PROFESSIONAL-DATA-ENGINEER SURE PASS & NEW PROFESSIONAL-DATA-ENGINEER EXAM FORMAT

Professional-Data-Engineer Sure Pass & New Professional-Data-Engineer Exam Format

Professional-Data-Engineer Sure Pass & New Professional-Data-Engineer Exam Format

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CertkingdomPDF Professional-Data-Engineer Questions have helped thousands of candidates to achieve their professional dreams. Our Google Certified Professional Data Engineer Exam (Professional-Data-Engineer) exam dumps are useful for preparation and a complete source of knowledge. If you are a full-time job holder and facing problems finding time to prepare for the Google Professional-Data-Engineer Exam Questions, you shouldn't worry more about it.

Google Professional-Data-Engineer Exam is a rigorous test of an individual's skills and knowledge in data engineering on Google Cloud technologies. As demand for skilled data engineering professionals continues to grow, the certification can open up many lucrative job opportunities for those looking to make their mark in the industry. Google Certified Professional Data Engineer Exam certification process requires practical experience, extensive preparation, and dedication to acquiring skills that are highly valued in today's rapidly evolving technology ecosystem.

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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.

Google Certified Professional Data Engineer Exam Sample Questions (Q317-Q322):

NEW QUESTION # 317
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. 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. Use Cloud Deployment Manager to automate access provision.
  • D. Only use service accounts when sharing data for Cloud Storage buckets and BigQuery datasets.
  • E. Create distinct groups for various teams, and specify groups in Cloud IAM policies.

Answer: C,E


NEW QUESTION # 318
You are loading CSV files from Cloud Storage to BigQuery. The files have known data quality issues, including mismatched data types, such as STRINGS and INT64s in the same column, and inconsistent formatting of values such as phone numbers or addresses. You need to create the data pipeline to maintain data quality and perform the required cleansing and transformation. What should you do?

  • A. Create a table with the desired schema, toad the CSV files into the table, and perform the transformations in place using SQL.
  • B. Load the CSV files into a staging table with the desired schema, perform the transformations with SQL. and then write the results to the final destination table.
  • C. Use Data Fusion to transform the data before loading it into BigQuery.
  • D. Use Data Fusion to convert the CSV files lo a self-describing data formal, such as AVRO. before loading the data to BigOuery.

Answer: C

Explanation:
Data Fusion's advantages:
Visual interface: Offers a user-friendly interface for designing data pipelines without extensive coding, making it accessible to a wider range of users.
Built-in transformations: Includes a wide range of pre-built transformations to handle common data quality issues, such as:
Data type conversions
Data cleansing (e.g., removing invalid characters, correcting formatting) Data validation (e.g., checking for missing values, enforcing constraints) Data enrichment (e.g., adding derived fields, joining with other datasets) Custom transformations: Allows for custom transformations using SQL or Java code for more complex cleaning tasks.
Scalability: Can handle large datasets efficiently, making it suitable for processing CSV files with potential data quality issues.
Integration with BigQuery: Integrates seamlessly with BigQuery, allowing for direct loading of transformed data.


NEW QUESTION # 319
Your company maintains a hybrid deployment with GCP, where analytics are performed on your
anonymized customer data. The data are imported to Cloud Storage from your data center through parallel
uploads to a data transfer server running on GCP. Management informs you that the daily transfers take
too long and have asked you to fix the problem. You want to maximize transfer speeds. Which action
should you take?

  • A. Increase the CPU size on your server.
  • B. Increase the size of the Google Persistent Disk on your server.
  • C. Increase your network bandwidth from Compute Engine to Cloud Storage.
  • D. Increase your network bandwidth from your datacenter to GCP.

Answer: D

Explanation:
Explanation/Reference:


NEW QUESTION # 320
Your team is working on a binary classification problem. You have trained a support vector machine (SVM) classifier with default parameters, and received an area under the Curve (AUC) of 0.87 on the validation set.
You want to increase the AUC of the model. What should you do?

  • A. Scale predictions you get out of the model (tune a scaling factor as a hyperparameter) in order to get the highest AUC
  • B. Perform hyperparameter tuning
  • C. Deploy the model and measure the real-world AUC; it's always higher because of generalization
  • D. Train a classifier with deep neural networks, because neural networks would always beat SVMs

Answer: B

Explanation:
https://towardsdatascience.com/understanding-hyperparameters-and-its-optimisation-techniques-f0debba07568


NEW QUESTION # 321
Your company is in a highly regulated industry. One of your requirements is to ensure individual users have access only to the minimum amount of information required to do their jobs. You want to enforce this requirement with Google BigQuery. Which three approaches can you take? (Choose three.)

  • A. Restrict access to tables by role.
  • B. Ensure that the data is encrypted at all times.
  • C. Restrict BigQuery API access to approved users.
  • D. Disable writes to certain tables.
  • E. Use Google Stackdriver Audit Logging to determine policy violations.
  • F. Segregate data across multiple tables or databases.

Answer: A,C,E


NEW QUESTION # 322
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New Professional-Data-Engineer Exam Format: https://www.certkingdompdf.com/Professional-Data-Engineer-latest-certkingdom-dumps.html

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