Professional Data Engineer Exam (Professional-Data-Engineer) - Google Cloud

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Sample Professional-Data-Engineer Exam Questions & Explanations

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Question #1 Verified Q&A
Your company built a TensorFlow neural-network model with a large number of neurons and layers. The model fits well for the training dat
A Serialization
B Dropout Methods
C Threading
D Dimensionality Reduction
Correct Answer: Dropout Methods
Explanation:
Reference: https://medium.com/mlreview/a-simple-deep-learning-model-for-stock-price-prediction- using-tensorflow-30505541d877
Question #2 Verified Q&A
You are building a model to make clothing recommendations. You know a user’s fashion preference is likely to change over time, so you build a data pipeline to stream new data back to the model as it becomes available. How should you use this data to train the model?
A Continuously retrain the model on a combination of existing data and the new data.
B Train on the existing data while using the new data as your test set.
C Continuously retrain the model on just the new data.
D Train on the new data while using the existing data as your test set.
Correct Answer: Train on the existing data while using the new data as your test set.
Explanation:
https://cloud.google.com/automl-tables/docs/prepare
Question #3 Verified Q&A
You designed a database for patient records as a pilot project to cover a few hundred patients in three clinics. Your design used a single database table to represent all patients and their visits, and<br />
you used self-joins to generate reports. The server resource utilization was at 50%. Since then, the<br />
scope of the project has expanded. The database must now store 100 times more patient records. You can no longer run the reports, because they either take too long or they encounter errors with insufficient compute resources. How should you adjust the database design?
A Shard the tables into smaller ones based on date ranges, and only generate reports with prespecified date ranges.
B Normalize the master patient-record table into the patient table and the visits table, and create other necessary tables to avoid self-join.
C Add capacity (memory and disk space) to the database server by the order of 200.
D Partition the table into smaller tables, with one for each clinic. Run queries against the smaller table pairs, and use unions for consolidated reports.
Correct Answer: Normalize the master patient-record table into the patient table and the visits table, and create other necessary tables to avoid self-join.
Question #4 Verified Q&A
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?
A Disable caching in BigQuery by editing table details.
B Refresh your browser tab showing the visualizations.
C Disable caching by editing the report settings.
D Clear your browser history for the past hour then reload the tab showing the virtualizations.
Correct Answer: Disable caching by editing the report settings.
Explanation:
Reference: https://support.google.com/datastudio/answer/7020039?hl=en
Question #5 Verified Q&A
An external customer provides you with a daily dump of data from their database. The data flows into Google Cloud Storage GCS as comma-separated values (CSV) files. You want to analyze this data in Google BigQuery, but the data could have rows that are formatted incorrectly or corrupted. How should you build this pipeline?
A Enable BigQuery monitoring in Google Stackdriver and create an alert.
B Import the data into BigQuery using the gcloud CLI and set max_bad_records to 0.
C Use federated data sources, and check data in the SQL query.
D Run a Google Cloud Dataflow batch pipeline to import the data into BigQuery, and push errors to another dead-letter table for analysis.
Correct Answer: Run a Google Cloud Dataflow batch pipeline to import the data into BigQuery, and push errors to another dead-letter table for analysis.

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Our Professional-Data-Engineer (Professional Data Engineer Exam) practice question bank currently includes 384 verified questions with comprehensive explanations.
Are the Professional-Data-Engineer exam practice questions up to date for 2026?
Yes, all Google Cloud Professional-Data-Engineer practice questions are regularly updated and verified against current exam objectives.
Will the Professional-Data-Engineer practice questions help me pass on my first attempt?
The Professional-Data-Engineer (Professional Data Engineer Exam) question bank is built to mirror the real exam's format and topics, with detailed explanations for every answer so you understand the reasoning — not just memorize responses.
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