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Google Professional-Data-Engineer Exam Overview:

Certification Vendor:Google Cloud
Exam Name:Google Cloud Certified Professional Data Engineer
Exam Number:Professional-Data-Engineer
Available Languages:English, Japanese
Passing Score:Not officially published (estimated ~80%)
Related Certifications:Google Cloud Certified Professional Data Engineer
Certificate Validity Period:2 years
Exam Format:Multiple-select, Multiple-choice
Real Exam Qty:50-60
Exam Duration:120 minutes
Exam Price:$200 USD
Sample Questions:Google Professional-Data-Engineer Sample Questions
Exam Way:Online (remote proctored) or at a testing center (Kryterion)
Pre Condition:No mandatory prerequisites. Recommended: 3+ years of industry experience including 1+ years designing and managing solutions using Google Cloud.
Official Syllabus URL:https://cloud.google.com/learn/certification/data-engineer

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Google Professional-Data-Engineer: Google Certified Professional Data Engineer Exam is an essential certification exam for professionals looking to advance their careers in the field of data engineering. Passing Professional-Data-Engineer Exam validates a candidate's expertise in designing, building, and managing data processing systems. It also demonstrates their ability to analyze and interpret data, make informed business decisions, and leverage cloud-based data processing systems to achieve business objectives.

Google Certified Professional Data Engineer Exam Sample Questions (Q428-Q433):

NEW QUESTION # 428
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?

Answer: B

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 # 429
You are using BigQuery's ML.GENERATE_TEXT function to write marketing materials for a new product launch. The problem is, the AI is generating random text that does not always relate to the product. You want the simplest way to improve the output to generate a marketing copy. What should you do?

Answer: D

Explanation:
The temperature parameter is the primary control for randomness and "creativity" in Large Language Models (LLMs) used within BigQuery ML.
* Randomness vs. Focus: A high temperature (e.g., closer to 1.0) leads to more random, diverse, and sometimes irrelevant output because the model is more likely to choose lower-probability tokens. If the AI is generating "random text," lowering the temperature (e.g., to 0.2 or 0.1) makes the model more deterministic and focused on the most likely next tokens related to the input prompt.
* Simplest Way: Adjusting a single parameter in a SQL function is the "simplest" approach compared to gathering datasets for few-shot prompting (A) or the high complexity and cost of fine-tuning (C).
* temperature vs. top_p: While top_p (nucleus sampling) also affects randomness, temperature is the standard first-line control for the overall "entropy" of the model's responses. Official Google documentation often suggests adjusting temperature first to curb hallucination or excessive randomness.
Reference: Google Cloud Documentation on ML.GENERATE_TEXT:
"temperature: A value in the range [0.0, 1.0]... It controls the degree of randomness in token selection.
Lower temperature values are good for prompts that require a more deterministic and less open-ended response, while higher temperature values can lead to more diverse or creative results." (Source: ML.
GENERATE_TEXT arguments)
"To get more predictable responses from the model, use a lower temperature." (Source: BigQuery ML generative AI overview)


NEW QUESTION # 430
You are running a streaming pipeline with Dataflow and are using hopping windows to group the data as the data arrives. You noticed that some data is arriving late but is not being marked as late data, which is resulting in inaccurate aggregations downstream. You need to find a solution that allows you to capture the late data in the appropriate window. What should you do?

Answer: A


NEW QUESTION # 431
You create an important report for your large team in Google Data Studio 360. The report uses Google BigQuery as its data source. You notice that visualizations are not showing data that is less than 1 hour old. What should you do?

Answer: C


NEW QUESTION # 432
You are managing a Cloud Dataproc cluster. You need to make a job run faster while minimizing costs, without losing work in progress on your clusters. What should you do?

Answer: C

Explanation:
Reference https://cloud.google.com/dataproc/docs/concepts/configuring-clusters/flex


NEW QUESTION # 433
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