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GCP-MLE 試験練習問題

Google Professional Machine Learning Engineer

GCP-MLE 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-MLE は「Google Professional Machine Learning 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-MLE
公式の試験・資格名
Google Professional Machine Learning Engineer
試験形式
50–60 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
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最初の 20 問を無料プレビュー

全 375 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

You are building an ML model to detect anomalies in real-time sensor data. You will use Pub/Sub to handle incoming requests. You want to store the results for analytics and visualization. How should you configure the pipeline?

  1. A. 1 = Dataflow, 2 = AI Platform, 3 = BigQuery
  2. B. 1 = DataProc, 2 = AutoML, 3 = Cloud Bigtable
  3. C. 1 = BigQuery, 2 = AutoML, 3 = Cloud Functions
  4. D. 1 = BigQuery, 2 = AI Platform, 3 = Cloud Storage
正解と解説を表示

正解

A

解説

Dataflow is must.

問題 2

Question#2Topic1

Your organization wants to make its internal shuttle service route more efficient. The shuttles currently stop at all pick-up points across the city every 30 minutes between 7 am and 10 am. The development team has already built an application on Google Kubernetes Engine that requires users to confirm their presence and shuttle station one day in advance. What approach should you take?

  1. A. 1. Build a tree-based regression model that predicts how many passengers will be picked up at each shuttle station. 2. Dispatch an appropriately sized shuttle and provide the map with the required stops based on the prediction.
  2. B. 1. Build a tree-based classification model that predicts whether the shuttle should pick up passengers at each shuttle station. 2. Dispatch an available shuttle and provide the map with the required stops based on the prediction.
  3. C. 1. Define the optimal route as the shortest route that passes by all shuttle stations with confirmed attendance at the given time under capacity constraints. 2. Dispatch an appropriately sized shuttle and indicate the required stops on the map.
  4. D. 1. Build a reinforcement learning model with tree-based classification models that predict the presence of passengers at shuttle stops as agents and a reward function around a distance-based metric. 2. Dispatch an appropriately sized shuttle and provide the map with the required stops based on the simulated outcome.
正解と解説を表示

正解

C

解説

No need to predict the presences since they are already confirmed, best thing we can do is optimize the route

問題 3

Question#3Topic1

You were asked to investigate failures of a production line component based on sensor readings. After receiving the dataset, you discover that less than 1% of the readings are positive examples representing failure incidents. You have tried to train several classification models, but none of them converge. How should you resolve the class imbalance problem?

  1. A. Use the class distribution to generate 10% positive examples.
  2. B. Use a convolutional neural network with max pooling and softmax activation.
  3. C. Downsample the data with upweighting to create a sample with 10% positive examples.
  4. D. Remove negative examples until the numbers of positive and negative examples are equal.
正解と解説を表示

正解

C

解説

https://developers.google.com/machine-learning/data-prep/construct/sampling-splitting/imbalanced-data#downsampling-and-upweighting - less than 1% of the readings are positive - none of them converge. Downsampling (in this context) means training on a disproportionately low subset of the majority class examples.

問題 4

Question#4Topic1

You want to rebuild your ML pipeline for structured data on Google Cloud. You are using PySpark to conduct data transformations at scale, but your pipelines are taking over 12 hours to run. To speed up development and pipeline run time, you want to use a serverless tool and SQL syntax. You have already moved your raw data into Cloud Storage. How should you build the pipeline on Google Cloud while meeting the speed and processing requirements?

  1. A. Use Data Fusion's GUI to build the transformation pipelines, and then write the data into BigQuery.
  2. B. Convert your PySpark into SparkSQL queries to transform the data, and then run your pipeline on Dataproc to write the data into BigQuery.
  3. C. Ingest your data into Cloud SQL, convert your PySpark commands into SQL queries to transform the data, and then use federated queries from BigQuery for machine learning.
  4. D. Ingest your data into BigQuery using BigQuery Load, convert your PySpark commands into BigQuery SQL queries to transform the data, and then write the transformations to a new table.
正解と解説を表示

正解

D

解説

BQ is the only serverless that support SQL

問題 5

Question#5Topic1

You manage a team of data scientists who use a cloud-based backend system to submit training jobs. This system has become very difficult to administer, and you want to use a managed service instead. The data scientists you work with use many different frameworks, including Keras, PyTorch, theano, Scikit-learn, and custom libraries. What should you do?

  1. A. Use the AI Platform custom containers feature to receive training jobs using any framework.
  2. B. Configure Kubeflow to run on Google Kubernetes Engine and receive training jobs through TF Job.
  3. C. Create a library of VM images on Compute Engine, and publish these images on a centralized repository.
  4. D. Set up Slurm workload manager to receive jobs that can be scheduled to run on your cloud infrastructure.
正解と解説を表示

正解

A

解説

because AI platform supported all the frameworks mentioned. And Kubeflow is not managed service in GCP. https://cloud.google.com/ai-platform/training/docs/getting-started-pytorch

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