Suppose you have a table that includes a nested column called "city" inside a column called "person", but when you try to submit the following query in BigQuery, it gives you an error.
SELECT person FROM `project1.example.table1` WHERE city = "London"
How would you correct the error?

• A. Change "person" to "city.person".
• B. Add ", UNNEST(city)" before the WHERE clause.
• C. Change "person" to "person.city".
• D. Add ", UNNEST(person)" before the WHERE clause.

As your organization expands its usage of GCP, many teams have started to create their own projects. Projects are further multiplied to accommodate different stages of deployments and target audiences. Each project requires unique access control configurations. The central IT team needs to have access to all projects. Furthermore, data from Cloud Storage buckets and BigQuery datasets must be shared for use in other projects in an ad hoc way. You want to simplify access control management by minimizing the number of policies. Which two steps should you take? Choose 2 answers.

• A. Introduce resource hierarchy to leverage access control policy inheritance.
• B. Only use service accounts when sharing data for Cloud Storage buckets and BigQuery datasets.
• C. Find all the active members who have access to these projects, and create a Cloud IAM policy to grant access to all these users.
• D. Use Cloud Deployment Manager to automate access provision.
• E. Create distinct groups for various teams, and specify groups in Cloud IAM policies.
• F. For each Cloud Storage bucket or BigQuery dataset, decide which projects need acces

You have developed three data processing jobs. One executes a Cloud Dataflow pipeline that transforms data uploaded to Cloud Storage and writes results to BigQuery. The second ingests data from on-premises servers and uploads it to Cloud Storage. The third is a Cloud Dataflow pipeline that gets information from third-party data providers and uploads the information to Cloud Storage. You need to be able to schedule and monitor the execution of these three workflows and manually execute them when needed. What should you do?

• A. Set up cron jobs in a Compute Engine instance to schedule and monitor the pipelines using GCP API calls.
• B. Use Stackdriver Monitoring and set up an alert with a Webhook notification to trigger the jobs.
• C. Develop an App Engine application to schedule and request the status of the jobs using GCP API calls.
• D. Create a Direct Acyclic Graph in Cloud Composer to schedule and monitor the jobs.

You have historical data covering the last three years in BigQuery and a data pipeline that delivers new data to BigQuery daily. You have noticed that when the Data Science team runs a query filtered on a date column and limited to 30-90 days of data, the query scans the entire table. You also noticed that your bill is increasing more quickly than you expected. You want to resolve the issue as cost-effectively as possible while maintaining the ability to conduct SQL queries. What should you do?

• A. Modify your pipeline to maintain the last 30-90 days of data in one table and the longer history in a different table to minimize full table scans over the entire history.
• B. Write an Apache Beam pipeline that creates a BigQuery table per data
• C. Re-create the tables using DD
• D. Recommend that the Data Science team export the table to a CSV file on Cloud Storage and use Cloud Datalab to explore the data by reading the files directly.
• E. Partition the tables by a column containing a TIMESTAMP or DATE Type.
• F. Recommend that the Data Science team use wildcards on the table name suffixes to select the data they need.