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Google Certified Professional Data Engineer Exam Sample Questions (Q183-Q188):

NEW QUESTION # 183
You are designing storage for two relational tables that are part of a 10-TB database on Google Cloud. You want to support transactions that scale horizontally. You also want to optimize data for range queries on non-key columns. What should you do?

A. Use Cloud SQL for storage. Add secondary indexes to support query patterns.
B. Use Cloud Spanner for storage. Add secondary indexes to support query patterns.
C. Use Cloud Spanner for storage. Use Cloud Dataflow to transform data to support query patterns.
D. Use Cloud SQL for storage. Use Cloud Dataflow to transform data to support query patterns.

Answer: B

NEW QUESTION # 184
You've migrated a Hadoop job from an on-prem cluster to dataproc and GCS. Your Spark job is a complicated analytical workload that consists of many shuffing operations and initial data are parquet files (on average
200-400 MB size each). You see some degradation in performance after the migration to Dataproc, so you'd like to optimize for it. You need to keep in mind that your organization is very cost-sensitive, so you'd like to continue using Dataproc on preemptibles (with 2 non-preemptible workers only) for this workload.
What should you do?

A. Switch from HDDs to SSDs, override the preemptible VMs configuration to increase the boot disk size.
B. Switch from HDDs to SSDs, copy initial data from GCS to HDFS, run the Spark job and copy results back to GCS.
C. Switch to TFRecords formats (appr. 200MB per file) instead of parquet files.
D. Increase the size of your parquet files to ensure them to be 1 GB minimum.

Answer: B

NEW QUESTION # 185
You need to move 2 PB of historical data from an on-premises storage appliance to Cloud Storage within six months, and your outbound network capacity is constrained to 20 Mb/sec. How should you migrate this data to Cloud Storage?

A. Use Transfer Appliance to copy the data to Cloud Storage
B. Use trickle or ionice along with gsutil cp to limit the amount of bandwidth gsutil utilizes to less than 20 Mb/ sec so it does not interfere with the production traffic
C. Use gsutil cp -Jto compress the content being uploaded to Cloud Storage
D. Create a private URL for the historical data, and then use Storage Transfer Service to copy the data to Cloud Storage

Answer: A

Explanation:
Explanation/Reference:

NEW QUESTION # 186
You are updating the code for a subscriber to a Put/Sub feed. You are concerned that upon deployment the subscriber may erroneously acknowledge messages, leading to message loss. You subscriber is not set up to retain acknowledged messages. What should you do to ensure that you can recover from errors after deployment?

A. Enable dead-lettering on the Pub/Sub topic to capture messages that aren't successful acknowledged if an error occurs after deployment, re-deliver any messages captured by the dead-letter queue
B. Use Cloud Build for your deployment if an error occurs after deployment, use a Seek operation to locate a tmestamp logged by Cloud Build at the start of the deployment
C. Create a Pub/Sub snapshot before deploying new subscriber code. Use a Seek operation to re-deliver messages that became available after the snapshot was created
D. Set up the Pub/Sub emulator on your local machine Validate the behavior of your new subscriber togs before deploying it to production

Answer: C

NEW QUESTION # 187
Why do you need to split a machine learning dataset into training data and test data?

A. So you can use one dataset for a wide model and one for a deep model
B. To make sure your model is generalized for more than just the training data
C. So you can try two different sets of features
D. To allow you to create unit tests in your code

Answer: B

Explanation:
The flaw with evaluating a predictive model on training data is that it does not inform you on how well the model has generalized to new unseen data. A model that is selected for its accuracy on the training dataset rather than its accuracy on an unseen test dataset is very likely to have lower accuracy on an unseen test dataset. The reason is that the model is not as generalized. It has specialized to the structure in the training dataset. This is called overfitting.
Reference: https://machinelearningmastery.com/a-simple-intuition-for-overfitting/

NEW QUESTION # 188
......

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100% Pass Professional-Data-Engineer - Newest Test Google Certified Professional Data Engineer Exam Pdf

Reliable Professional-Data-Engineer Study Plan - Exam Professional-Data-Engineer Tutorial

Google Certified Professional Data Engineer Exam Sample Questions (Q183-Q188):

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