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Who should take the Google Professional Data Engineer exam

Individuals should pursue the exam if they want to demonstrate their expertise and ability to design and develop Data Engineering. Following professional get benefited from Google Professional Data Engineer Certification

Data scientists
Business analysts
Data architects
Data analysts

Google Professional-Data-Engineer Certification Exam is designed for professionals who want to demonstrate their skills in designing and building data processing systems on the Google Cloud Platform. Google Certified Professional Data Engineer Exam certification is ideal for data engineers, data analysts, and database administrators who work with big data and want to enhance their skills. Professional-Data-Engineer Exam covers a broad range of topics, including data processing, data storage, data analysis, and machine learning.

Google Certified Professional Data Engineer Exam Sample Questions (Q177-Q182):

NEW QUESTION # 177
Your organization has been collecting and analyzing data in Google BigQuery for 6 months. The majority of the data analyzed is placed in a time-partitioned table named events_partitioned. To reduce the cost of queries, your organization created a view called events, which queries only the last 14 days of dat
a. The view is described in legacy SQL. Next month, existing applications will be connecting to BigQuery to read the events data via an ODBC connection. You need to ensure the applications can connect. Which two actions should you take? (Choose two.)

A. Create a new partitioned table using a standard SQL query
B. Create a Google Cloud Identity and Access Management (Cloud IAM) role for the ODBC connection and shared "events"
C. Create a new view over events using standard SQL
D. Create a service account for the ODBC connection to use for authentication
E. Create a new view over events_partitioned using standard SQL

Answer: B,C

NEW QUESTION # 178
When running a pipeline that has a BigQuery source, on your local machine, you continue to get permission denied errors. What could be the reason for that?

A. Pipelines cannot be run locally
B. Your gcloud does not have access to the BigQuery resources
C. BigQuery cannot be accessed from local machines
D. You are missing gcloud on your machine

Answer: B

Explanation:
When reading from a Dataflow source or writing to a Dataflow sink using DirectPipelineRunner, the Cloud Platform account that you configured with the gcloud executable will need access to the corresponding source
/sink
Reference: https://cloud.google.com/dataflow/java-sdk/JavaDoc/com/google/cloud/dataflow/sdk/runners
/DirectPipelineRunner

NEW QUESTION # 179
The YARN ResourceManager and the HDFS NameNode interfaces are available on a Cloud Dataproc cluster
____.

A. master node
B. conditional node
C. application node
D. worker node

Answer: A

Explanation:
Explanation
The YARN ResourceManager and the HDFS NameNode interfaces are available on a Cloud Dataproc cluster master node. The cluster master-host-name is the name of your Cloud Dataproc cluster followed by an -m suffix-for example, if your cluster is named "my-cluster", the master-host-name would be "my-cluster-m".
Reference: https://cloud.google.com/dataproc/docs/concepts/cluster-web-interfaces#interfaces

NEW QUESTION # 180
You are working on a sensitive project involving private user dat
a. You have set up a project on Google Cloud Platform to house your work internally. An external consultant is going to assist with coding a complex transformation in a Google Cloud Dataflow pipeline for your project. How should you maintain users' privacy?

A. Grant the consultant the Cloud Dataflow Developer role on the project.
B. Create an anonymized sample of the data for the consultant to work with in a different project.
C. Create a service account and allow the consultant to log on with it.
D. Grant the consultant the Viewer role on the project.

Answer: C

NEW QUESTION # 181
A data scientist has created a BigQuery ML model and asks you to create an ML pipeline to serve predictions.
You have a REST API application with the requirement to serve predictions for an individual user ID with latency under 100 milliseconds. You use the following query to generate predictions: SELECT predicted_label, user_id FROM ML.PREDICT (MODEL 'dataset.model', table user_features). How should you create the ML pipeline?

A. Create a Cloud Dataflow pipeline using BigQueryIOto read results from the query. Grant the Dataflow Worker role to the application service account.
B. Add a WHERE clause to the query, and grant the BigQuery Data Viewer role to the application service account.
C. Create an Authorized View with the provided query. Share the dataset that contains the view with the application service account.
D. Create a Cloud Dataflow pipeline using BigQueryIOto read predictions for all users from the query. Write the results to Cloud Bigtable using BigtableIO. Grant the Bigtable Reader role to the application service account so that the application can read predictions for individual users from Cloud Bigtable.

Answer: D

NEW QUESTION # 182
......

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