Professional Data Engineer on Google Cloud Platform: PROFESSIONAL-DATA-ENGINEER

You decided to use Cloud Datastore to ingest vehicle telemetry data in real time. You want to build a storage system that will account for the long-term data growth, while keeping the costs low. You also want to create snapshots of the data periodically, so that you can make a point-in-time (PIT) recovery, or clone a copy of the data for Cloud Datastore in a different environment. You want to archive these snapshots for a long time. Which two methods can accomplish this? Choose 2 answers.

A. Use managed export, and store the data in a Cloud Storage bucket using Nearline or Coldline class.

B. Use managed exportm, and then import to Cloud Datastore in a separate project under a unique namespace reserved for that export.

C. Use managed export, and then import the data into a BigQuery table created just for that export, and delete temporary export files.

D. Write an application that uses Cloud Datastore client libraries to read all the entities. Treat each entity as a BigQuery table row via BigQuery streaming insert. Assign an export timestamp for each export, and attach it as an extra column for each row. Make sure that the BigQuery table is partitioned using the export timestamp column.

E. Write an application that uses Cloud Datastore client libraries to read all the entities. Format the exported data into a JSON file. Apply compression before storing the data in Cloud Source Repositories.

You want to schedule a number of sequential load and transformation jobs Data files will be added to a Cloud Storage bucket by an upstream process There is no fixed schedule for when the new data arrives Next, a Dataproc job is triggered to perform some transformations and write the data to BigQuery. You then need to run additional transformation jobs in BigQuery The transformation jobs are different for every table These jobs might take hours to complete You need to determine the most efficient and maintainable workflow to process hundreds of tables and provide the freshest data to your end users. What should you do?

A. 1Create an Apache Airflow directed acyclic graph (DAG) in Cloud Composer with sequential tasks by using the Cloud Storage. Dataproc. and BigQuery operators 2 Use a single shared DAG for all tables that need to go through the pipeline 3 Schedule the DAG to run hourly

B. 1 Create an Apache Airflow directed acyclic graph (DAG) in Cloud Composer with sequential tasks by using the Dataproc and BigQuery operators. 2 Create a separate DAG for each table that needs to go through the pipeline 3 Use a Cloud Storage object trigger to launch a Cloud Function that triggers the DAG

C. 1 Create an Apache Airflow directed acyclic graph (DAG) in Cloud Composer with sequential tasks by using the Cloud Storage, Dataproc. and BigQuery operators 2 Create a separate DAG for each table that needs to go through the pipeline 3 Schedule the DAGs to run hourly

D. 1 Create an Apache Airflow directed acyclic graph (DAG) in Cloud Composer with sequential tasks by using the Dataproc and BigQuery operators 2 Use a single shared DAG for all tables that need to go through the pipeline. 3 Use a Cloud Storage object trigger to launch a Cloud Function that triggers the DAG

When you store data in Cloud Bigtable, what is the recommended minimum amount of stored data?

A. 500 TB

B. 1 GB

C. 1 TB

D. 500 GB