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Google Professional-Data-Engineer certification exam consists of multiple choice and scenario-based questions. Professional-Data-Engineer exam covers a wide range of topics, including data processing architecture, data modeling, data ingestion, data transformation, data analysis, and data visualization. Professional-Data-Engineer exam also tests the candidate's ability to work with various Google Cloud Platform tools and services, such as Google Cloud Storage, Google BigQuery, and Google Cloud Dataflow. Candidates who pass the exam will receive the Google Certified Professional Data Engineer certification, which demonstrates their expertise in the field of data engineering and their ability to use Google Cloud Platform technologies to design and implement data processing systems.

To be eligible for the exam, candidates should have a minimum of three years of experience in data engineering, as well as a thorough understanding of the Google Cloud Platform. They should also have hands-on experience in designing and implementing data processing systems using various Google Cloud tools and services, such as BigQuery, Cloud Dataflow, Cloud Storage, and Cloud Pub/Sub.

Google Professional-Data-Engineer Certification Exam is a tough test that measures the competency of candidates in a variety of areas related to data engineering. Professional-Data-Engineer exam consists of multiple-choice questions that cover topics such as data processing, data storage, data analysis, data visualization, and machine learning. Professional-Data-Engineer exam also includes hands-on tasks that require candidates to demonstrate their ability to use Google Cloud Platform tools and services to solve real-world data engineering problems.

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Google Certified Professional Data Engineer Exam Sample Questions (Q105-Q110):

NEW QUESTION # 105
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. Use .fromQueryoperation to read specific fields from the table.
C. Specify the TableReferenceobject in the code.
D. Call a transform that returns TableRowobjects, where each element in the PCollectionrepresents a single row in the table.

Answer: D

NEW QUESTION # 106
Your company receives both batch- and stream-based event data. You want to process the data using
Google Cloud Dataflow over a predictable time period. However, you realize that in some instances data
can arrive late or out of order. How should you design your Cloud Dataflow pipeline to handle data that is
late or out of order?

A. Ensure every datasource type (stream or batch) has a timestamp, and use the timestamps to define
the logic for lagged data.
B. Set a single global window to capture all the data.
C. Use watermarks and timestamps to capture the lagged data.
D. Set sliding windows to capture all the lagged data.

Answer: D

NEW QUESTION # 107
Your financial services company is moving to cloud technology and wants to store 50 TB of financial time-series data in the cloud. This data is updated frequently and new data will be streaming in all the time. Your company also wants to move their existing Apache Hadoop jobs to the cloud to get insights into this data. Which product should they use to store the data?

A. Google Cloud Storage
B. Google Cloud Datastore
C. Google BigQuery
D. Cloud Bigtable

Answer: D

Explanation:
Explanation/Reference: https://cloud.google.com/bigtable/docs/schema-design-time-series

NEW QUESTION # 108
You decided to use Cloud Datastore to ingest vehicle telemetry data in real time. You want to build a storage system that will account for the long-term data growth, while keeping the costs low. You also want to create snapshots of the data periodically, so that you can make a point-in-time (PIT) recovery, or clone a copy of the data for Cloud Datastore in a different environment. You want to archive these snapshots for a long time.
Which two methods can accomplish this? Choose 2 answers.

A. Write an application that uses Cloud Datastore client libraries to read all the entities. Format the exported data into a JSON file. Apply compression before storing the data in Cloud Source Repositories.
B. Use managed export, and store the data in a Cloud Storage bucket using Nearline or Coldline class.
C. Use managed export, and then import the data into a BigQuery table created just for that export, and delete temporary export files.
D. Write an application that uses Cloud Datastore client libraries to read all the entities. Treat each entity as a BigQuery table row via BigQuery streaming insert. Assign an export timestamp for each export, and attach it as an extra column for each row. Make sure that the BigQuery table is partitioned using the export timestamp column.
E. Use managed exportm, and then import to Cloud Datastore in a separate project under a unique namespace reserved for that export.

Answer: A,C

NEW QUESTION # 109
You store historic data in Cloud Storage. You need to perform analytics on the historic dat
a. You want to use a solution to detect invalid data entries and perform data transformations that will not require programming or knowledge of SQL.
What should you do?

A. Use Cloud Dataproc with a Hadoop job to detect errors and perform transformations.
B. Use federated tables in BigQuery with queries to detect errors and perform transformations.
C. Use Cloud Dataflow with Beam to detect errors and perform transformations.
D. Use Cloud Dataprep with recipes to detect errors and perform transformations.

Answer: D

NEW QUESTION # 110
......

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