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The Google Professional-Data-Engineer Exam comprises multiple-choice and scenario-based questions that test the candidate's knowledge and skills in different areas of data engineering. The test is designed to assess the candidate's ability to design and build data processing systems that meet specific business requirements. Furthermore, the exam evaluates the candidate's proficiency in data analysis, data visualization, and machine learning.
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Google Certified Professional Data Engineer Exam Sample Questions (Q344-Q349):
NEW QUESTION # 344
You are operating a Cloud Dataflow streaming pipeline. The pipeline aggregates events from a Cloud Pub/ Sub subscription source, within a window, and sinks the resulting aggregation to a Cloud Storage bucket.
The source has consistent throughput. You want to monitor an alert on behavior of the pipeline with Cloud Stackdriver to ensure that it is processing data. Which Stackdriver alerts should you create?
Answer: D
Explanation:
Increase in number of undelivered messages shows that the messages are not getting subscribed.
NEW QUESTION # 345
An aerospace company uses a proprietary data format to store its night data. You need to connect this new data source to BigQuery and stream the data into BigQuery. You want to efficiency import the data into BigQuery where consuming as few resources as possible. What should you do?
Answer: C
NEW QUESTION # 346
You're training a model to predict housing prices based on an available dataset with real estate properties.
Your plan is to train a fully connected neural net, and you've discovered that the dataset contains latitude and longtitude of the property. Real estate professionals have told you that the location of the property is highly influential on price, so you'd like to engineer a feature that incorporates this physical dependency.
What should you do?
Answer: B
Explanation:
Explanation
Explanation/Reference:
Reference https://cloud.google.com/bigquery/docs/gis-data
NEW QUESTION # 347
You are choosing a NoSQL database to handle telemetry data submitted from millions of Internet-of-Things (IoT) devices. The volume of data is growing at 100 TB per year, and each data entry has about 100 attributes.
The data processing pipeline does not require atomicity, consistency, isolation, and durability (ACID).
However, high availability and low latency are required.
You need to analyze the data by querying against individual fields. Which three databases meet your requirements? (Choose three.)
Answer: A,B,F
NEW QUESTION # 348
You are working on a sensitive project involving private user data. You have set up a project on Google Cloud Platform to house your work internally. An external consultant is going to assist with coding a complex transformation in a Google Cloud Dataflow pipeline for your project. How should you maintain users' privacy?
Answer: C
Explanation:
A service account is a special type of Google account intended to represent a non-human user that needs to authenticate and be authorized to access data in Google APIs.
https://cloud.google.com/iam/docs/understanding-service-accounts
NEW QUESTION # 349
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