2025 NEW PROFESSIONAL-DATA-ENGINEER EXAM PATTERN 100% PASS | PROFESSIONAL PROFESSIONAL-DATA-ENGINEER: GOOGLE CERTIFIED PROFESSIONAL DATA ENGINEER EXAM 100% PASS

2025 New Professional-Data-Engineer Exam Pattern 100% Pass | Professional Professional-Data-Engineer: Google Certified Professional Data Engineer Exam 100% Pass

2025 New Professional-Data-Engineer Exam Pattern 100% Pass | Professional Professional-Data-Engineer: Google Certified Professional Data Engineer Exam 100% Pass

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Professional Data Engineer Exam Details

Like other Google exams, this exam also consists of multiple choice and multiple select questions. Consider the fact that you need to pay $200 for the registration. After that, you will access the test for 2 hours which is presented either in English or Japanese. Moreover, you can either take the exam online or have to find a test center near your place to take this test.

There is no formal prerequisite for the exam but it is recommended to have 3-4 years of experience within the data engineering field and to be responsible for the tasks related to data engineering and machine learning. So, on the final test day, you need to have exhaustive knowledge about these domains to perform your best.

  • Operationalizing machine learning models
  • Providing solution quality
  • Designing data processing systems
  • Building data processing systems

To become a Google Certified Professional Data Engineer, a candidate must pass the certification exam, which costs $200. Professional-Data-Engineer Exam is available in English, Japanese, and Spanish and can be taken online or at a testing center. Professional-Data-Engineer exam is valid for two years, after which a candidate must recertify to maintain their certification.

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Google Certified Professional Data Engineer Exam Exam Lab Questions & Professional-Data-Engineer valid VCE test & Google Certified Professional Data Engineer Exam Exam Simulator Online

To pass the Google Professional-Data-Engineer exam on the first try, candidates need Google Certified Professional Data Engineer Exam updated practice material. Preparing with real Professional-Data-Engineer exam questions is one of the finest strategies for cracking the exam in one go. Students who study with Google Professional-Data-Engineer Real Questions are more prepared for the exam, increasing their chances of succeeding.

Google Professional-Data-Engineer Certification Exam is designed to assess the skills and knowledge of candidates in various areas related to data engineering. Professional-Data-Engineer exam covers topics such as data processing architecture, data modeling, data ingestion, data transformation, and data storage. Candidates are also expected to have a strong understanding of Google Cloud technologies, including BigQuery, Cloud Storage, and Dataflow.

Google Certified Professional Data Engineer Exam Sample Questions (Q334-Q339):

NEW QUESTION # 334
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 . How should you create the ML pipeline?
user_features)

  • A. Add a WHERE clause to the query, and grant the BigQuery Data Viewer role to the application service account.
  • B. Create a Cloud Dataflow pipeline using BigQueryIO to read results from the query. Grant the Dataflow Worker 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 BigQueryIO to 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 # 335
You want to optimize your queries for cost and performance. How should you structure your data?

  • A. Cluster table data by create_date partition by locationed and device_version
  • B. Partition table data by create_date, location_id and device_version
  • C. Cluster table data by create_date location_id and device_version
  • D. Partition table data by create_date cluster table data by location_Id and device_version

Answer: D


NEW QUESTION # 336
You are designing a data warehouse in BigQuery to analyze sales data for a telecommunication service provider. You need to create a data model for customers, products, and subscriptions All customers, products, and subscriptions can be updated monthly, but you must maintain a historical record of all data. You plan to use the visualization layer for current and historical reporting. You need to ensure that the data model is simple, easy-to-use. and cost-effective. What should you do?

  • A. Create a denormalized, append-only model with nested and repeated fields Use the ingestion timestamp to track historical data.
  • B. Create a denormalized model with nested and repeated fields Update the table and use snapshots to track historical data
  • C. Create a normalized model with tables for each entity. Keep all input files in a Cloud Storage bucket to track historical data
  • D. Create a normalized model with tables for each entity. Use snapshots before updates to track historical data

Answer: A

Explanation:
- A denormalized, append-only model simplifies query complexity by eliminating the need for joins. - Adding data with an ingestion timestamp allows for easy retrieval of both current and historical states. - Instead of updating records, new records are appended, which maintains historical information without the need to create separate snapshots.


NEW QUESTION # 337
MJTelco Case Study
Company Overview
MJTelco is a startup that plans to build networks in rapidly growing, underserved markets around the world.
The company has patents for innovative optical communications hardware. Based on these patents, they can create many reliable, high-speed backbone links with inexpensive hardware.
Company Background
Founded by experienced telecom executives, MJTelco uses technologies originally developed to overcome communications challenges in space. Fundamental to their operation, they need to create a distributed data infrastructure that drives real-time analysis and incorporates machine learning to continuously optimize their topologies. Because their hardware is inexpensive, they plan to overdeploy the network allowing them to account for the impact of dynamic regional politics on location availability and cost.
Their management and operations teams are situated all around the globe creating many-to-many relationship between data consumers and provides in their system. After careful consideration, they decided public cloud is the perfect environment to support their needs.
Solution Concept
MJTelco is running a successful proof-of-concept (PoC) project in its labs. They have two primary needs:
* Scale and harden their PoC to support significantly more data flows generated when they ramp to more than 50,000 installations.
* Refine their machine-learning cycles to verify and improve the dynamic models they use to control topology definition.
MJTelco will also use three separate operating environments - development/test, staging, and production - to meet the needs of running experiments, deploying new features, and serving production customers.
Business Requirements
* Scale up their production environment with minimal cost, instantiating resources when and where needed in an unpredictable, distributed telecom user community.
* Ensure security of their proprietary data to protect their leading-edge machine learning and analysis.
* Provide reliable and timely access to data for analysis from distributed research workers
* Maintain isolated environments that support rapid iteration of their machine-learning models without affecting their customers.
Technical Requirements
* Ensure secure and efficient transport and storage of telemetry data
* Rapidly scale instances to support between 10,000 and 100,000 data providers with multiple flows each.
* Allow analysis and presentation against data tables tracking up to 2 years of data storing approximately
100m records/day
* Support rapid iteration of monitoring infrastructure focused on awareness of data pipeline problems both in telemetry flows and in production learning cycles.
CEO Statement
Our business model relies on our patents, analytics and dynamic machine learning. Our inexpensive hardware is organized to be highly reliable, which gives us cost advantages. We need to quickly stabilize our large distributed data pipelines to meet our reliability and capacity commitments.
CTO Statement
Our public cloud services must operate as advertised. We need resources that scale and keep our data secure. We also need environments in which our data scientists can carefully study and quickly adapt our models. Because we rely on automation to process our data, we also need our development and test environments to work as we iterate.
CFO Statement
The project is too large for us to maintain the hardware and software required for the data and analysis. Also, we cannot afford to staff an operations team to monitor so many data feeds, so we will rely on automation and infrastructure. Google Cloud's machine learning will allow our quantitative researchers to work on our high- value problems instead of problems with our data pipelines.
You create a new report for your large team in Google Data Studio 360. The report uses Google BigQuery as its data source. It is company policy to ensure employees can view only the data associated with their region, so you create and populate a table for each region. You need to enforce the regional access policy to the data.
Which two actions should you take? (Choose two.)

  • A. Ensure each table is included in a dataset for a region.
  • B. Adjust the settings for each table to allow a related region-based security group view access.
  • C. Adjust the settings for each dataset to allow a related region-based security group view access.
  • D. Adjust the settings for each view to allow a related region-based security group view access.
  • E. Ensure all the tables are included in global dataset.

Answer: A,D


NEW QUESTION # 338
You have important legal hold documents in a Cloud Storage bucket. You need to ensure that these documents are not deleted or modified. What should you do?

  • A. Set a retention policy. Lock the retention policy.
  • B. Set a retention policy. Set the default storage class to Archive for long-term digital preservation.
  • C. Enable the Object Versioning feature. Add a lifecycle rule.
  • D. Enable the Object Versioning feature. Create a copy in a bucket in a different region.

Answer: A

Explanation:
To ensure that important legal hold documents in a Cloud Storage bucket are not deleted or modified, the most effective method is to set and lock a retention policy. Here's why this is the best choice:
Retention Policy:
A retention policy defines a retention period during which objects in the bucket cannot be deleted or modified. This ensures data immutability.
Once a retention policy is set and locked, it cannot be removed or reduced, providing strong protection against accidental or malicious deletions.
Locking the Retention Policy:
Locking a retention policy ensures that the retention period cannot be changed. This action is permanent and guarantees that the specified retention period will be enforced.
Steps to Implement:
Set the Retention Policy:
Define a retention period for the bucket to ensure that all objects are protected for the required duration.
Lock the Retention Policy:
Lock the retention policy to prevent any modifications, ensuring the immutability of the documents.
Reference:
Cloud Storage Retention Policy Documentation
How to Set a Retention Policy


NEW QUESTION # 339
......

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