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Google Professional-Data-Engineer Exam Overview:

Certification Vendor:	Google Cloud
Exam Name:	Google Cloud Certified Professional Data Engineer Exam
Exam Number:	Professional Data Engineer
Exam Format:	multiple select, case study, multiple choice
Available Languages:	German, English, Japanese, French, Spanish, Portuguese
Exam Duration:	120 minutes
Passing Score:	Not publicly disclosed (scaled scoring system)
Exam Price:	$200 USD
Certificate Validity Period:	2 years
Real Exam Qty:	Approximately 50–60 multiple choice and multiple select questions
Related Certifications:	Google Cloud Certified - Professional Cloud Architect Google Cloud Certified - Professional Data Analytics Engineer Google Cloud Certified - Professional Machine Learning Engineer Google Cloud Certified - Associate Cloud Engineer
Recommended Training:	Google Cloud Professional Data Engineer Course (Coursera) Google Cloud Data Engineering Learning Path
Exam Registration:	Google Cloud Certification Registration
Sample Questions:	Google Professional-Data-Engineer Sample Questions
Exam Way:	Online proctored exam or onsite test center (Kryterion Webassessor)
Pre Condition:	No formal prerequisites required; recommended experience with data processing and Google Cloud Platform services
Official Syllabus URL:	https://cloud.google.com/learn/certification/data-engineer

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The Google Professional-Data-Engineer Exam consists of multiple-choice and multiple-select questions, and candidates are given two hours to complete it. Professional-Data-Engineer exam covers a range of topics, including designing data processing systems, building and maintaining data structures and databases, data analysis, machine learning, and data visualization.

Google Certified Professional Data Engineer Exam Sample Questions (Q19-Q24):

NEW QUESTION # 19
Your platform on your on-premises environment generates 100 GB of data daily, composed of millions of structured JSON text files. Your on-premises environment cannot be accessed from the public internet. You want to use Google Cloud products to query and explore the platform data. What should you do?

A. Use Transfer Service for on-premises data to copy data from your on-premises environment to Cloud Storage. Use the BigQuery Data Transfer Service to import data into BigQuery.
B. Use the BigQuery Data Transfer Service dataset copy to transfer all data into BigQuery.
C. Use a Transfer Appliance to copy data from your on-premises environment to Cloud Storage. Use the BigQuery Data Transfer Service to import data into BigQuery.
D. Use Cloud Scheduler to copy data daily from your on-premises environment to Cloud Storage.
Use the BigQuery Data Transfer Service to import data into BigQuery.

Answer: A

Explanation:
Your on-premises environment cannot be accessed from the public internet.
It signifies that we can apply private connection like Cloud Interconnect.
Reference:
https://cloud.google.com/network-connectivity/docs/interconnect/concepts/overview

NEW QUESTION # 20
You are troubleshooting your Dataflow pipeline that processes data from Cloud Storage to BigQuery. You have discovered that the Dataflow worker nodes cannot communicate with one another Your networking team relies on Google Cloud network tags to define firewall rules You need to identify the issue while following Google-recommended networking security practices. What should you do?

A. Determine whether your Dataflow pipeline is deployed with the external IP address option enabled.
B. Determine whether your Dataflow pipeline has a custom network tag set.
C. Determine whether there is a firewall rule set to allow traffic on TCP ports 12345 and 12346 on the subnet used by Dataflow workers.
D. Determine whether there is a firewall rule set to allow traffic on TCP ports 12345 and 12346 for the Dataflow network tag.

Answer: C

Explanation:
Dataflow worker nodes need to communicate with each other and with the Dataflow service on TCP ports
12345 and 12346. These ports are used for data shuffling and streaming engine communication. By default, Dataflow assigns a network tag called dataflow to the worker nodes, and creates a firewall rule that allows traffic on these ports for the dataflow network tag. However, if you use a custom network tag for your Dataflow pipeline, you need to create a firewall rule that allows traffic on these ports for your custom network tag. Otherwise, the worker nodes will not be able to communicate with each other and the Dataflow service, and the pipeline will fail.
Therefore, the best way to identify the issue is to determine whether there is a firewall rule set to allow traffic on TCP ports 12345 and 12346 for the Dataflow network tag. If there is no such firewall rule, or if the firewall rule does not match the network tag used by your Dataflow pipeline, you need to create or update the firewall rule accordingly.
Option A is not a good solution, as determining whether your Dataflow pipeline has a custom network tag set does not tell you whether there is a firewall rule that allows traffic on the required ports for that network tag.
You need to check the firewall rule as well.
Option C is not a good solution, as determining whether your Dataflow pipeline is deployed with the external IP address option enabled does not tell you whether there is a firewall rule that allows traffic on the required ports for the Dataflow network tag. The external IP address option determines whether the worker nodes can access resources on the public internet, but it does not affect the internal communication between the worker nodes and the Dataflow service.
Option D is not a good solution, as determining whether there is a firewall rule set to allow traffic on TCP ports 12345 and 12346 on the subnet used by Dataflow workers does not tell you whether the firewall rule applies to the Dataflow network tag. The firewall rule should be based on the network tag, not the subnet, as the network tag is more specific and secure. References: Dataflow network tags | Cloud Dataflow | Google Cloud, Dataflow firewall rules | Cloud Dataflow | Google Cloud, Dataflow network configuration | Cloud Dataflow | Google Cloud, Dataflow Streaming Engine | Cloud Dataflow | Google Cloud.

NEW QUESTION # 21
You want to analyze hundreds of thousands of social media posts daily at the lowest cost and with the fewest steps.
You have the following requirements:
- You will batch-load the posts once per day and run them through the
Cloud Natural Language API.
- You will extract topics and sentiment from the posts.
- You must store the raw posts for archiving and reprocessing.
- You will create dashboards to be shared with people both inside and
outside your organization.
You need to store both the data extracted from the API to perform analysis as well as the raw social media posts for historical archiving. What should you do?

A. Store the raw social media posts in Cloud Storage, and write the data extracted from the API into BigQuery.
B. Feed to social media posts into the API directly from the source, and write the extracted data from the API into BigQuery.
C. Store the social media posts and the data extracted from the API in BigQuery.
D. Store the social media posts and the data extracted from the API in Cloud SQL.

Answer: A

Explanation:
Social media posts can images/videos which cannot be stored in bigquery/

NEW QUESTION # 22
You work for a large fast food restaurant chain with over 400,000 employees. You store employee information in Google BigQuery in a Users table consisting of a FirstName field and a LastName field. A member of IT is building an application and asks you to modify the schema and data in BigQuery so the application can query a FullName field consisting of the value of the FirstName field concatenated with a space, followed by the value of the LastName field for each employee. How can you make that data available while minimizing cost?

A. Use BigQuery to export the data for the table to a CSV file. Create a Google Cloud Dataproc job to process the CSV file and output a new CSV file containing the proper values for FirstName, LastName and FullName. Run a BigQuery load job to load the new CSV file into BigQuery.
B. Create a view in BigQuery that concatenates the FirstName and LastName field values to produce the FullName.
C. Create a Google Cloud Dataflow job that queries BigQuery for the entire Users table, concatenates the FirstName value and LastName value for each user, and loads the proper values for FirstName, LastName, and FullName into a new table in BigQuery.
D. Add a new column called FullName to the Users table. Run an UPDATE statement that updates the FullName column for each user with the concatenation of the FirstName and LastName values.

Answer: B

NEW QUESTION # 23
What are two of the characteristics of using online prediction rather than batch prediction?

A. Predictions are returned in the response message.
B. It is optimized to handle a high volume of data instances in a job and to run more complex models.
C. It is optimized to minimize the latency of serving predictions.
D. Predictions are written to output files in a Cloud Storage location that you specify.

Answer: A,C

Explanation:
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

NEW QUESTION # 24
......

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