Google Cloud Data Engineer Professional Professional-Data-Engineer Exam Questions

Preparing for the Professional-Data-Engineer exam is simple with CertsGate. We offer easy-to-understand study materials that help you learn the most important exam topics. You can study using our PDF questions, practice online with a real exam-style test, or use the desktop practice software. Choose the study method that works best for you and prepare at your own pace.

At CertsGate, we keep our Professional-Data-Engineer practice questions up to date. Whenever the exam syllabus or objectives change, we update our study materials so you always learn the latest topics. This helps you save time, avoid outdated content, and feel more confident when you take your exam.

Question #1 (Topic: Demo Questions)

Your company handles data processing for a number of different clients. Each client prefers to use their own suite of analytics tools, with some allowing direct query access via Google BigQuery. You need to secure the data so that clients cannot see each other’s data. You want to ensure appropriate access to the data. Which three steps should you take? (Choose three.)

Load data into different partitions.

Load data into a different dataset for each client.

Put each client’s BigQuery dataset into a different table.

Restrict a client’s dataset to approved users.

Only allow a service account to access the datasets.

Use the appropriate identity and access management (IAM) roles for each client’s users.

Correct Answer: B, D, F

Question #2 (Topic: Demo Questions)

The Development and External teams nave the project viewer Identity and Access Management (1AM) role m a folder named Visualization. You want the Development Team to be able to read data from both Cloud Storage and BigQuery, but the External Team should only be able to read data from BigQuery. What should you do?

Remove Cloud Storage IAM permissions to the External Team on the acme-raw-data project

Create Virtual Private Cloud (VPC) firewall rules on the acme-raw-data protect that deny all Ingress traffic from the External Team CIDR range

Create a VPC Service Controls perimeter containing both protects and BigQuery as a restricted API Add the External Team users to the perimeter s Access Level

Create a VPC Service Controls perimeter containing both protects and Cloud Storage as a restricted API. Add the Development Team users to the perimeter ' s Access Level

Correct Answer: C

Question #3 (Topic: Demo Questions)

Which of these statements about exporting data from BigQuery is false?

To export more than 1 GB of data, you need to put a wildcard in the destination filename.

The only supported export destination is Google Cloud Storage.

Data can only be exported in JSON or Avro format.

The only compression option available is GZIP.

Correct Answer: C

Explanation:

Data can be exported in CSV, JSON, or Avro format. If you are exporting nested or repeated data, then CSV format is not supported.

[Reference: https://cloud.google.com/bigquery/docs/exporting-data, , ]

Question #4 (Topic: Demo Questions)

You have a job that you want to cancel. It is a streaming pipeline, and you want to ensure that any data that is in-flight is processed and written to the output. Which of the following commands can you use on the Dataflow monitoring console to stop the pipeline job?

Cancel

Drain

Stop

Finish

Correct Answer: B

Explanation:

Using the Drain option to stop your job tells the Dataflow service to finish your job in its current state. Your job will immediately stop ingesting new data from input sources, but the Dataflow

service will preserve any existing resources (such as worker instances) to finish processing and writing any buffered data in your pipeline.

[Reference: https://cloud.google.com/dataflow/pipelines/stopping-a-pipeline, , ]

Question #5 (Topic: Demo Questions)

What are two of the characteristics of using online prediction rather than batch prediction?

It is optimized to handle a high volume of data instances in a job and to run more complex models.

Predictions are returned in the response message.

Predictions are written to output files in a Cloud Storage location that you specify.

It is optimized to minimize the latency of serving predictions.

Correct Answer: B, D

Explanation:

Online prediction

Optimized to minimize the latency of serving predictions.

Predictions returned in the response message.

Batch prediction

Optimized to handle a high volume of instances in a job and to run more complex models.

Predictions written to output files in a Cloud Storage location that you specify.

[Reference: https://cloud.google.com/ml-engine/docs/prediction-overview#online_prediction_versus_batch_prediction, , ]

Page: 1 / 1