on age and access patterns.

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?

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

You are working with a large dataset of customer reviews stored in Cloud Storage. The dataset contains several inconsistencies, such as missing values, incorrect data types, and duplicate entries. You need toclean the data to ensure that it is accurate and consistent before using it for analysis. What should you do?

• A. Use BigQuery to batch load the data into BigQuery. Use SQL for cleaning and analysis.
• B. Use Storage Transfer Service to move the data to a different Cloud Storage bucket. Use event triggers to invoke Cloud Run functions to load the data into BigQuery. Use SQL for analysis.
• C. Use Cloud Run functions to clean the data and load it into BigQuery. Use SQL for analysis.
• D. Use the PythonOperator in Cloud Composer to clean the data and load it into BigQuery. Use SQL for analysis.

Your data science team needs to collaboratively analyze a 25 TB BigQuery dataset to support the development of a machine learning model. You want to use Colab Enterprise notebooks while ensuring efficient data access and minimizing cost. What should you do?

• A. Copy the BigQuery dataset to the local storage of the Colab Enterprise runtime, and analyze the data using Pandas.
• B. Use BigQuery magic commands within a Colab Enterprise notebook to query and analyze the data.
• C. Create a Dataproc cluster connected to a Colab Enterprise notebook, and use Spark to process the data in BigQuery.
• D. Export the BigQuery dataset to Google Drive. Load the dataset into the Colab Enterprise notebook using Pandas.