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Free Professional Data Engineer Exam Braindumps
Google Cloud Data Engineer Professional Questions 131–135 (page: 28)

Updated On: 10-Feb-2026
Page 28 of 78
PDF with 401 Questions

What is the recommended action to do in order to switch between SSD and HDD storage for your Google Cloud Bigtable instance?

  1. create a third instance and sync the data from the two storage types via batch jobs
  2. export the data from the existing instance and import the data into a new instance
  3. run parallel instances where one is HDD and the other is SDD
  4. the selection is final and you must resume using the same storage type

Answer(s): B

Explanation:

When you create a Cloud Bigtable instance and cluster, your choice of SSD or HDD storage for the cluster is permanent. You cannot use the Google Cloud Platform Console to change the type of storage that is used for the cluster.

If you need to convert an existing HDD cluster to SSD, or vice-versa, you can export the data from the existing instance and import the data into a new instance. Alternatively, you can write a Cloud Dataflow or Hadoop MapReduce job that copies the data from one instance to another.


Reference:

https://cloud.google.com/bigtable/docs/choosing-ssd-hdd­



You are training a spam classifier. You notice that you are overfitting the training dat

  1. Which three actions can you take to resolve this problem? (Choose three.)
  2. Get more training examples
  3. Reduce the number of training examples
  4. Use a smaller set of features
  5. Use a larger set of features
  6. Increase the regularization parameters
  7. Decrease the regularization parameters

Answer(s): A,D,F



You are implementing security best practices on your data pipeline. Currently, you are manually executing jobs as the Project Owner. You want to automate these jobs by taking nightly batch files containing non-public information from Google Cloud Storage, processing them with a Spark Scala job on a Google Cloud Dataproc cluster, and depositing the results into Google BigQuery.

How should you securely run this workload?

  1. Restrict the Google Cloud Storage bucket so only you can see the files
  2. Grant the Project Owner role to a service account, and run the job with it
  3. Use a service account with the ability to read the batch files and to write to BigQuery
  4. Use a user account with the Project Viewer role on the Cloud Dataproc cluster to read the batch files and write to BigQuery

Answer(s): B



You are using Google BigQuery as your data warehouse. Your users report that the following simple query is running very slowly, no matter when they run the query:

SELECT country, state, city FROM [myproject:mydataset.mytable] GROUP BY country

You check the query plan for the query and see the following output in the Read section of Stage:1:



What is the most likely cause of the delay for this query?

  1. Users are running too many concurrent queries in the system
  2. The [myproject:mydataset.mytable] table has too many partitions
  3. Either the state or the city columns in the [myproject:mydataset.mytable] table have too many NULL values
  4. Most rows in the [myproject:mydataset.mytable] table have the same value in the country column, causing data skew

Answer(s): A



Your globally distributed auction application allows users to bid on items. Occasionally, users place identical bids at nearly identical times, and different application servers process those bids. Each bid event contains the item, amount, user, and timestamp. You want to collate those bid events into a single location in real time to determine which user bid first.
What should you do?

  1. Create a file on a shared file and have the application servers write all bid events to that file.
    Process the file with Apache Hadoop to identify which user bid first.
  2. Have each application server write the bid events to Cloud Pub/Sub as they occur. Push the events from Cloud Pub/Sub to a custom endpoint that writes the bid event information into Cloud SQL.
  3. Set up a MySQL database for each application server to write bid events into. Periodically query each of those distributed MySQL databases and update a master MySQL database with bid event information.
  4. Have each application server write the bid events to Google Cloud Pub/Sub as they occur. Use a pull subscription to pull the bid events using Google Cloud Dataflow. Give the bid for each item to the user in the bid event that is processed first.

Answer(s): C



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