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The Google Professional-Data-Engineer exam tests the candidate's proficiency in working with various data processing tools, including Google Cloud Platform technologies such as BigQuery, Dataflow, and Cloud Storage. Professional-Data-Engineer exam also covers topics such as data modeling, data ingestion, data transformation, and data analysis. Additionally, the exam tests the candidate's understanding of best practices for data engineering, including performance optimization, security, and compliance.

Google Professional-Data-Engineer certification exam consists of multiple-choice questions and performance-based tasks that simulate real-world scenarios. Professional-Data-Engineer Exam is divided into four main domains: Designing data processing systems, building and operationalizing data processing systems, integrating data sources, and managing data processing infrastructure.

Google Professional-Data-Engineer certification is highly valued in the industry. It demonstrates that the holder has the skills and knowledge to design and implement data solutions on Google Cloud Platform. Google Certified Professional Data Engineer Exam certification is especially relevant for those looking to work with Big Data, as Google Cloud Platform is one of the leading providers of Big Data solutions.

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Google Certified Professional Data Engineer Exam Sample Questions (Q27-Q32):

NEW QUESTION # 27
Your company is performing data preprocessing for a learning algorithm in Google Cloud Dataflow.
Numerous data logs are being are being generated during this step, and the team wants to analyze them.
Due to the dynamic nature of the campaign, the data is growing exponentially every hour.
The data scientists have written the following code to read the data for a new key features in the logs.
BigQueryIO.Read
.named("ReadLogData")
.from("clouddataflow-readonly:samples.log_data")
You want to improve the performance of this data read. What should you do?

A. Use of both the Google BigQuery TableSchemaand TableFieldSchemaclasses.
B. Specify the TableReferenceobject in the code.
C. Use .fromQueryoperation to read specific fields from the table.
D. Call a transform that returns TableRowobjects, where each element in the PCollectionrepresents a single row in the table.

Answer: D

NEW QUESTION # 28
You are collecting loT sensor data from millions of devices across the world and storing the data in BigQuery. Your access pattern is based on recent data tittered by location_id and device_version with the following query:

You want to optimize your queries for cost and performance. How should you structure your data?

A. Partition table data by create_date, location_id and device_version
B. Cluster table data by create_date, partition by location and device_version
C. Partition table data by create_date cluster table data by tocation_id and device_version
D. Cluster table data by create_date location_id and device_version

Answer: D

NEW QUESTION # 29
You are working on a niche product in the image recognition domain. Your team has developed a model that is dominated by custom C++ TensorFlow ops your team has implemented. These ops are used inside your main training loop and are performing bulky matrix multiplications. It currently takes up to several days to train a model. You want to decrease this time significantly and keep the cost low by using an accelerator on Google Cloud. What should you do?

A. Stay on CPUs, and increase the size of the cluster you're training your model on.
B. Use Cloud GPUs after implementing GPU kernel support for your customs ops.
C. Use Cloud TPUs after implementing GPU kernel support for your customs ops.
D. Use Cloud TPUs without any additional adjustment to your code.

Answer: C

Explanation:
Cloud TPUs are not suited to the following workloads: [...] Neural network workloads that contain custom TensorFlow operations written in C++. Specifically, custom operations in the body of the main training loop are not suitable for TPUs.

NEW QUESTION # 30
The Dataflow SDKs have been recently transitioned into which Apache service?

A. Apache Beam
B. Apache Kafka
C. Apache Spark
D. Apache Hadoop

Answer: A

Explanation:
Explanation
Dataflow SDKs are being transitioned to Apache Beam, as per the latest Google directive Reference: https://cloud.google.com/dataflow/docs/

NEW QUESTION # 31
For the best possible performance, what is the recommended zone for your Compute Engine instance and Cloud Bigtable instance?

A. Have the Compute Engine instance in the furthest zone from the Cloud Bigtable instance.
B. Have both the Compute Engine instance and the Cloud Bigtable instance to be in different zones.
C. Have both the Compute Engine instance and the Cloud Bigtable instance to be in the same zone.
D. Have the Cloud Bigtable instance to be in the same zone as all of the consumers of your data.

Answer: C

Explanation:
It is recommended to create your Compute Engine instance in the same zone as your Cloud Bigtable instance for the best possible performance, If it's not possible to create a instance in the same zone, you should create your instance in another zone within the same region. For example, if your Cloud Bigtable instance is located in us-central1-b, you could create your instance in us-central1-f. This change may result in several milliseconds of additional latency for each Cloud Bigtable request.
It is recommended to avoid creating your Compute Engine instance in a different region from your Cloud Bigtable instance, which can add hundreds of milliseconds of latency to each Cloud Bigtable request.
Reference: https://cloud.google.com/bigtable/docs/creating-compute-instance

NEW QUESTION # 32
......

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