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Google Professional-Data-Engineer Exam Overview:

Certification Vendor:	Google Cloud
Exam Name:	Google Cloud Professional Data Engineer Exam
Exam Number:	Professional-Data-Engineer
Related Certifications:	Google Cloud Associate Cloud Engineer Google Cloud Professional Cloud Architect Google Cloud Professional Data Analyst
Certificate Validity Period:	2 years
Passing Score:	700 / 1000
Exam Price:	USD 200 (plus tax where applicable)
Exam Format:	Multiple choice, Multiple select
Exam Duration:	120 minutes
Available Languages:	Japanese, English
Real Exam Qty:	40 - 50
Recommended Training:	Google Cloud Professional Data Engineer Learning Path Official Exam Guide
Exam Registration:	Google Cloud Certification Registration
Sample Questions:	Google Professional-Data-Engineer Sample Questions
Exam Way:	Online-proctored or onsite-proctored
Pre Condition:	No mandatory prerequisites; recommended 3+ years industry experience, including 1+ year designing and managing Google Cloud data solutions
Official Syllabus URL:	https://cloud.google.com/learn/certification/data-engineer

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Google Professional-Data-Engineer Certification is a highly respected credential that validates the knowledge and skills of professionals working in the field of data engineering. Google Certified Professional Data Engineer Exam certification is designed to test the ability of candidates to design, build, operate, and manage data processing systems that are scalable, secure, and reliable. Google Certified Professional Data Engineer Exam certification exam is conducted by Google and is intended for individuals who have a good understanding of the Google Cloud Platform and data engineering best practices.

Google Certified Professional Data Engineer Exam Sample Questions (Q130-Q135):

NEW QUESTION # 130
You are choosing a NoSQL database to handle telemetry data submitted from millions of Internet-of-Things (IoT) devices. The volume of data is growing at 100 TB per year, and each data entry has about 100 attributes.
The data processing pipeline does not require atomicity, consistency, isolation, and durability (ACID).
However, high availability and low latency are required.
You need to analyze the data by querying against individual fields. Which three databases meet your requirements? (Choose three.)

A. Redis
B. MongoDB
C. Cassandra
D. HDFS with Hive
E. MySQL
F. HBase

Answer: B,D,F

NEW QUESTION # 131
You plan to deploy Cloud SQL using MySQL. You need to ensure high availability in the event of a zone failure. What should you do?

A. Create a Cloud SQL instance in one zone, and create a read replica in another zone within the same region.
B. Create a Cloud SQL instance in one zone, and create a failover replica in another zone within the same region.
C. Create a Cloud SQL instance in a region, and configure automatic backup to a Cloud Storage bucket in the same region.
D. Create a Cloud SQL instance in one zone, and configure an external read replica in a zone in a different region.

Answer: D

NEW QUESTION # 132
You migrated a data backend for an application that serves 10 PB of historical product data for analytics.
Only the last known state for a product, which is about 10 GB of data, needs to be served through an API to the other applications. You need to choose a cost-effective persistent storage solution that can accommodate the analytics requirements and the API performance of up to 1000 queries per second (QPS) with less than 1 second latency. What should you do?

A. 1. Store the historical data in BigQuery for analytics.
2. In a Cloud SQL table, store the last state of the product after every product change.
3. Serve the last state data directly from Cloud SQL to the API.
B. 1. Store the products as a collection in Firestore with each product having a set of historical changes.
2. Use simple and compound queries for analytics.
3. Serve the last state data directly from Firestore to the API.
C. 1. Store the historical data in BigQuery for analytics.
2. Use a materialized view to precompute the last state of a product.
3. Serve the last state data directly from BigQuery to the API.
D. 1. Store the historical data in Cloud SQL for analytics.
2. In a separate table, store the last state of the product after every product change.
3. Serve the last state data directly from Cloud SQL to the API.

Answer: C

NEW QUESTION # 133
Your analytics team wants to build a simple statistical model to determine which customers are most likely to work with your company again, based on a few different metrics. They want to run the model on Apache Spark, using data housed in Google Cloud Storage, and you have recommended using Google Cloud Dataproc to execute this job. Testing has shown that this workload can run in approximately 30 minutes on a 15-node cluster, outputting the results into Google BigQuery. The plan is to run this workload weekly.
How should you optimize the cluster for cost?

A. Use pre-emptible virtual machines (VMs) for the cluster
B. Use SSDs on the worker nodes so that the job can run faster
C. Use a higher-memory node so that the job runs faster
D. Migrate the workload to Google Cloud Dataflow

Answer: A

Explanation:
https://cloud.google.com/dataproc/docs/concepts/compute/preemptible-vms

NEW QUESTION # 134
What is the recommended action to do in order to switch between SSD and HDD storage for your Google Cloud Bigtable instance?

A. run parallel instances where one is HDD and the other is SDD
B. the selection is final and you must resume using the same storage type
C. create a third instance and sync the data from the two storage types via batch jobs
D. export the data from the existing instance and import the data into a new instance

Answer: D

Explanation:
Explanation
When you create a Cloud Bigtable instance and cluster, your choice of SSD or HDD storage for the cluster is permanent. You cannot use the Google Cloud Platform Console to change the type of storage that is used for the cluster.
If you need to convert an existing HDD cluster to SSD, or vice-versa, you can export the data from the existing instance and import the data into a new instance. Alternatively, you can write a Cloud Dataflow or Hadoop MapReduce job that copies the data from one instance to another.
Reference: https://cloud.google.com/bigtable/docs/choosing-ssd-hdd-

NEW QUESTION # 135
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