If a dataset contains rows with individual people and columns for year of birth, country, and income, how many of the columns are continuous and how many are categorical?

• A. 2 continuous and 1 categorical
• B. 3 continuous
• C. 1 continuous and 2 categorical
• D. 3 categorical

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You want to create a machine learning model using BigQuery ML and create an endpoint for hosting the model using Vertex AI. This will enable the processing of continuous streaming data in near-real time from multiple vendors. The data may contain invalid values. What should you do?

• A. Use BigQuery streaming inserts to land the data from multiple vendors where your BigQuery dataset ML model is deployed.
• B. Create a new BigQuery dataset and use streaming inserts to land the data from multiple vendors.
  Configure your BigQuery ML model to use the "ingestion" dataset as the framing data.
• C. Create a Pub/Sub topic and send all vendor data to it. Use Dataflow to process and sanitize the Pub/ Sub data and stream it to BigQuery.
• D. Create a Pub/Sub topic and send all vendor data to it. Connect a Cloud Function to the topic to process the data and store it in BigQuery.

Your company has data assets across multiple Cloud Storage buckets and BigQuery datasets containing raw and processed data. The requirement is to establish a unified data governance framework that allows for centralized metadata discovery, data quality monitoring, and consistent security policy application across these various data stores without physically moving or duplicating the data. You need to implement a solution to achieve this federated governance.
What should you do?

• A. 1. Deploy a centralized Cloud SQL database to store metadata extracted from BigQuery and Cloud Storage using custom scripts.
  2. Integrate the database with Looker Studio for data discovery and visualization.
  3. Implement a custom policy engine using Cloud Run functions triggered by changes in IAM policies to enforce consistent security across projects.
• B. 1. Export metadata out of Dataplex Universal Catalog by running a metadata export job.
  2. Implement Dataproc Metastore to manage table schemas and Apache Hive metastore for metadata discovery.
  3. Manage security using a combination of BigQuery row-level security and Cloud Storage policies.
• C. 1. Create a Looker Studio dashboard on BigQuery INFORMATION_SCHEMAviews to visualize and monitor data quality.
  2. Manage security using IAM policies at the project level, supplemented by BigQuery authorized views for granular access control.
• D. 1. Use Dataplex to organize the BigQuery datasets and Cloud Storage buckets into lakes and zones.
  2. Use Dataplex for automated metadata discovery, centralized security policy management, data profiling, and data quality tasks.

설명: (Fast2test 회원만 볼 수 있음)

You have a data stored in BigQuery. The data in the BigQuery dataset must be highly available.
You need to define a storage, backup, and recovery strategy of this data that minimizes cost.
How should you configure the BigQuery table?

• A. Set the BigQuery dataset to be multi-regional. In the event of an emergency, use a point-in-time snapshot to recover the data.
• B. Set the BigQuery dataset to be multi-regional. Create a scheduled query to make copies of the data to tables suffixed with the time of the backup. In the event of an emergency, use the backup copy of the table.
• C. Set the BigQuery dataset to be regional. Create a scheduled query to make copies of the data to tables suffixed with the time of the backup. In the event of an emergency, use the backup copy of the table.
• D. Set the BigQuery dataset to be regional. In the event of an emergency, use a point-in-time snapshot to recover the data.

설명: (Fast2test 회원만 볼 수 있음)

You are integrating your legacy on-premises MySQL database into a new Google Cloud data warehouse. The database contains historical customer loyalty data which needs to be extracted daily and loaded into BigQuery. You want a managed integration solution that is secure and supports incremental loads. What should you do?

• A. Leverage Database Migration Service for continuous replication of the entire database to Cloud SQL.
• B. Use Cloud SQL federated queries to directly query the on-premises MySQL database from BigQuery.
• C. Configure a scheduled transfer in BigQuery Data Transfer Service to ingest data from the MySQL database with a scheduled transfer configuration.
• D. Implement a data pipeline using Pub/Sub for streaming the data changes from MySQL.

설명: (Fast2test 회원만 볼 수 있음)