Instantly download ADP updated real questions

Your organization has highly sensitive data that gets updated once a day and is stored across multiple datasets in BigQuery. You need to provide a new data analyst access to query specific data in BigQuery while preventing access to sensitive dat a. What should you do?

• A. Create a new Google Cloud project, and copy the limited data into a BigQuery table. Grant the data analyst the BigQuery Data Owner IAM role in the new Google Cloud project.
• B. Grant the data analyst the BigQuery Job User IAM role in the Google Cloud project.
• C. Create a materialized view with the limited data in a new dataset. Grant the data analyst BigQuery Data Viewer IAM role in the dataset and the BigQuery Job User IAM role in the Google Cloud project.
• D. Grant the data analyst the BigQuery Data Viewer IAM role in the Google Cloud project.

Your organization uses a BigQuery table that is partitioned by ingestion time. You need to remove data that is older than one year to reduce your organization's storage costs. You want to use the most efficient approach while minimizing cost. What should you do?

• A. Set the table partition expiration period to one year using the ALTER TABLE statement in SQL.
• B. Create a scheduled query that periodically runs an update statement in SQL that sets the "deleted" column to "yes" for data that is more than one year old. Create a view that filters out rows that have been marked deleted.
• C. Require users to specify a partition filter using the alter table statement in SQL.
• D. Create a view that filters out rows that are older than one year.

You have created a LookML model and dashboard that shows daily sales metrics for five regional managers to use. You want to ensure that the regional managers can only see sales metrics specific to their region. You need an easy-to-implement solution. What should you do?

• A. Add anaccess_filterExplore filter on theregion_namedimension by using thesales_regionuser attribute.
• B. Create asales_regionuser attribute, and assign each manager's region as the value of their user attribute.
• C. Create five different Explores with thesql_always_filterExplore filter applied on theregion_namedimension. Set eachregion_namevalue to the corresponding region for each manager.
• D. Create separate Looker dashboards for each regional manager. Set the default dashboard filter to the corresponding region for each manager.

You are developing a data ingestion pipeline to load small CSV files into BigQuery from Cloud Storage. You want to load these files upon arrival to minimize data latency. You want to accomplish this with minimal cost and maintenance. What should you do?

• A. Create a Dataproc cluster to pull CSV files from Cloud Storage, process them using Spark, and write the results to BigQuery.
• B. Create a Cloud Run function to load the data into BigQuery that is triggered when data arrives in Cloud Storage.
• C. Use the bq command-line tool within a Cloud Shell instance to load the data into BigQuery.
• D. Create a Cloud Composer pipeline to load new files from Cloud Storage to BigQuery and schedule it to run every 10 minutes.

Your organization has a petabyte of application logs stored as Parquet files in Cloud Storage. You need to quickly perform a one- time SQL-based analysis of the files and join them to data that already resides in BigQuery. What should you do?

• A. Launch a Cloud Data Fusion environment, use plugins to connect to BigQuery and Cloud Storage, and use the SQL join operation to analyze the data.
• B. Create a Dataproc cluster, and write a PySpark job to join the data from BigQuery to the files in Cloud Storage.
• C. Create external tables over the files in Cloud Storage, and perform SQL joins to tables in BigQuery to analyze the data.
• D. Use the bq load command to load the Parquet files into BigQuery, and perform SQL joins to analyze the data.