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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:	Japanese, English
Exam Duration:	120 minutes
Exam Format:	Multiple-choice, Multiple-select
Real Exam Qty:	50-60
Exam Price:	$200 USD
Passing Score:	Not officially published (estimated ~80%)
Certificate Validity Period:	2 years
Related Certifications:	Google Cloud Certified Professional Data Engineer
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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Who should take the Google Professional Data Engineer exam

Individuals should pursue the exam if they want to demonstrate their expertise and ability to design and develop Data Engineering. Following professional get benefited from Google Professional Data Engineer Certification

Business analysts
Data scientists
Developers responsible for managing big data transformation initiatives
Data architects
Data analysts

Google Professional-Data-Engineer Certification Exam covers a broad range of topics, including data processing systems, data modeling, data analysis, data visualization, and machine learning. It requires a strong understanding of Google Cloud Platform products and services, such as BigQuery, Dataflow, Dataproc, and Pub/Sub. Professional-Data-Engineer exam also tests the ability to design and implement solutions that are scalable, efficient, and secure.

Google Certified Professional Data Engineer Exam Sample Questions (Q274-Q279):

NEW QUESTION # 274
Your company uses a proprietary system to send inventory data every 6 hours to a data ingestion service in the cloud. Transmitted data includes a payload of several fields and the timestamp of the transmission. If there are any concerns about a transmission, the system re-transmits the data. How should you deduplicate the data most efficiency?

A. Compute the hash value of each data entry, and compare it with all historical data.
B. Assign global unique identifiers (GUID) to each data entry.
C. Maintain a database table to store the hash value and other metadata for each data entry.
D. Store each data entry as the primary key in a separate database and apply an index.

Answer: C

Explanation:
Using Hash values we can remove duplicate values from a database. Hashvalues will be same for duplicate data and thus can be easily rejected.

NEW QUESTION # 275
You are designing a Dataflow pipeline for a batch processing job. You want to mitigate multiple zonal failures at job submission time. What should you do?

A. Submit duplicate pipelines in two different zones by using the -zone flag.
B. Specify a worker region by using the -region flag.
C. Set the pipeline staging location as a regional Cloud Storage bucket.
D. Create an Eventarc trigger to resubmit the job in case of zonal failure when submitting the job.

Answer: C

Explanation:
By specifying a worker region, you can run your Dataflow pipeline in a multi-zone or multi-region configuration, which provides higher availability and resilience in case ofzonal failures1. The -region flag allows you to specify the regional endpoint for your pipeline, which determines the location of the Dataflow service and the default location of the Compute Engine resources1. If you do not specify a zone by using the
-zone flag, Dataflow automatically selects a zone within the region for your job workers1. This option is recommended over submitting duplicate pipelines in two different zones, which would incur additional costs and complexity. Setting the pipeline staging location as a regional Cloud Storage bucket does not affect the availability of your pipeline, as the staging location only stores the pipeline code and dependencies2. Creating an Eventarc trigger to resubmit the job in case of zonal failure is not a reliable solution, as it depends on the availability of the Eventarc service and the zonal resources at the time of resubmission. References:
* 1: Pipeline troubleshooting and debugging | Cloud Dataflow | Google Cloud
* 3: Regional endpoints | Cloud Dataflow | Google Cloud

NEW QUESTION # 276
You want to rebuild your batch pipeline for structured data on Google Cloud. You are using PySpark to conduct data transformations at scale, but your pipelines are taking over twelve hours to run. To expedite development and pipeline run time, you want to use a serverless tool and SQL syntax. You have already moved your raw data into Cloud Storage. How should you build the pipeline on Google Cloud while meeting speed and processing requirements?

A. Ingest your data into Cloud SQL, convert your PySpark commands into SparkSQL queries to transform the data, and then use federated queries from BigQuery for machine learning.
B. Ingest your data into BigQuery from Cloud Storage, convert your PySpark commands into BigQuery SQL queries to transform the data, and then write the transformations to a new table
C. Use Apache Beam Python SDK to build the transformation pipelines, and write the data into BigQuery
D. Convert your PySpark commands into SparkSQL queries to transform the data; and then run your pipelineb on Dataproc to write the data into BigQuery

Answer: B

Explanation:
Dataproc is diffrent than Dataproc Serveless. This question is talking about dataproc.
By the way dp serverless support both pyspark and sparkSql no need of conversion.

NEW QUESTION # 277
Your company is implementing a data warehouse using BigQuery, and you have been tasked with designing the data model You move your on-premises sales data warehouse with a star data schema to BigQuery but notice performance issues when querying the data of the past 30 days Based on Google's recommended practices, what should you do to speed up the query without increasing storage costs?

A. Shard the data by customer ID
B. Partition the data by transaction date
C. Denormalize the data
D. Materialize the dimensional data in views

Answer: D

NEW QUESTION # 278
You are creating a new pipeline in Google Cloud to stream IoT data from Cloud Pub/Sub through Cloud Dataflow to BigQuery. While previewing the data, you notice that roughly 2% of the data appears to be corrupt.
You need to modify the Cloud Dataflow pipeline to filter out this corrupt data. What should you do?

A. Add a ParDo transform in Cloud Dataflow to discard corrupt elements.
B. Add a Partition transform in Cloud Dataflow to separate valid data from corrupt data.
C. Add a GroupByKey transform in Cloud Dataflow to group all of the valid data together and discard the rest.
D. Add a SideInput that returns a Boolean if the element is corrupt.

Answer: A

Explanation:
Explanation

NEW QUESTION # 279
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