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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
Available Languages:	French, German, Spanish, Portuguese, English, Japanese
Real Exam Qty:	Approximately 50–60 multiple choice and multiple select questions
Related Certifications:	Google Cloud Certified - Professional Machine Learning Engineer Google Cloud Certified - Professional Data Analytics Engineer Google Cloud Certified - Professional Cloud Architect Google Cloud Certified - Associate Cloud Engineer
Certificate Validity Period:	2 years
Passing Score:	Not publicly disclosed (scaled scoring system)
Exam Format:	multiple select, case study, multiple choice
Exam Price:	$200 USD
Exam Duration:	120 minutes
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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Professionals who pass the Google Professional-Data-Engineer: Google Certified Professional Data Engineer Exam are considered to be highly skilled data engineers who can solve complex data problems. They possess the skills to design, implement, and manage large-scale data processing systems and are capable of analyzing and interpreting data to make informed business decisions. Moreover, they have an in-depth understanding of cloud-based data processing systems and can leverage them to achieve business objectives.

Google Certified Professional Data Engineer Exam Sample Questions (Q298-Q303):

NEW QUESTION # 298
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: D

NEW QUESTION # 299
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 to TFRecords formats (appr. 200MB per file) instead of parquet files.
C. Increase the size of your parquet files to ensure them to be 1 GB minimum.
D. Switch from HDDs to SSDs, copy initial data from GCS to HDFS, run the Spark job and copy results back to GCS.

Answer: C

NEW QUESTION # 300
When running a pipeline that has a BigQuery source, on your local machine, you continue to get permission denied errors. What could be the reason for that?

A. Pipelines cannot be run locally
B. Your gcloud does not have access to the BigQuery resources
C. You are missing gcloud on your machine
D. BigQuery cannot be accessed from local machines

Answer: B

Explanation:
Explanation
When reading from a Dataflow source or writing to a Dataflow sink using DirectPipelineRunner, the Cloud Platform account that you configured with the gcloud executable will need access to the corresponding source/sink Reference:
https://cloud.google.com/dataflow/java-sdk/JavaDoc/com/google/cloud/dataflow/sdk/runners/DirectPipelineRun

NEW QUESTION # 301
Which of these numbers are adjusted by a neural network as it learns from a training dataset (select 2 answers)?

A. Continuous features
B. Biases
C. Weights
D. Input values

Answer: B,C

Explanation:
A neural network is a simple mechanism that's implemented with basic math. The only difference between the traditional programming model and a neural network is that you let the computer determine the parameters (weights and bias) by learning from training datasets.
Reference:
https://cloud.google.com/blog/big-data/2016/07/understanding-neural-networks-with-tensorflow-playground

NEW QUESTION # 302
Which of the following is NOT true about Dataflow pipelines?

A. Dataflow pipelines can consume data from other Google Cloud services
B. Dataflow pipelines can be programmed in Java
C. Dataflow pipelines are tied to Dataflow, and cannot be run on any other runner
D. Dataflow pipelines use a unified programming model, so can work both with streaming and batch data sources

Answer: C

Explanation:
Explanation
Dataflow pipelines can also run on alternate runtimes like Spark and Flink, as they are built using the Apache Beam SDKs Reference: https://cloud.google.com/dataflow/

NEW QUESTION # 303
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