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Free Professional Data Engineer Exam Braindumps (page: 38)

Page 37 of 95
PDF with 375 Questions

You are integrating one of your internal IT applications and Google BigQuery, so users can query BigQuery from the application's interface. You do not want individual users to authenticate to BigQuery and you do not want to give them access to the dataset. You need to securely access BigQuery from your IT application.

What should you do?

  1. Create groups for your users and give those groups access to the dataset
  2. Integrate with a single sign-on (SSO) platform, and pass each user's credentials along with the query request
  3. Create a service account and grant dataset access to that account. Use the service account's private key to access the dataset
  4. Create a dummy user and grant dataset access to that user. Store the username and password for that user in a file on the files system, and use those credentials to access the BigQuery dataset

Answer(s): C



You set up a streaming data insert into a Redis cluster via a Kafka cluster. Both clusters are running on

Compute Engine instances. You need to encrypt data at rest with encryption keys that you can create, rotate, and destroy as needed.
What should you do?

  1. Create a dedicated service account, and use encryption at rest to reference your data stored in your
    Compute Engine cluster instances as part of your API service calls.
  2. Create encryption keys in Cloud Key Management Service. Use those keys to encrypt your data in all of the Compute Engine cluster instances.
  3. Create encryption keys locally. Upload your encryption keys to Cloud Key Management Service.
    Use those keys to encrypt your data in all of the Compute Engine cluster instances.
  4. Create encryption keys in Cloud Key Management Service.
    Reference: those keys in your API service calls when accessing the data in your Compute Engine cluster instances.

Answer(s): C


Reference:

those keys in your API service calls when accessing the data in your Compute Engine cluster instances.

Answer(s): C



You are developing an application that uses a recommendation engine on Google Cloud. Your solution should display new videos to customers based on past views. Your solution needs to generate labels for the entities in videos that the customer has viewed. Your design must be able to provide very fast filtering suggestions based on data from other customer preferences on several TB of dat

  1. What should you do?
  2. Build and train a complex classification model with Spark MLlib to generate labels and filter the results.
    Deploy the models using Cloud Dataproc. Call the model from your application.
  3. Build and train a classification model with Spark MLlib to generate labels. Build and train a second classification model with Spark MLlib to filter results to match customer preferences. Deploy the models using Cloud Dataproc. Call the models from your application.
  4. Build an application that calls the Cloud Video Intelligence API to generate labels. Store data in Cloud
    Bigtable, and filter the predicted labels to match the user's viewing history to generate preferences.
  5. Build an application that calls the Cloud Video Intelligence API to generate labels. Store data in Cloud
    SQL, and join and filter the predicted labels to match the user's viewing history to generate preferences.

Answer(s): C



You are selecting services to write and transform JSON messages from Cloud Pub/Sub to BigQuery for a data pipeline on Google Cloud. You want to minimize service costs. You also want to monitor and accommodate input data volume that will vary in size with minimal manual intervention.
What should you do?

  1. Use Cloud Dataproc to run your transformations. Monitor CPU utilization for the cluster. Resize the number of worker nodes in your cluster via the command line.
  2. Use Cloud Dataproc to run your transformations. Use the diagnose command to generate an operational output archive. Locate the bottleneck and adjust cluster resources.
  3. Use Cloud Dataflow to run your transformations. Monitor the job system lag with Stackdriver. Use the default autoscaling setting for worker instances.
  4. Use Cloud Dataflow to run your transformations. Monitor the total execution time for a sampling of jobs. Configure the job to use non-default Compute Engine machine types when needed.

Answer(s): B






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