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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
Passing Score:	Not officially published (estimated ~80%)
Exam Duration:	120 minutes
Available Languages:	Japanese, English
Exam Price:	$200 USD
Related Certifications:	Google Cloud Certified Professional Data Engineer
Certificate Validity Period:	2 years
Real Exam Qty:	50-60
Exam Format:	Multiple-select, Multiple-choice
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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Google Professional-Data-Engineer Certification has become a must-have for data engineering professionals who work with Google Cloud. Google Certified Professional Data Engineer Exam certification validates their knowledge and skills in designing and building data processing systems, as well as their ability to analyze and use machine learning models. Google Certified Professional Data Engineer Exam certification also helps professionals to stand out in a competitive job market and advance their careers.

Google Certified Professional Data Engineer Exam Sample Questions (Q125-Q130):

NEW QUESTION # 125
An online brokerage company requires a high volume trade processing architecture. You need to create a secure queuing system that triggers jobs. The jobs will run in Google Cloud and cat thecompany's Python API to execute trades. You need to efficiently implement a solution. What should you do?

A. Write an application that makes a queue in a NoSQL database
B. Use Cloud Composer to subscribe to a Pub/Sub tope and can the Python API.
C. Use a Pub/Sub push subscription to trigger a Cloud Function to pass the data to tie Python API.
D. Write an application hosted on a Compute Engine instance that makes a push subscription to the Pub
/Sub topic

Answer: A

NEW QUESTION # 126
You are migrating your data warehouse to BigQuery. You have migrated all of your data into tables in a dataset. Multiple users from your organization will be using the dat
a. They should only see certain tables based on their team membership. How should you set user permissions?

A. Create authorized views for each team in the same dataset in which the data resides, and assign the users/groups data viewer access to the authorized views
B. Create authorized views for each team in datasets created for each team. Assign the authorized views data viewer access to the dataset in which the data resides. Assign the users/groups data viewer access to the datasets in which the authorized views reside
C. Create SQL views for each team in the same dataset in which the data resides, and assign the users/groups data viewer access to the SQL views
D. Assign the users/groups data viewer access at the table level for each table

Answer: D

NEW QUESTION # 127
You want to build a managed Hadoop system as your data lake. The data transformation process is composed of a series of Hadoop jobs executed in sequence. To accomplish the design of separating storage from compute, you decided to use the Cloud Storage connector to store all input data, output data, and intermediary data. However, you noticed that one Hadoop job runs very slowly with Cloud Dataproc, when compared with the on-premises bare-metal Hadoop environment (8-core nodes with 100-GB RAM). Analysis shows that this particular Hadoop job is disk I/O intensive. You want to resolve the issue. What should you do?

A. Allocate more CPU cores of the virtual machine instances of the Hadoop cluster so that the networking bandwidth for each instance can scale up
B. Allocate sufficient memory to the Hadoop cluster, so that the intermediary data of that particular Hadoop job can be held in memory
C. Allocate additional network interface card (NIC), and configure link aggregation in the operating system to use the combined throughput when working with Cloud Storage
D. Allocate sufficient persistent disk space to the Hadoop cluster, and store the intermediate data of that particular Hadoop job on native Hadoop Distributed File System (HDFS)

Answer: D

Explanation:
Its google recommended approach to use LocalDisk/HDFS to store Intermediate result and use Cloud Storage for initial and final results.

NEW QUESTION # 128
Your team is building a data lake platform on Google Cloud. As a part of the data foundation design, you are planning to store all the raw data in Cloud Storage You are expecting to ingest approximately 25 GB of data a day and your billing department is worried about the increasing cost of storing old data. The current business requirements are:
* The old data can be deleted anytime
* You plan to use the visualization layer for current and historical reporting
* The old data should be available instantly when accessed
* There should not be any charges for data retrieval.
What should you do to optimize for cost?

A. Create an Object Lifecycle Management policy to modify the storage class for data older than 30 days to nearline, 90 days to coldline. and 365 days to archive storage class. Delete old data as needed.
B. Create an Object Lifecycle Management policy to modify the storage class for data older than 30 days to nearlme. 45 days to coldline. and 60 days to archive storage class Delete old data as needed.
C. Create the bucket with the Autoclass storage class feature.
D. Create an Object Lifecycle Management policy to modify the storage class for data older than 30 days to coldline, 90 days to nearline. and 365 days to archive storage class Delete old data as needed.

Answer: C

Explanation:
- Autoclass automatically moves objects between storage classes without impacting performance or availability, nor incurring retrieval costs. - It continuously optimizes storage costs based on access patterns without the need to set specific lifecycle management policies.

NEW QUESTION # 129
You work for an economic consulting firm that helps companies identify economic trends as they happen. As part of your analysis, you use Google BigQuery to correlate customer data with the average prices of the 100 most common goods sold, including bread, gasoline, milk, and others. The average prices of these goods are updated every 30 minutes. You want to make sure this data stays up to date so you can combine it with other data in BigQuery as cheaply as possible. What should you do?

A. Load the data every 30 minutes into a new partitioned table in BigQuery.
B. Store and update the data in a regional Google Cloud Storage bucket and create a federated data source in BigQuery
C. Store the data in Google Cloud Datastore. Use Google Cloud Dataflow to query BigQuery and combine the data programmatically with the data stored in Cloud Datastore
D. Store the data in a file in a regional Google Cloud Storage bucket. Use Cloud Dataflow to query BigQuery and combine the data programmatically with the data stored in Google Cloud Storage.

Answer: A

NEW QUESTION # 130
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