Google Professional-Machine-Learning-Engineer 問題集

試験コード：Professional-Machine-Learning-Engineer

試験名称：Google Professional Machine Learning Engineer

最近更新時間：2026-09-27

問題と解答：全412問

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質問 1：
You are developing a model to help your company create more targeted online advertising campaigns. You need to create a dataset that you will use to train the model. You want to avoid creating or reinforcing unfair bias in the model. What should you do? (Choose two.)
A. Collect a stratified sample of production traffic to build the training dataset
B. Include a comprehensive set of demographic features
C. Conduct fairness tests across sensitive categories and demographics on the trained model
D. Collect a random sample of production traffic to build the training dataset
E. Include only the demographic groups that most frequently interact with advertisements
正解：A

質問 2：
You are training a custom language model for your company using a large dataset. You plan to use the Reduction Server strategy on Vertex AI. You need to configure the worker pools of the distributed training job. What should you do?
A. Configure the machines of the first two pools to have TPUs, and to use a container image where your training code runs. Configure the third pool to have TPUs, and use the reductionserver container image.
B. Configure the machines of the first two worker pools to have TPUs and to use a container image where your training code runs. Configure the third worker pool without accelerators, and use the reductionserver container image without accelerators, and choose a machine type that prioritizes bandwidth.
C. Configure the machines of the first two worker pools to have GPUs and to use a container image where your training code runs. Configure the third worker pool to use the reductionserver container image without accelerators, and choose a machine type that prioritizes bandwidth.
D. Configure the machines of the first two worker pools to have GPUs, and to use a container image where your training code runs. Configure the third worker pool to have GPUs, and use the reductionserver container image.
正解：C

質問 3：
You are developing an ML model to predict house prices. While preparing the data, you discover that an important predictor variable, distance from the closest school, is often missing and does not have high variance. Every instance (row) in your data is important. How should you handle the missing data?
A. Predict the missing values using linear regression.
B. Delete the rows that have missing values.
C. Replace the missing values with zeros.
D. Apply feature crossing with another column that does not have missing values.
正解：A

質問 4：
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?
A. Use Data Fusion's GUI to build the transformation pipelines, and then write the data into BigQuery.
B. 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.
C. Convert your PySpark into SparkSQL queries to transform the data, and then run your pipeline on Dataproc to write the data into BigQuery.
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：D
解説: (Topexam メンバーにのみ表示されます)

質問 5：
You work for a media company that operates a streaming movie platform where users can search for movies in a database. The existing search algorithm uses keyword matching to return results.
Recently, you have observed an increase in searches using complex semantic queries that include the movies' metadata such as the actor, genre, and director.
You need to build a revamped search solution that will provide better results, and you need to build this proof of concept as quickly as possible. How should you build the search platform?
A. Configure Vertex AI Vector Search as the search platform's backend.
B. Create the search platform through Vertex AI Agent Builder.
C. Use a foundational large language model (LLM) from Model Garden as the search platform's backend.
D. Use a BERT-based model and host it on a Vertex AI endpoint.
正解：A
解説: (Topexam メンバーにのみ表示されます)

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Google Professional-Machine-Learning-Engineer 認定試験の出題範囲：

トピック	出題範囲
トピック 1	Automating and orchestrating ML pipelines: This topic focuses on developing end-to-end ML pipelines, automation of model retraining, and lastly tracking and auditing metadata.
トピック 2	Monitoring ML solutions: It identifies risks to ML solutions. Moreover, the topic discusses monitoring, testing, and troubleshooting ML solutions.
トピック 3	Scaling prototypes into ML models: This topic covers building and training models. It also focuses on opting for suitable hardware for training.
トピック 4	Collaborating within and across teams to manage data and models: It explores and processes organization-wide data including Apache Spark, Cloud Storage, Apache Hadoop, Cloud SQL, and Cloud Spanner. The topic also discusses using Jupyter Notebooks to model prototypes. Lastly, it discusses tracking and running ML experiments.
トピック 5	Architecting low-code AI solutions: This section of the exam measures the skills of Google Machine Learning Engineers and covers developing machine learning models using BigQuery ML. It includes selecting appropriate models for business problems, such as linear and binary classification, regression, time series, matrix factorization, boosted trees, and autoencoders. Additionally, it involves feature engineering or selection and generating predictions using BigQuery ML.

参照：https://cloud.google.com/certification/guides/machine-learning-engineer