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NEW QUESTION 45
You want to automate execution of a multi-step data pipeline running on Google Cloud. The pipeline includes Cloud Dataproc and Cloud Dataflow jobs that have multiple dependencies on each other. You want to use managed services where possible, and the pipeline will run every day. Which tool should you use?
- A. Cloud Composer
- B. cron
- C. Cloud Scheduler
- D. Workflow Templates on Cloud Dataproc
NEW QUESTION 46
A data scientist has created a BigQuery ML model and asks you to create an ML pipeline to serve predictions. You have a REST API application with the requirement to serve predictions for an individual user ID with latency under 100 milliseconds. You use the following query to generate predictions: SELECT predicted_label, user_id FROM ML.PREDICT (MODEL 'dataset.model', table user_features). How should you create the ML pipeline?
- A. Create a Cloud Dataflow pipeline using BigQueryIO to read predictions for all users from the query. Write the results to Cloud Bigtable using BigtableIO. Grant the Bigtable Reader role to the application service account so that the application can read predictions for individual users from Cloud Bigtable.
- B. Create a Cloud Dataflow pipeline using BigQueryIO to read results from the query. Grant the Dataflow Worker role to the application service account.
- C. Create an Authorized View with the provided query. Share the dataset that contains the view with the application service account.
- D. Add a WHERE clause to the query, and grant the BigQuery Data Viewer role to the application service account.
NEW QUESTION 47
You are operating a streaming Cloud Dataflow pipeline. Your engineers have a new version of the pipeline with a different windowing algorithm and triggering strategy. You want to update the running pipeline with the new version. You want to ensure that no data is lost during the update. What should you do?
- A. Stop the Cloud Dataflow pipeline with the Drain option. Create a new Cloud Dataflow job with the updated code
- B. Update the Cloud Dataflow pipeline inflight by passing the --updateoption with the --jobNameset to a new unique job name
- C. Stop the Cloud Dataflow pipeline with the Cancel option. Create a new Cloud Dataflow job with the updated code
- D. Update the Cloud Dataflow pipeline inflight by passing the --update option with the --jobName set to the existing job name
NEW QUESTION 48
Your company has hired a new data scientist who wants to perform complicated analyses across very large datasets stored in Google Cloud Storage and in a Cassandra cluster on Google Compute Engine.
The scientist primarily wants to create labelled data sets for machine learning projects, along with some visualization tasks. She reports that her laptop is not powerful enough to perform her tasks and it is slowing her down. You want to help her perform her tasks. What should you do?
- A. Grant the user access to Google Cloud Shell.
- B. Host a visualization tool on a VM on Google Compute Engine.
- C. Run a local version of Jupiter on the laptop.
- D. Deploy Google Cloud Datalab to a virtual machine (VM) on Google Compute Engine.
Datalab provides Jupyter for this kind of work.
NEW QUESTION 49