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| Certification Vendor: | Google Cloud |
|---|---|
| Exam Name: | Google Cloud Certified Professional Data Engineer Exam |
| Exam Number: | Professional Data Engineer |
| Available Languages: | German, Japanese, Portuguese, Spanish, French, English |
| Exam Duration: | 120 minutes |
| Passing Score: | Not publicly disclosed (scaled scoring system) |
| Real Exam Qty: | Approximately 50–60 multiple choice and multiple select questions |
| Exam Format: | multiple choice, case study, multiple select |
| Certificate Validity Period: | 2 years |
| Related Certifications: | Google Cloud Certified - Professional Machine Learning Engineer Google Cloud Certified - Professional Cloud Architect Google Cloud Certified - Professional Data Analytics Engineer Google Cloud Certified - Associate Cloud Engineer |
| Exam Price: | $200 USD |
| Recommended Training: | Google Cloud Professional Data Engineer Course (Coursera) Google Cloud Data Engineering Learning Path |
| Exam Registration: | Google Cloud Certification Registration |
| Sample Questions: | Google Professional-Data-Engineer Sample Questions |
| Exam Way: | Online proctored exam or onsite test center (Kryterion Webassessor) |
| Pre Condition: | No formal prerequisites required; recommended experience with data processing and Google Cloud Platform services |
| Official Syllabus URL: | https://cloud.google.com/learn/certification/data-engineer |
>> Professional-Data-Engineer最新考古題 <<
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要參加Google Professional-Data-Engineer考試，考生需要深入了解數據處理技術，如Hadoop、Spark和其他大數據框架。他們還應該熟練掌握Python、Java或Go等編程語言，並具有設計和開發數據處理管道的經驗。此外，考生還應該有實際操作Google Cloud平台服務，如BigQuery、Dataflow和Dataproc的經驗。通過Google Professional-Data-Engineer考試，可證明數據專業人員在Google Cloud上管理數據解決方案的專業知識和能力，進而有助於他們的職業發展。
問題 #203
Which of these numbers are adjusted by a neural network as it learns from a training dataset (select 2 answers)?
答案：C,D
解題說明：
A neural network is a simple mechanism that's implemented with basic math. The only difference between the traditional programming model and a neural network is that you let the computer determine the parameters (weights and bias) by learning from training datasets.
Reference: https://cloud.google.com/blog/big-data/2016/07/understanding-neural-networks-with-tensorflow-playground
問題 #204
You have thousands of Apache Spark jobs running in your on-premises Apache Hadoop cluster. You want to migrate the jobs to Google Cloud. You want to use managed services to run your jobs instead of maintaining a long-lived Hadoop cluster yourself. You have a tight timeline and want to keep code changes to a minimum. What should you do?
答案：D
解題說明：
Dataproc's Compatibility with Apache Spark: Dataproc is a managed service for running Hadoop and Spark clusters on Google Cloud. This means it is designed to seamlessly run Apache Spark jobs with minimal code changes. Your existing Spark jobs should run on Dataproc with little to no modification.
Cloud Storage as a Scalable Data Lake: Cloud Storage provides a highly scalable and durable storage solution for your data. It's designed to handle large volumes of data that Spark jobs typically process.
Minimizing Operational Overhead: By using Dataproc, you eliminate the need to manage and maintain a Hadoop cluster yourself. Google Cloud handles the infrastructure, allowing you to focus on your data processing tasks.
Tight Timeline and Minimal Code Changes: This option directly addresses the requirements of the question. It offers a quick and easy way to migrate your Spark jobs to Google Cloud with minimal disruption to your existing codebase.
Why other options are not suitable:
A . Copy your data to Compute Engine disks. Manage and run your jobs directly on those instances: This option requires you to manage the underlying infrastructure yourself, which contradicts the requirement of using managed services.
C . Move your data to BigQuery. Convert your Spark scripts to a SQL-based processing approach: While BigQuery is a powerful data warehouse, converting Spark scripts to SQL would require substantial code changes and might not be feasible within a tight timeline.
D . Rewrite your jobs in Apache Beam. Run your jobs in Dataflow: Rewriting jobs in Apache Beam would be a significant undertaking and not suitable for a quick migration with minimal code changes.
問題 #205
You are using BigQuery and Data Studio to design a customer-facing dashboard that displays large quantities of aggregated dat
a. You expect a high volume of concurrent users. You need to optimize tie dashboard to provide quick visualizations with minimal latency. What should you do?
答案：B
問題 #206
Flowlogistic is rolling out their real-time inventory tracking system. The tracking devices will all send package-tracking messages, which will now go to a single Google Cloud Pub/Sub topic instead of the Apache Kafka cluster. A subscriber application will then process the messages for real-time reporting and store them in Google BigQuery for historical analysis. You want to ensure the package data can be analyzed over time.
Which approach should you take?
答案：B
解題說明：
Topic 2, 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.
問題 #207
Suppose you have a table that includes a nested column called "city" inside a column called
"person", but when you try to submit the following query in BigQuery, it gives you an error.
SELECT person FROM `project1.example.table1` WHERE city = "London"
How would you correct the error?
答案：D
解題說明：
To access the person.city column, you need to "UNNEST(person)" and JOIN it to table1 using a comma.
Reference:
https://cloud.google.com/bigquery/docs/reference/standard-sql/migrating-from-legacy-sql#nested_repeated_results
問題 #208
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2026 VCESoft最新的Professional-Data-Engineer PDF版考試題庫和Professional-Data-Engineer考試問題和答案免費分享：https://drive.google.com/open?id=1NR8pfVvp6yaAf8_OjfdYsorwEVqnD7vS