When using Cloud Dataproc clusters, you can access the YARN web interface by configuring a browser to connect through a proxy.

• A. VPN
• B. HTTPS
• C. SOCKS
• D. HTTP

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. Use Cloud Deployment Manager to automate access provision.
• 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. Only use service accounts when sharing data for Cloud Storage buckets and BigQuery datasets.
• E. For each Cloud Storage bucket or BigQuery dataset, decide which projects need acces
• F. Create distinct groups for various teams, and specify groups in Cloud IAM policies.

You have a data pipeline with a Cloud Dataflow job that aggregates and writes time series metrics to Cloud Bigtable. This data feeds a dashboard used by thousands of users across the organization. You need to support additional concurrent users and reduce the amount of time required to write the dat a. Which two actions should you take? (Choose two.)

• A. Configure your Cloud Dataflow pipeline to use local execution
• B. Modify your Cloud Dataflow pipeline to use the Flatten transform before writing to Cloud Bigtable
• C. Modify your Cloud Dataflow pipeline to use the CoGroupByKey transform before writing to Cloud Bigtable
• D. Increase the maximum number of Cloud Dataflow workers by setting maxNumWorkers in PipelineOptions
• E. Increase the number of nodes in the Cloud Bigtable cluster

You are building a new application that you need to collect data from in a scalable way. Data arrives continuously from the application throughout the day, and you expect to generate approximately 150 GB of JSON data per day by the end of the year. Your requirements are: Decoupling producer from consumer Space and cost-efficient storage of the raw ingested data, which is to be stored indefinitely Near real-time SQL query Maintain at least 2 years of historical data, which will be queried with SQ Which pipeline should you use to meet these requirements?

• A. Create an application that publishes events to Cloud Pub/Sub, and create Spark jobs on Cloud Dataproc to convert the JSON data to Avro format, stored on HDFS on Persistent Disk.
• B. Create an application that provides an AP
• C. Create an application that publishes events to Cloud Pub/Sub, and create a Cloud Dataflow pipeline that transforms the JSON event payloads to Avro, writing the data to Cloud Storage and BigQuery.
• D. Create an application that writes to a Cloud SQL database to store the dat
• E. Set up periodic exports of the database to write to Cloud Storage and load into BigQuery.
• F. Write a tool to poll the API and write data to Cloud Storage as gzipped JSON files.

Each analytics team in your organization is running BigQuery jobs in their own projects. You want to enable each team to monitor slot usage within their projects. What should you do?

• A. Create an aggregated log export at the organization level, capture the BigQuery job execution logs, create a custom metric based on the totalSlotMs, and create a Stackdriver Monitoring dashboard based on the custom metric
• B. Create a Stackdriver Monitoring dashboard based on the BigQuery metric slots/allocated_for_project
• C. Create a log export for each project, capture the BigQuery job execution logs, create a custom metric based on the totalSlotMs, and create a Stackdriver Monitoring dashboard based on the custom metric
• D. Create a Stackdriver Monitoring dashboard based on the BigQuery metric query/scanned_bytes