Associate-Data-Practitioner Practice Questions

A.  

Use a Transfer Appliance.

B.  

Use Cloud Storage FUS

E.  

C.  

Use Storage Transfer Service.

D.  

Use gcloud storage commands.

A.  

Set up a Cloud Monitoring dashboard to track key Dataflow metrics, such as data throughput, error rates, and resource utilization.

B.  

Create a custom script to periodically poll the Dataflow API for job status updates, and send email alerts if any errors are identified.

C.  

Navigate to the Dataflow Jobs page in the Google Cloud console. Use the job logs and worker logs to identify the error.

D.  

Use the gcloud CLI tool to retrieve job metrics and logs, and analyze them for errors and performance bottlenecks.

A.  

Disable BiqQuery query result caching.

B.  

Modify the schema to use parameterized data types.

C.  

Create a scheduled query to calculate and store intermediate results.

D.  

Create materialized views.

A.  

Push event information to a Pub/Sub topic. Create a Dataflow job using the Dataflow job builder.

B.  

Push event information to a Pub/Sub topic. Create a Cloud Run function to subscribe to the Pub/Sub topic, apply transformations, and insert the data into BigQuery.

C.  

Push event information to a Pub/Sub topic. Create a BigQuery subscription in Pub/Sub.

D.  

Push event information to Cloud Storage, and create an external table in BigQuery. Create a BigQuery scheduled job that executes once each day to apply transformations.

A.  

Design a Spark program that runs under Dataproc. Code the program to wait for user input when an error is detected. Rerun the last action after correcting any stage output data errors.

B.  

Design the pipeline as a set of PTransforms in Dataflow. Restart the pipeline after correcting any stage output data errors.

C.  

Design the workflow as a Cloud Workflow instance. Code the workflow to jump to a given stage based on an input parameter. Rerun the workflow after correcting any stage output data errors.

D.  

Design the processing as a directed acyclic graph (DAG) in Cloud Composer. Clear the state of the failed task after correcting any stage output data errors.

A.  

Use the PythonOperator in Cloud Composer to clean the data and load it into BigQuery. Use SQL for analysis.

B.  

Use BigQuery to batch load the data into BigQuery. Use SQL for cleaning and analysis.

C.  

Use Storage Transfer Service to move the data to a different Cloud Storage bucket. Use event triggers to invoke Cloud Run functions to load the data into BigQuery. Use SQL for analysis.

D.  

Use Cloud Run functions to clean the data and load it into BigQuery. Use SQL for analysis.