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| Certification Vendor: | Google Cloud |
|---|---|
| Exam Name: | Google Cloud Certified Professional Data Engineer Exam |
| Exam Number: | Professional Data Engineer |
| Real Exam Qty: | Approximately 50–60 multiple choice and multiple select questions |
| Certificate Validity Period: | 2 years |
| Available Languages: | Spanish, Portuguese, German, English, Japanese, French |
| Exam Price: | $200 USD |
| Passing Score: | Not publicly disclosed (scaled scoring system) |
| Exam Duration: | 120 minutes |
| Exam Format: | multiple choice, multiple select, case study |
| Related Certifications: | Google Cloud Certified - Associate Cloud Engineer Google Cloud Certified - Professional Machine Learning Engineer Google Cloud Certified - Professional Data Analytics Engineer Google Cloud Certified - Professional Cloud Architect |
| Recommended Training: | Google Cloud Data Engineering Learning Path Google Cloud Professional Data Engineer Course (Coursera) |
| 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 |
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Google Professional-Data-Engineer Certification Exam is one of the most sought-after certifications in the field of data engineering. Google Certified Professional Data Engineer Exam certification is ideal for individuals who are looking to take their career to the next level and want to demonstrate their expertise in cloud-based data engineering. By earning this certification, individuals can prove to employers that they have the necessary skills and knowledge to design and implement data processing systems using Google Cloud Platform technologies.
NEW QUESTION # 373
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?
Answer: C
Explanation:
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.
NEW QUESTION # 374
Your organization stores employee information in a BigQuery dataset. Your human resources (HR) admin team requires full access to the data, but the HR analyst team needs to conduct salary analysis without being able to access personally identifiable information (PII). You want to ensure that users have the correct level of access for their role managed through Dataplex, while reducing data duplication. What should you do?
Answer: D
Explanation:
Comprehensive and Detailed Explanation:
The requirements are role-based access, masking specific columns (PII) for one role, and avoiding data duplication.
Option B is the correct answer because it directly addresses all requirements using Google Cloud's modern data governance tools. You use Data Catalog (governed by Dataplex) to create a taxonomy and policy tags (e.
g., a "PII" tag). You apply this tag to the sensitive columns in your BigQuery table. Then, using IAM, you can grant the HR Analyst role access to the table but apply a data masking policy to the "PII" tag for that role.
When an analyst queries the table, BigQuery dynamically masks the tagged columns for them at query time.
The HR Admin role is granted full access without the masking policy. This provides fine-grained security without creating any copies of the data.
Option A is a valid but older approach. An authorized view could work, but it requires manually maintaining the view's SQL definition. Policy tags are more scalable and manageable, especially as the number of tables and policies grows.
Options C and D are incorrect because they both involve creating a new, separate dataset with masked data.
This explicitly violates the requirement to "reduce data duplication."
Reference (Google Cloud Documentation Concepts):
This is the primary use case for column-level security in BigQuery. The official documentation shows how to
"Restrict access with BigQuery column-level security" by creating a taxonomy and policy tags in Data Catalog. You then grant IAM permissions on these policy tags (roles/datacatalog.categoryFineGrainedReader) to different groups of users. For users who should see masked data, you do not grant them this role on the sensitive tags, and a data masking rule (e.g., nullification, hashing) can be applied. This entire governance framework can be managed centrally via Dataplex.
NEW QUESTION # 375
When you design a Google Cloud Bigtable schema it is recommended that you _________.
Answer: C
Explanation:
All operations are atomic at the row level. For example, if you update two rows in a table, it's possible that one row will be updated successfully and the other update will fail. Avoid schema designs that require atomicity across rows.
Reference:
https://cloud.google.com/bigtable/docs/schema-design#row-keys
NEW QUESTION # 376
Your company has recently grown rapidly and now ingesting data at a significantly higher rate than it was previously. You manage the daily batch MapReduce analytics jobs in Apache Hadoop. However, the recent increase in data has meant the batch jobs are falling behind. You were asked to recommend ways the development team could increase the responsiveness of the analytics without increasing costs. What should you recommend they do?
Answer: A
Explanation:
Spark performs in-memory processing and faster, which results in optimization of job's processing time.
NEW QUESTION # 377
When you store data in Cloud Bigtable, what is the recommended minimum amount of stored data?
Answer: A
Explanation:
Cloud Bigtable is not a relational database. It does not support SQL queries, joins, or multi- row transactions. It is not a good solution for less than 1 TB of data.
Reference:
https://cloud.google.com/bigtable/docs/overview#title_short_and_other_storage_options
NEW QUESTION # 378
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