Which of these is not a supported method of putting data into a partitioned table?

• A. Create a partitioned table and stream new records to it every day.
• B. If you have existing data in a separate file for each day, then create a partitioned table and upload each file into the appropriate partition.
• C. Run a query to get the records for a specific day from an existing table and for the destination table, specify a partitioned table ending with the day in the format "$YYYYMMDD".
• D. Use ORDER BY to put a table's rows into chronological order and then change the table's type to "Partitioned".

Which row keys are likely to cause a disproportionate number of reads and/or writes on a particular node in a Bigtable cluster (select 2 answers)?

• A. A sequential numeric ID
• B. A timestamp followed by a stock symbol
• C. A stock symbol followed by a timestamp
• D. A non-sequential numeric ID

You operate an IoT pipeline built around Apache Kafka that normally receives around 5000 messages per second. You want to use Google Cloud Platform to create an alert as soon as the moving average over 1 hour drops below 4000 messages per second. What should you do?

• A. Use a Cloud Dataflow template to write your messages from Cloud Pub/Sub to Cloud Bigtabl
• B. Set a sliding time window of 1 hour every 5 minute
• C. If that number falls below 4000, send an alert.
• D. Consume the stream of data in Cloud Dataflow using Kafka I
• E. Set a fixed time window of 1 hour.Compute the average when the window closes, and send an alert if the average is less than 4000 messages.
• F. Use Kafka Connect to link your Kafka message queue to Cloud Pub/Su
• G. Consume the stream of data in Cloud Dataflow using Kafka I
• H. Compute the average when the window closes, and send an alert if the average is less than 4000 messages.
• I. If that number falls below 4000, send an alert.
• J. Use Kafka Connect to link your Kafka message queue to Cloud Pub/Su
• K. Use Cloud Scheduler to run a script every five minutes that counts the number of rows created in BigQuery in the last hour
• L. Use Cloud Scheduler to run a script every hour that counts the number of rows created in Cloud Bigtable in the last hour
• M. Use a Cloud Dataflow template to write your messages from Cloud Pub/Sub to BigQuer

You are running a pipeline in Cloud Dataflow that receives messages from a Cloud Pub/Sub topic and writes the results to a BigQuery dataset in the EU. Currently, your pipeline is located in europe-west4 and has a maximum of 3 workers, instance type n1-standard-1. You notice that during peak periods, your pipeline is struggling to process records in a timely fashion, when all 3 workers are at maximum CPU utilization. Which two actions can you take to increase performance of your pipeline? (Choose two.)

• A. Create a new step in your pipeline to write to this table first, and then create a new pipeline to write from Cloud Bigtable to BigQuery
• B. Create a temporary table in Cloud Bigtable that will act as a buffer for new data
• C. Use a larger instance type for your Cloud Dataflow workers
• D. Create a temporary table in Cloud Spanner that will act as a buffer for new data
• E. Create a new step in your pipeline to write to this table first, and then create a new pipeline to write from Cloud Spanner to BigQuery
• F. Increase the number of max workers
• G. Change the zone of your Cloud Dataflow pipeline to run in us-central1

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