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Google Professional-Data-Engineer Exam Overview:

Certification Vendor:	Google Cloud
Exam Name:	Google Cloud Certified Professional Data Engineer
Exam Number:	Professional-Data-Engineer
Available Languages:	English, Japanese
Exam Duration:	120 minutes
Related Certifications:	Google Cloud Certified Professional Data Engineer
Passing Score:	Not officially published (estimated ~80%)
Real Exam Qty:	50-60
Exam Format:	Multiple-choice, Multiple-select
Exam Price:	$200 USD
Certificate Validity Period:	2 years
Sample Questions:	Google Professional-Data-Engineer Sample Questions
Exam Way:	Online (remote proctored) or at a testing center (Kryterion)
Pre Condition:	No mandatory prerequisites. Recommended: 3+ years of industry experience including 1+ years designing and managing solutions using Google Cloud.
Official Syllabus URL:	https://cloud.google.com/learn/certification/data-engineer

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To pass the Google Professional-Data-Engineer exam, candidates must have a solid understanding of data engineering concepts and techniques, as well as practical experience working with the Google Cloud Platform. They must be able to design and implement data processing systems that are secure, scalable, and efficient, and have the ability to troubleshoot and optimize these systems as needed. Professional-Data-Engineer Exam is challenging and comprehensive, but passing it can open up many career opportunities in data engineering, especially for those interested in working with Google Cloud Platform.

Google Certified Professional Data Engineer Exam Sample Questions (Q81-Q86):

NEW QUESTION # 81
Which SQL keyword can be used to reduce the number of columns processed by BigQuery?

A. LIMIT
B. WHERE
C. BETWEEN
D. SELECT

Answer: D

Explanation:
SELECT allows you to query specific columns rather than the whole table. LIMIT, BETWEEN, and WHERE clauses will not reduce the number of columns processed by BigQuery.
Reference: https://cloud.google.com/bigquery/launch-
checklist#architecture_design_and_development_checklist

NEW QUESTION # 82
You need to deploy additional dependencies to all of a Cloud Dataproc cluster at startup using an existing initialization action. Company security policies require that Cloud Dataproc nodes do not have access to the Internet so public initialization actions cannot fetch resources. What should you do?

A. Copy all dependencies to a Cloud Storage bucket within your VPC security perimeter
B. Use an SSH tunnel to give the Cloud Dataproc cluster access to the Internet
C. Deploy the Cloud SQL Proxy on the Cloud Dataproc master
D. Use Resource Manager to add the service account used by the Cloud Dataproc cluster to the Network User role

Answer: A

Explanation:
If you create a Dataproc cluster with internal IP addresses only, attempts to access the Internet in an initialization action will fail unless you have configured routes to direct the traffic through a NAT or a VPN gateway. Without access to the Internet, you can enable Private Google Access, and place job dependencies in Cloud Storage; cluster nodes can download the dependencies from Cloud Storage from internal IPs.
Reference:
https://cloud.google.com/dataproc/docs/concepts/configuring-clusters/init-actions

NEW QUESTION # 83
Your company is currently setting up data pipelines for their campaign. For all the Google Cloud Pub/Sub streaming data, one of the important business requirements is to be able to periodically identify the inputs and their timings during their campaign. Engineers have decided to use windowing and transformation in Google Cloud Dataflow for this purpose. However, when testing this feature, they find that the Cloud Dataflow job fails for the all streaming insert. What is the most likely cause of this problem?

A. They have not set the triggers to accommodate the data coming in late, which causes the job to fail
B. They have not assigned the timestamp, which causes the job to fail
C. They have not applied a global windowing function, which causes the job to fail when the pipeline is created
D. They have not applied a non-global windowing function, which causes the job to fail when the pipeline is created

Answer: D

NEW QUESTION # 84
Business owners at your company have given you a database of bank transactions. Each row contains the user ID, transaction type, transaction location, and transaction amount. They ask you to investigate what type of machine learning can be applied to the data. Which three machine learning applications can you use?
(Choose three.)

A. Reinforcement learning to predict the location of a transaction.
B. Unsupervised learning to predict the location of a transaction.
C. Supervised learning to predict the location of a transaction.
D. Unsupervised learning to determine which transactions are most likely to be fraudulent.
E. Clustering to divide the transactions into N categories based on feature similarity.
F. Supervised learning to determine which transactions are most likely to be fraudulent.

Answer: A,D,E

Explanation:
Explanation/Reference:

NEW QUESTION # 85
Government regulations in the banking industry mandate the protection of client's personally identifiable information (PII). Your company requires PII to be access controlled encrypted and compliant with major data protection standards In addition to using Cloud Data Loss Prevention (Cloud DIP) you want to follow Google-recommended practices and use service accounts to control access to PII. What should you do?

A. Use Cloud Storage to comply with major data protection standards. Use one service account shared by all users
B. Use Cloud Storage to comply with major data protection standards. Use multiple service accounts attached to IAM groups to grant the appropriate access to each group
C. Use one service account to access a Cloud SQL database and use separate service accounts for each human user
D. Assign the required identity and Access Management (IAM) roles to every employee, and create a single service account to access protect resources

Answer: B

NEW QUESTION # 86
......

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