Professional-Data-Engineer VCE dumps: Google Certified Professional Data Engineer Exam & Professional-Data-Engineer test prep

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To prepare for the Google Professional-Data-Engineer Certification Exam, candidates must have a solid understanding of data engineering fundamentals, as well as knowledge of the Google Cloud Platform and its associated services. Candidates can use a variety of study resources, such as official Google Cloud Platform documentation, online courses, and practice exams, to prepare for the exam. Additionally, candidates should have experience working with data processing systems, data warehousing, and data analysis tools.

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The Google Certified Professional Data Engineer Exam certification exam is divided into multiple sections, each of which covers a specific area of data engineering. Professional-Data-Engineer exam is scored on a scale of 1000, with a passing score of 700 or higher. Professional-Data-Engineer Exam is computer-based and can be taken at a testing center or online. The cost of the exam is $200, and it is valid for two years.

Google Certified Professional Data Engineer Exam Sample Questions (Q203-Q208):

NEW QUESTION # 203
You're training a model to predict housing prices based on an available dataset with real estate properties.
Your plan is to train a fully connected neural net, and you've discovered that the dataset contains latitude and longitude of the property. Real estate professionals have told you that the location of the property is highly influential on price, so you'd like to engineer a feature that incorporates this physical dependency.
What should you do?

A. Create a feature cross of latitude and longitude, bucketize at the minute level and use L1 regularization during optimization.
B. Provide latitude and longitude as input vectors to your neural net.
C. Create a feature cross of latitude and longitude, bucketize it at the minute level and use L2 regularization during optimization.
D. Create a numeric column from a feature cross of latitude and longitude.

Answer: D

Explanation:
Explanation/Reference:
Reference https://cloud.google.com/bigquery/docs/gis-data

NEW QUESTION # 204
As your organization expands its usage of GCP, many teams have started to create their own projects. Projects are further multiplied to accommodate different stages of deployments and target audiences. Each project requires unique access control configurations. The central IT team needs to have access to all projects. Furthermore, data from Cloud Storage buckets and BigQuery datasets must be shared for use in other projects in an ad hoc way. You want to simplify access control management by minimizing the number of policies. Which two steps should you take? Choose 2 answers.

A. Use Cloud Deployment Manager to automate access provision.
B. Introduce resource hierarchy to leverage access control policy inheritance.
C. Create distinct groups for various teams, and specify groups in Cloud IAM policies.
D. For each Cloud Storage bucket or BigQuery dataset, decide which projects need access. Find all the active members who have access to these projects, and create a Cloud IAM policy to grant access to all these users.
E. Only use service accounts when sharing data for Cloud Storage buckets and BigQuery datasets.

Answer: A,C

NEW QUESTION # 205
The _________ for Cloud Bigtable makes it possible to use Cloud Bigtable in a Cloud Dataflow pipeline.

A. BigQuery Data Transfer Service
B. BiqQuery API
C. DataFlow SDK
D. Cloud Dataflow connector

Answer: D

Explanation:
Explanation
The Cloud Dataflow connector for Cloud Bigtable makes it possible to use Cloud Bigtable in a Cloud Dataflow pipeline. You can use the connector for both batch and streaming operations.
Reference: https://cloud.google.com/bigtable/docs/dataflow-hbase

NEW QUESTION # 206
Which of the following is NOT one of the three main types of triggers that Dataflow supports?

A. Trigger based on element count
B. Trigger that is a combination of other triggers
C. Trigger based on element size in bytes
D. Trigger based on time

Answer: C

Explanation:
There are three major kinds of triggers that Dataflow supports: 1. Time-based triggers 2. Data-driven triggers. You can set a trigger to emit results from a window when that window has received a certain number of data elements. 3. Composite triggers. These triggers combine multiple time-based or data- driven triggers in some logical way Reference: https://cloud.google.com/dataflow/model/triggers

NEW QUESTION # 207
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 failover replica in another zone within the same region.
B. Create a Cloud SQL instance in one zone, and configure an external read replica in a zone in a different 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 create a read replica in another zone within the same region.

Answer: A

Explanation:
https://cloud.google.com/sql/docs/mysql/high-availability

NEW QUESTION # 208
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