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GCP-PDE 試験練習問題

Google Professional Data Engineer

GCP-PDE の PDF 練習問題。資料の言語は 英語です。購入はこの試験のみで、自動更新はありません。

試験概要

GCP-PDE は「Google Professional Data Engineer」の認定試験です。試験形式、時間、対応言語は以下をご確認ください。最新の出題範囲や受験条件は公式サイトをご参照ください。

試験コード
GCP-PDE
公式の試験・資格名
Google Professional Data Engineer
試験形式
40–50 single-answer and multiple-select questions (standard exam)
試験時間
120 分
受験可能な言語
英語、日本語

公式試験の対応言語と練習問題ファイルの言語は異なります。試験の版と言語は公式の予約ページでご確認ください。

公式情報： 公式サイト
情報確認日：2026-10-20

対応言語

  • 英語
  • 簡体字中国語
購入前にお試し

最初の 20 問を無料プレビュー

全 349 問

題庫の元の順序で表示します。回答欄を開くと、正解と収録されている解説を確認できます。

問題の言語：英語。問題文・選択肢・正解・解説は元の題庫の言語で表示します。

免責事項：ExamPico の問題はすべてインターネット上の情報を整理したもので、学習参考用として提供しています。

問題 1

Question#1Topic1

Your company built a TensorFlow neutral-network model with a large number of neurons and layers. The model fits well for the training data. However, when tested against new data, it performs poorly. What method can you employ to address this?

  1. A. Threading
  2. B. Serialization
  3. C. Dropout Methods
  4. D. Dimensionality Reduction
正解と解説を表示

正解

C

解説

Reference:
https://medium.com/mlreview/a-simple-deep-learning-model-for-stock-price-prediction-using-tensorflow-30505541d877
Question illustration

問題 2

Question#2Topic1

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?

  1. A. Continuously retrain the model on just the new data.
  2. B. Continuously retrain the model on a combination of existing data and the new data.
  3. C. Train on the existing data while using the new data as your test set.
  4. D. Train on the new data while using the existing data as your test set.
正解を表示

正解

B

問題 3

Question#3Topic1

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 you used self-joins to generate reports. The server resource utilization was at 50%. Since then, the 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?

  1. A. Add capacity (memory and disk space) to the database server by the order of 200.
  2. B. Shard the tables into smaller ones based on date ranges, and only generate reports with prespecified date ranges.
  3. C. Normalize the master patient-record table into the patient table and the visits table, and create other necessary tables to avoid self-join.
  4. 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.
正解を表示

正解

C

問題 4

Question#4Topic1

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?

  1. A. Disable caching by editing the report settings.
  2. B. Disable caching in BigQuery by editing table details.
  3. C. Refresh your browser tab showing the visualizations.
  4. D. Clear your browser history for the past hour then reload the tab showing the virtualizations.
正解と解説を表示

正解

A

解説

Reference:
https://support.google.com/datastudio/answer/7020039?hl=en
Question illustration

問題 5

Question#5Topic1

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?

  1. A. Use federated data sources, and check data in the SQL query.
  2. B. Enable BigQuery monitoring in Google Stackdriver and create an alert.
  3. C. Import the data into BigQuery using the gcloud CLI and set max_bad_records to 0.
  4. D. Run a Google Cloud Dataflow batch pipeline to import the data into BigQuery, and push errors to another dead-letter table for analysis.
正解を表示

正解

D

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