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Professional-Data-Engineer Latest Exam Discount | Latest Professional-Data-Engineer Exam Test

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Google Professional Data Engineer Practice Test Questions, Google Professional Data Engineer Exam Practice Test Questions

The Google Professional Data Engineer certification is designed to evaluate the candidates’ skills in designing data processing systems and ensuring solution quality. It is also created to measure their competence in building and operationalizing data processing systems and operationalizing ML models. The potential applicants must complete a single exam to get certified.

The Google Professional-Data-Engineer Exam is designed for data engineers, data analysts, and data scientists who work with data processing systems and data analytics solutions on the Google Cloud Platform. Google Certified Professional Data Engineer Exam certification demonstrates their ability to design, build, and manage scalable, reliable, and cost-effective data solutions on Google Cloud.

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Certification has become a prerequisite for employment and career growth in the Google industry for reputable companies. To advance comfortably in your career, passing the Professional-Data-Engineer exam is a valuable validation of your expertise. However, many test takers struggle to find updated Google Certified Professional Data Engineer Exam (Professional-Data-Engineer) dumps and fail to prepare effectively in a short period, resulting in a loss of time, money, and motivation.

Professional Data Engineer Exam Details

Like other Google exams, this exam also consists of multiple choice and multiple select questions. Consider the fact that you need to pay $200 for the registration. After that, you will access the test for 2 hours which is presented either in English or Japanese. Moreover, you can either take the exam online or have to find a test center near your place to take this test.

There is no formal prerequisite for the exam but it is recommended to have 3-4 years of experience within the data engineering field and to be responsible for the tasks related to data engineering and machine learning. So, on the final test day, you need to have exhaustive knowledge about these domains to perform your best.

Operationalizing machine learning models
Designing data processing systems
Building data processing systems
Providing solution quality

Google Certified Professional Data Engineer Exam Sample Questions (Q163-Q168):

NEW QUESTION # 163
You are designing a data processing pipeline. The pipeline must be able to scale automatically as load increases. Messages must be processed at least once, and must be ordered within windows of 1 hour. How should you design the solution?

A. Use Cloud Pub/Sub for message ingestion and Cloud Dataflow for streaming analysis.
B. Use Cloud Pub/Sub for message ingestion and Cloud Dataproc for streaming analysis.
C. Use Apache Kafka for message ingestion and use Cloud Dataflow for streaming analysis.
D. Use Apache Kafka for message ingestion and use Cloud Dataproc for streaming analysis.

Answer: A

NEW QUESTION # 164
When you design a Google Cloud Bigtable schema it is recommended that you
_________.

A. Create schema designs that require atomicity across rows
B. Avoid schema designs that are based on NoSQL concepts
C. Avoid schema designs that require atomicity across rows
D. Create schema designs that are based on a relational database design

Answer: C

Explanation:
All operations are atomic at the row level. For example, if you update two rows in a table, it's possible that one row will be updated successfully and the other update will fail. Avoid schema designs that require atomicity across rows.
Reference: https://cloud.google.com/bigtable/docs/schema-design#row-keys

NEW QUESTION # 165
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. Specify the TableReferenceobject in the code.
B. Call a transform that returns TableRowobjects, where each element in the PCollectionrepresents a single row in the table.
C. Use of both the Google BigQuery TableSchemaand TableFieldSchemaclasses.
D. Use .fromQueryoperation to read specific fields from the table.

Answer: B

NEW QUESTION # 166
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

NEW QUESTION # 167
You are administering a BigQuery dataset that uses a customer-managed encryption key (CMEK). You need to share the dataset with a partner organization that does not have access to your CMEK. What should you do?

A. Export the tables to parquet files to a Cloud Storage bucket and grant the storageinsights. viewer role on the bucket to the partner organization.
B. Create an authorized view that contains the CMEK to decrypt the data when accessed.
C. Copy the tables you need to share to a dataset without CMEKs Create an Analytics Hub listing for this dataset.
D. Provide the partner organization a copy of your CMEKs to decrypt the data.

Answer: C

Explanation:
If you want to share a BigQuery dataset that uses a customer-managed encryption key (CMEK) with a partner organization that does not have access to your CMEK, you cannot use an authorized view or provide them a copy of your CMEK, because these options would violate the security and privacy of your data. Instead, you can copy the tables you need to share to a dataset without CMEKs, and then create an Analytics Hub listing for this dataset. Analytics Hub is a service that allows you to securely share and discover data assets across your organization and with external partners. By creating an Analytics Hub listing, you can grant the partner organization access to the copied dataset without CMEKs, and also control the level of access and the duration of the sharing. Reference:
Customer-managed Cloud KMS keys
[Authorized views]
[Analytics Hub overview]
[Creating an Analytics Hub listing]

NEW QUESTION # 168
......

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