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Quiz 2026 Professional-Data-Engineer: Google Certified Professional Data Engineer Exam–Trustable Valid Exam Cost

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Google Certified Professional Data Engineer Exam Sample Questions (Q133-Q138):

NEW QUESTION # 133
When you design a Google Cloud Bigtable schema it is recommended that you _________.

A. Create schema designs that are based on a relational database design
B. Avoid schema designs that require atomicity across rows
C. Avoid schema designs that are based on NoSQL concepts
D. Create schema designs that require atomicity across rows

Answer: B

Explanation:
All operations are atomic at the row level. For example, if you update two rows in a table, it's possible that one row will be updated successfully and the other update will fail. Avoid schema designs that require atomicity across rows.

NEW QUESTION # 134
Flowlogistic's CEO wants to gain rapid insight into their customer base so his sales team can be better informed in the field. This team is not very technical, so they've purchased a visualization tool to simplify the creation of BigQuery reports. However, they've been overwhelmed by all the data in the table, and are spending a lot of money on queries trying to find the data they need. You want to solve their problem in the most cost-effective way. What should you do?

A. Create a view on the table to present to the virtualization tool.
B. Create identity and access management (IAM) roles on the appropriate columns, so only they appear in a query.
C. Create an additional table with only the necessary columns.
D. Export the data into a Google Sheet for virtualization.

Answer: A

NEW QUESTION # 135
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. Increase the size of your parquet files to ensure them to be 1 GB minimum.
B. Switch to TFRecords formats (appr. 200MB per file) instead of parquet files.
C. Switch from HDDs to SSDs, copy initial data from GCS to HDFS, run the Spark job and copy results back to GCS.
D. Switch from HDDs to SSDs, override the preemptible VMs configuration to increase the boot disk size.

Answer: D

Explanation:
In order to increase performance switch to SSD which will be costly, so to tackle this increase the boot disk size, bootsize is worker node cache size 100 Gb.

NEW QUESTION # 136
You have an upstream process that writes data to Cloud Storage. This data is then read by an Apache Spark job that runs on Dataproc. These jobs are run in the us-central1 region, but the data could be stored anywhere in the United States. You need to have a recovery process in place in case of a catastrophic single region failure. You need an approach with a maximum of 15 minutes of data loss (RPO=15 mins). You want to ensure that there is minimal latency when reading the data. What should you do?

A. 1. Create two regional Cloud Storage buckets, one in the us-central1 region and one in the us-south1 region.
2. Have the upstream process write data to the us-central1 bucket. Use the Storage Transfer Service to copy data hourly from the us-central1 bucket to the us-south1 bucket.
3. Run the Dataproc cluster in a zone in the us-central1 region, reading from the bucket in that region.
4. In case of regional failure, redeploy your Dataproc clusters to the us-south1 region and read from the bucket in that region instead.
B. 1. Create a Cloud Storage bucket in the US multi-region.
2. Run the Dataproc cluster in a zone in the ua-central1 region, reading data from the US multi-region bucket.
3. In case of a regional failure, redeploy the Dataproc cluster to the us-central2 region and continue reading from the same bucket.
C. 1. Create a dual-region Cloud Storage bucket in the us-central1 and us-south1 regions.
2. Enable turbo replication.
3. Run the Dataproc cluster in a zone in the us-central1 region, reading from the bucket in the same region.
4. In case of a regional failure, redeploy the Dataproc clusters to the us-south1 region and read from the same bucket.
D. 1. Create a dual-region Cloud Storage bucket in the us-central1 and us-south1 regions.
2. Enable turbo replication.
3. Run the Dataproc cluster in a zone in the us-central1 region, reading from the bucket in the us-south1 region.
4. In case of a regional failure, redeploy your Dataproc duster to the us-south1 region and continue reading from the same bucket.

Answer: C

Explanation:
To ensure data recovery with minimal data loss and low latency in case of a single region failure, the best approach is to use a dual-region bucket with turbo replication. Here's why option B is the best choice:
* Dual-Region Bucket:
* A dual-region bucket provides geo-redundancy by replicating data across two regions, ensuring high availability and resilience against regional failures.
* The chosen regions (us-central1 and us-south1) provide geographic diversity within the United States.
* Turbo Replication:
* Turbo replication ensures that data is replicated between the two regions within 15 minutes, meeting the Recovery Point Objective (RPO) of 15 minutes.
* This minimizes data loss in case of a regional failure.
* Running Dataproc Cluster:
* Running the Dataproc cluster in the same region as the primary data storage (us-central1) ensures minimal latency for normal operations.
* In case of a regional failure, redeploying the Dataproc cluster to the secondary region (us-south1) ensures continuity with minimal data loss.
Steps to Implement:
* Create a Dual-Region Bucket:
* Set up a dual-region bucket in the Google Cloud Console, selecting us-central1 and us-south1 regions.
* Enable turbo replication to ensure rapid data replication between the regions.
* Deploy Dataproc Cluster:
* Deploy the Dataproc cluster in the us-central1 region to read data from the bucket located in the same region for optimal performance.
* Set Up Failover Plan:
* Plan for redeployment of the Dataproc cluster to the us-south1 region in case of a failure in the us- central1 region.
* Ensure that the failover process is well-documented and tested to minimize downtime and data loss.
Reference Links:
* Google Cloud Storage Dual-Region
* Turbo Replication in Google Cloud Storage
* Dataproc Documentation

NEW QUESTION # 137
Why do you need to split a machine learning dataset into training data and test data?

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

Answer: D

Explanation:
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 # 138
......

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