Latest Professional-Data-Engineer Learning Material, Valid Professional-Data-Engineer Test Cram

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Passing the Google Professional-Data-Engineer Certification Exam is a significant achievement that can lead to multiple career opportunities. It can help individuals establish themselves as experts in the field of data engineering and demonstrate their ability to design, implement, and manage complex data processing systems. Moreover, it can also increase their credibility and earning potential, as the demand for certified data engineers is on the rise. Therefore, if you are looking to establish a career in data engineering, obtaining the Google Professional-Data-Engineer Certification is a must.

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Latest Professional-Data-Engineer Learning Material | 100% Free Valid Google Certified Professional Data Engineer Exam Test Cram


To get success in the Google Professional-Data-Engineer exam is not an easy task, it is quite difficult to pass it. But with proper planning, firm commitment, and ExamDiscuss Professional-Data-Engineer Questions, you can pass this milestone easily. ExamDiscuss is a leading platform that offers real, valid, and updated Google Professional-Data-Engineer Exam Dumps. With the ExamDiscuss Google Certified Professional Data Engineer Exam (Professional-Data-Engineer) Questions you can easily prepare well for the final Google Professional-Data-Engineer exam and crack it easily.

Google Professional-Data-Engineer Certification Exam is designed for professionals seeking to demonstrate their expertise in designing, building, and managing data processing systems on the Google Cloud Platform. Professional-Data-Engineer exam is intended for data engineers, data architects, and data analysts who work with big data solutions. Google Certified Professional Data Engineer Exam certification validates the skills and knowledge required to design and build data processing systems, as well as manage and monitor them in a production environment.

Google Certified Professional Data Engineer Exam Sample Questions (Q27-Q32):


NEW QUESTION # 27
You are building a teal-lime prediction engine that streams files, which may contain Pll (personal identifiable information) data, into Cloud Storage and eventually into BigQuery You want to ensure that the sensitive data is masked but still maintains referential Integrity, because names and emails are often used as join keys How should you use the Cloud Data Loss Prevention API (DLP API) to ensure that the Pll data is not accessible by unauthorized individuals?

  • A. Scan every table in BigQuery, and mask the data it finds that has Pll

  • B. Redact all Pll data, and store a version of the unredacted data in a locked-down bucket

  • C. Create a pseudonym by replacing Pll data with a cryptographic format-preserving token

  • D. Create a pseudonym by replacing the Pll data with cryptogenic tokens, and store the non-tokenized data in a locked-down button.


Answer: D

 

NEW QUESTION # 28
MJTelco's Google Cloud Dataflow pipeline is now ready to start receiving data from the 50,000 installations. You want to allow Cloud Dataflow to scale its compute power up as required. Which Cloud Dataflow pipeline configuration setting should you update?

  • A. The maximum number of workers

  • B. The zone

  • C. The disk size per worker

  • D. The number of workers


Answer: B

 

NEW QUESTION # 29
You are designing the database schema for a machine learning-based food ordering service that will predict what users want to eat. Here is some of the information you need to store:
The user profile: What the user likes and doesn't like to eat
The user account information: Name, address, preferred meal times
The order information: When orders are made, from where, to whom
The database will be used to store all the transactional data of the product. You want to optimize the data schem
a. Which Google Cloud Platform product should you use?

  • A. Cloud Bigtable

  • B. Cloud SQL

  • C. Cloud Datastore

  • D. BigQuery


Answer: D

 

NEW QUESTION # 30
Which SQL keyword can be used to reduce the number of columns processed by BigQuery?

  • A. BETWEEN

  • B. LIMIT

  • C. SELECT

  • D. WHERE


Answer: C

Explanation:
SELECT allows you to query specific columns rather than the whole table.
LIMIT, BETWEEN, and WHERE clauses will not reduce the number of columns processed by
BigQuery.

 

NEW QUESTION # 31
You designed a database for patient records as a pilot project to cover a few hundred patients in three clinics.
Your design used a single database table to represent all patients and their visits, and you used self-joins to generate reports. The server resource utilization was at 50%. Since then, the scope of the project has expanded.
The database must now store 100 times more patient records. You can no longer run the reports, because they either take too long or they encounter errors with insufficient compute resources. How should you adjust the database design?

  • A. Normalize the master patient-record table into the patient table and the visits table, and create other necessary tables to avoid self-join.

  • B. Shard the tables into smaller ones based on date ranges, and only generate reports with prespecified date ranges.

  • C. Partition the table into smaller tables, with one for each clinic. Run queries against the smaller table pairs, and use unions for consolidated reports.

  • D. Add capacity (memory and disk space) to the database server by the order of 200.


Answer: B

 

NEW QUESTION # 32
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