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Certification Path

The Google Professional Data Engineer Certification is one of the highest level of certification mainly focussing to the professional Data Engineering.

There is no prerequisite for this exam but still it would be best to follow some sequence in order to prove immense knowledge as a Google professional Data Engineer.

You can complete Google Associate Certifications then approach for the professional certification. For more information related to Google cloud certification track Google-certification-path

To pass the Google Professional-Data-Engineer exam, candidates must have a solid understanding of data engineering concepts and techniques, as well as practical experience working with the Google Cloud Platform. They must be able to design and implement data processing systems that are secure, scalable, and efficient, and have the ability to troubleshoot and optimize these systems as needed. Professional-Data-Engineer Exam is challenging and comprehensive, but passing it can open up many career opportunities in data engineering, especially for those interested in working with Google Cloud Platform.

Google Professional-Data-Engineer certification is highly respected in the industry, and it can open up new career opportunities for individuals who hold it. Google Cloud Platform is one of the leading cloud computing platforms, and companies across different industries are increasingly adopting it. Professionals who are certified in Google Cloud Platform technologies are in high demand, and they can earn competitive salaries. Therefore, passing the Professional-Data-Engineer exam is a worthwhile investment for individuals who want to advance their careers in the field of data engineering.

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Google Certified Professional Data Engineer Exam Sample Questions (Q24-Q29):

NEW QUESTION # 24
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 two.)

A. Introduce resource hierarchy to leverage access control policy inheritance.
B. 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.
C. Only use service accounts when sharing data for Cloud Storage buckets and BigQuery datasets.
D. Create distinct groups for various teams, and specify groups in Cloud IAM policies.
E. Use Cloud Deployment Manager to automate access provision.

Answer: A,D

Explanation:
Google suggests that we should provide access by following google hierarchy and groups for users with similar roles.

NEW QUESTION # 25
You are developing an application on Google Cloud that will automatically generate subject labels for users' blog posts. You are under competitive pressure to add this feature quickly, and you have no additional developer resources. No one on your team has experience with machine learning. What should you do?

A. Call the Cloud Natural Language API from your application. Process the generated Entity Analysis as labels.
B. Build and train a text classification model using TensorFlow. Deploy the model using a Kubernetes Engine cluster. Call the model from your application and process the results as labels.
C. Call the Cloud Natural Language API from your application. Process the generated Sentiment Analysis as labels.
D. Build and train a text classification model using TensorFlow. Deploy the model using Cloud Machine Learning Engine. Call the model from your application and process the results as labels.

Answer: A

NEW QUESTION # 26
You are creating a new pipeline in Google Cloud to stream IoT data from Cloud Pub/Sub through Cloud Dataflow to BigQuery. While previewing the data, you notice that roughly 2% of the data appears to be corrupt.
You need to modify the Cloud Dataflow pipeline to filter out this corrupt data. What should you do?

A. Add a ParDo transform in Cloud Dataflow to discard corrupt elements.
B. Add a GroupByKey transform in Cloud Dataflow to group all of the valid data together and discard the rest.
C. Add a SideInput that returns a Boolean if the element is corrupt.
D. Add a Partition transform in Cloud Dataflow to separate valid data from corrupt data.

Answer: A

NEW QUESTION # 27
You have a data pipeline with a Cloud Dataflow job that aggregates and writes time series metrics to Cloud Bigtable. This data feeds a dashboard used by thousands of users across the organization. You need to support additional concurrent users and reduce the amount of time required to write the data. Which two actions should you take? (Choose two.)

A. Modify your Cloud Dataflow pipeline to use the CoGroupByKeytransform before writing to Cloud Bigtable
B. Configure your Cloud Dataflow pipeline to use local execution
C. Increase the number of nodes in the Cloud Bigtable cluster
D. Increase the maximum number of Cloud Dataflow workers by setting maxNumWorkersin PipelineOptions
E. Modify your Cloud Dataflow pipeline to use the Flattentransform before writing to Cloud Bigtable

Answer: A,E

Explanation:
Explanation/Reference: https://www.slideshare.net/LucasArruda3/how-to-build-an-etl-pipeline-with-apache-beam-on- google-cloud-dataflow

NEW QUESTION # 28
You are building a model to make clothing recommendations. You know a user's fashion preference is likely to change over time, so you build a data pipeline to stream new data back to the model as it becomes available.
How should you use this data to train the model?

A. Train on the existing data while using the new data as your test set.
B. Continuously retrain the model on a combination of existing data and the new data.
C. Train on the new data while using the existing data as your test set.
D. Continuously retrain the model on just the new data.

Answer: A

Explanation:
Explanation
https://cloud.google.com/automl-tables/docs/prepare

NEW QUESTION # 29
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