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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：2025-06-09

問題と解答：全290問

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質問 1：
You work for a pharmaceutical company based in Canada. Your team developed a BigQuery ML model to predict the number of flu infections for the next month in Canada Weather data is published weekly and flu infection statistics are published monthly. You need to configure a model retraining policy that minimizes cost What should you do?
A. Download the weather data each week, and download the flu data each month Deploy the model to a Vertex Al endpoint with feature drift monitoring. and retrain the model if a monitoring alert is detected.
B. Download the weather and flu data each month Configure Cloud Scheduler to execute a Vertex Al pipeline to retrain the model monthly.
C. Download the weather and flu data each week Configure Cloud Scheduler to execute a Vertex Al pipeline to retrain the model every month.
D. Download the weather and flu data each week Configure Cloud Scheduler to execute a Vertex Al pipeline to retrain the model weekly.
正解：A
解説: (Topexam メンバーにのみ表示されます)

質問 2：
You need to deploy a scikit-learn classification model to production. The model must be able to serve requests
24/7 and you expect millions of requests per second to the production application from 8 am to 7 pm. You need to minimize the cost of deployment What should you do?
A. Deploy an online Vertex Al prediction endpoint Set the max replica count to 1
B. Deploy an online Vertex Al prediction endpoint Set the max replica count to 100
C. Deploy an online Vertex Al prediction endpoint with one GPU per replica Set the max replica count to
100.
D. Deploy an online Vertex Al prediction endpoint with one GPU per replica Set the max replica count to
1.
正解：B
解説: (Topexam メンバーにのみ表示されます)

質問 3：
You work with a data engineering team that has developed a pipeline to clean your dataset and save it in a Cloud Storage bucket. You have created an ML model and want to use the data to refresh your model as soon as new data is available. As part of your CI/CD workflow, you want to automatically run a Kubeflow Pipelines training job on Google Kubernetes Engine (GKE). How should you architect this workflow?
A. Use App Engine to create a lightweight python client that continuously polls Cloud Storage for new files As soon as a file arrives, initiate the training job
B. Use Cloud Scheduler to schedule jobs at a regular interval. For the first step of the job. check the timestamp of objects in your Cloud Storage bucket If there are no new files since the last run, abort the job.
C. Configure a Cloud Storage trigger to send a message to a Pub/Sub topic when a new file is available in a storage bucket. Use a Pub/Sub-triggered Cloud Function to start the training job on a GKE cluster
D. Configure your pipeline with Dataflow, which saves the files in Cloud Storage After the file is saved, start the training job on a GKE cluster
正解：C
解説: (Topexam メンバーにのみ表示されます)

質問 4：
You work for a semiconductor manufacturing company. You need to create a real-time application that automates the quality control process High-definition images of each semiconductor are taken at the end of the assembly line in real time. The photos are uploaded to a Cloud Storage bucket along with tabular data that includes each semiconductor's batch number serial number dimensions, and weight You need to configure model training and serving while maximizing model accuracy. What should you do?
A. Use Vertex Al Data Labeling Service to label the images and train an AutoML image classification model. Schedule a daily batch prediction job that publishes a Pub/Sub message when the job completes.
B. Import the tabular data into BigQuery use Vertex Al Data Labeling Service to label the data and train an AutoML tabular classification model Deploy the model and configure Pub/Sub to publish a message when a semiconductor's data is categorized into the failing class.
C. Convert the images into an embedding representation Import this data into BigQuery, and train a BigQuery. ML K-means clustenng model with two clusters Deploy the model and configure Pub/Sub to publish a message when a semiconductor's data is categorized into the failing cluster.
D. Use Vertex Al Data Labeling Service to label the images and train an AutoML image classification model.
Deploy the model and configure Pub/Sub to publish a message when an image is categorized into the failing class.
正解：D
解説: (Topexam メンバーにのみ表示されます)

質問 5：
You have developed a fraud detection model for a large financial institution using Vertex AI. The model achieves high accuracy, but stakeholders are concerned about potential bias based on customer demographics.
You have been asked to provide insights into the model's decision-making process and identify any fairness issues. What should you do?
A. Use feature attribution in Vertex AI to analyze model predictions and the impact of each feature on the model's predictions.
B. Enable Vertex AI Model Monitoring to detect training-serving skew. Configure an alert to send an email when the skew or drift for a model's feature exceeds a predefined threshold. Retrain the model by appending new data to existing training data.
C. Create feature groups using Vertex AI Feature Store to segregate customer demographic features and non-demographic features. Retrain the model using only non-demographic features.
D. Compile a dataset of unfair predictions. Use Vertex AI Vector Search to identify similar data points in the model's predictions. Report these data points to the stakeholders.
正解：A
解説: (Topexam メンバーにのみ表示されます)

質問 6：
You need to train a natural language model to perform text classification on product descriptions that contain millions of examples and 100,000 unique words. You want to preprocess the words individually so that they can be fed into a recurrent neural network. What should you do?
A. Sort the words by frequency of occurrence, and use the frequencies as the encodings in your model.
B. Identify word embeddings from a pre-trained model, and use the embeddings in your model.
C. Create a hot-encoding of words, and feed the encodings into your model.
D. Assign a numerical value to each word from 1 to 100,000 and feed the values as inputs in your model.
正解：B
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	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 2	Serving and scaling models: This section deals with Batch and online inference, using frameworks such as XGBoost, and managing features using VertexAI.
トピック 3	Monitoring ML solutions: It identifies risks to ML solutions. Moreover, the topic discusses monitoring, testing, and troubleshooting ML solutions.
トピック 4	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 5	Scaling prototypes into ML models: This topic covers building and training models. It also focuses on opting for suitable hardware for training.

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

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Professional-Machine-Learning-Engineer 関連試験: Associate-Google-Workspace-Administrator - Associate Google Workspace Administrator; Associate-Cloud-Engineer - Google Associate Cloud Engineer Exam; Professional-Collaboration-Engineer - Google Cloud Certified - Professional Collaboration Engineer; Associate-Cloud-Engineer-JPN - Google Associate Cloud Engineer Exam (Associate-Cloud-Engineer日本語版); Professional-Cloud-Security-Engineer-JPN - Google Cloud Certified - Professional Cloud Security Engineer Exam (Professional-Cloud-Security-Engineer日本語版)

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