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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：2025-04-25

問題と解答：全290問

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質問 1：
You have been asked to build a model using a dataset that is stored in a medium-sized (~10 GB) BigQuery table. You need to quickly determine whether this data is suitable for model development. You want to create a one-time report that includes both informative visualizations of data distributions and more sophisticated statistical analyses to share with other ML engineers on your team. You require maximum flexibility to create your report. What should you do?
A. Use Vertex AI Workbench user-managed notebooks to generate the report.
B. Use Dataprep to create the report.
C. Use the output from TensorFlow Data Validation on Dataflow to generate the report.
D. Use the Google Data Studio to create the report.
正解：A
解説: (Topexam メンバーにのみ表示されます)

質問 2：
You work on a team that builds state-of-the-art deep learning models by using the TensorFlow framework.
Your team runs multiple ML experiments each week which makes it difficult to track the experiment runs.
You want a simple approach to effectively track, visualize and debug ML experiment runs on Google Cloud while minimizing any overhead code. How should you proceed?
A. Set up Vertex Al Experiments to track metrics and parameters Configure Vertex Al TensorBoard for visualization.
B. Set up a Cloud Function to write and save metrics files to a Cloud Storage Bucket Configure a Google Cloud VM to host TensorBoard locally for visualization.
C. Set up a Vertex Al Workbench notebook instance Use the instance to save metrics data in a Cloud Storage bucket and to host TensorBoard locally for visualization.
D. Set up a Cloud Function to write and save metrics files to a BigQuery table. Configure a Google Cloud VM to host TensorBoard locally for visualization.
正解：A
解説: (Topexam メンバーにのみ表示されます)

質問 3：
You have written unit tests for a Kubeflow Pipeline that require custom libraries. You want to automate the execution of unit tests with each new push to your development branch in Cloud Source Repositories. What should you do?
A. Set up a Cloud Logging sink to a Pub/Sub topic that captures interactions with Cloud Source Repositories. Execute the unit tests using a Cloud Function that is triggered when messages are sent to the Pub/Sub topic
B. Using Cloud Build, set an automated trigger to execute the unit tests when changes are pushed to your development branch.
C. Set up a Cloud Logging sink to a Pub/Sub topic that captures interactions with Cloud Source Repositories Configure a Pub/Sub trigger for Cloud Run, and execute the unit tests on Cloud Run.
D. Write a script that sequentially performs the push to your development branch and executes the unit tests on Cloud Run
正解：B
解説: (Topexam メンバーにのみ表示されます)

質問 4：
You are building a real-time prediction engine that streams files which may contain Personally Identifiable Information (Pll) to Google Cloud. You want to use the Cloud Data Loss Prevention (DLP) API to scan the files. How should you ensure that the Pll is not accessible by unauthorized individuals?
A. Create three buckets of data: Quarantine, Sensitive, and Non-sensitive Write all data to the Quarantine bucket.
B. Stream all files to Google Cloud, and write batches of the data to BigQuery While the data is being written to BigQuery conduct a bulk scan of the data using the DLP API.
C. Periodically conduct a bulk scan of that bucket using the DLP API, and move the data to either the Sensitive or Non-Sensitive bucket
D. Create two buckets of data Sensitive and Non-sensitive Write all data to the Non-sensitive bucket Periodically conduct a bulk scan of that bucket using the DLP API, and move the sensitive data to the Sensitive bucket
E. Stream all files to Google CloudT and then write the data to BigQuery Periodically conduct a bulk scan of the table using the DLP API.
正解：A
解説: (Topexam メンバーにのみ表示されます)

質問 5：
You work for a magazine publisher and have been tasked with predicting whether customers will cancel their annual subscription. In your exploratory data analysis, you find that 90% of individuals renew their subscription every year, and only 10% of individuals cancel their subscription. After training a NN Classifier, your model predicts those who cancel their subscription with 99% accuracy and predicts those who renew their subscription with 82% accuracy. How should you interpret these results?
A. This is not a good result because the model should have a higher accuracy for those who renew their subscription than for those who cancel their subscription.
B. This is a good result because the accuracy across both groups is greater than 80%.
C. This is not a good result because the model is performing worse than predicting that people will always renew their subscription.
D. This is a good result because predicting those who cancel their subscription is more difficult, since there is less data for this group.
正解：C
解説: (Topexam メンバーにのみ表示されます)

質問 6：
You recently used XGBoost to train a model in Python that will be used for online serving Your model prediction service will be called by a backend service implemented in Golang running on a Google Kubemetes Engine (GKE) cluster Your model requires pre and postprocessing steps You need to implement the processing steps so that they run at serving time You want to minimize code changes and infrastructure maintenance and deploy your model into production as quickly as possible. What should you do?
A. Use the XGBoost prebuilt serving container when importing the trained model into Vertex Al Deploy the model to a Vertex Al endpoint Work with the backend engineers to implement the pre- and postprocessing steps in the Golang backend service.
B. Use FastAPI to implement an HTTP server Create a Docker image that runs your HTTP server and deploy it on your organization's GKE cluster.
C. Use FastAPI to implement an HTTP server Create a Docker image that runs your HTTP server Upload the image to Vertex Al Model Registry and deploy it to a Vertex Al endpoint.
D. Use the Predictor interface to implement a custom prediction routine Build the custom contain upload the container to Vertex Al Model Registry, and deploy it to a Vertex Al endpoint.
正解：D
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-Cloud-Engineer - Google Associate Cloud Engineer Exam; Professional-Cloud-Security-Engineer-JPN - Google Cloud Certified - Professional Cloud Security Engineer Exam (Professional-Cloud-Security-Engineer日本語版); Cloud-Digital-Leader-JPN - Google Cloud Digital Leader (Cloud-Digital-Leader日本語版); Professional-Collaboration-Engineer-JPN - Google Cloud Certified - Professional Collaboration Engineer (Professional-Collaboration-Engineer日本語版); Professional-Data-Engineer-JPN - Google Certified Professional Data Engineer Exam (Professional-Data-Engineer日本語版)

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