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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：2024-04-24

問題と解答：全271問

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質問 1：
You are training an ML model using data stored in BigQuery that contains several values that are considered Personally Identifiable Information (Pll). You need to reduce the sensitivity of the dataset before training your model. Every column is critical to your model. How should you proceed?
A. Using Dataflow, ingest the columns with sensitive data from BigQuery, and then randomize the values in each sensitive column.
B. Use the Cloud Data Loss Prevention (DLP) API to scan for sensitive data, and use Dataflow with the DLP API to encrypt sensitive values with Format Preserving Encryption
C. Before training, use BigQuery to select only the columns that do not contain sensitive data Create an authorized view of the data so that sensitive values cannot be accessed by unauthorized individuals.
D. Use the Cloud Data Loss Prevention (DLP) API to scan for sensitive data, and use Dataflow to replace all sensitive data by using the encryption algorithm AES-256 with a salt.
正解：B
解説: (Topexam メンバーにのみ表示されます)

質問 2：
You have developed an AutoML tabular classification model that identifies high-value customers who interact with your organization's website.
You plan to deploy the model to a new Vertex Al endpoint that will integrate with your website application.
You expect higher traffic to the website during
nights and weekends. You need to configure the model endpoint's deployment settings to minimize latency and cost. What should you do?
A. Configure the model deployment settings to use an n1-standard-32 machine type.
B. Configure the model deployment settings to use an n1-standard-4 machine type. Set the minReplicaCount value to 1 and the maxReplicaCount value to 8.
C. Configure the model deployment settings to use an n1-standard-8 machine type and a GPU accelerator.
D. Configure the model deployment settings to use an n1-standard-4 machine type and a GPU accelerator.
Set the minReplicaCount value to 1 and the maxReplicaCount value to 4.
正解：B
解説: (Topexam メンバーにのみ表示されます)

質問 3：
You are an ML engineer on an agricultural research team working on a crop disease detection tool to detect leaf rust spots in images of crops to determine the presence of a disease. These spots, which can vary in shape and size, are correlated to the severity of the disease. You want to develop a solution that predicts the presence and severity of the disease with high accuracy. What should you do?
A. Create an object detection model that can localize the rust spots.
B. Develop a template matching algorithm using traditional computer vision libraries.
C. Develop an image classification ML model to predict the presence of the disease.
D. Develop an image segmentation ML model to locate the boundaries of the rust spots.
正解：D
解説: (Topexam メンバーにのみ表示されます)

質問 4：
You deployed an ML model into production a year ago. Every month, you collect all raw requests that were sent to your model prediction service during the previous month. You send a subset of these requests to a human labeling service to evaluate your model's performance. After a year, you notice that your model's performance sometimes degrades significantly after a month, while other times it takes several months to notice any decrease in performance. The labeling service is costly, but you also need to avoid large performance degradations. You want to determine how often you should retrain your model to maintain a high level of performance while minimizing cost. What should you do?
A. Compare the cost of the labeling service with the lost revenue due to model performance degradation over the past year. If the lost revenue is greater than the cost of the labeling service, increase the frequency of model retraining; otherwise, decrease the model retraining frequency.
B. Identify temporal patterns in your model's performance over the previous year. Based on these patterns, create a schedule for sending serving data to the labeling service for the next year.
C. Train an anomaly detection model on the training dataset, and run all incoming requests through this model. If an anomaly is detected, send the most recent serving data to the labeling service.
D. Run training-serving skew detection batch jobs every few days to compare the aggregate statistics of the features in the training dataset with recent serving data. If skew is detected, send the most recent serving data to the labeling service.
正解：D
解説: (Topexam メンバーにのみ表示されます)

質問 5：
You recently trained a XGBoost model that you plan to deploy to production for online inference Before sending a predict request to your model's binary you need to perform a simple data preprocessing step This step exposes a REST API that accepts requests in your internal VPC Service Controls and returns predictions You want to configure this preprocessing step while minimizing cost and effort What should you do?
A. Build a Flask-based app. package the app and a pickled model in a custom container image, and deploy the model to Vertex Al Endpoints.
B. Store a pickled model in Cloud Storage Build a Flask-based app packages the app in a custom container image, and deploy the model to Vertex Al Endpoints.
C. Build a custom predictor class based on XGBoost Predictor from the Vertex Al SDK. package it and a pickled model in a custom container image based on a Vertex built-in image, and deploy the model to Vertex Al Endpoints.
D. Build a custom predictor class based on XGBoost Predictor from the Vertex Al SDK and package the handler in a custom container image based on a Vertex built-in container image Store a pickled model in Cloud Storage and deploy the model to Vertex Al Endpoints.
正解：D
解説: (Topexam メンバーにのみ表示されます)

質問 6：
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?
A. Remove negative examples until the numbers of positive and negative examples are equal
B. Use the class distribution to generate 10% positive examples
C. Downsample the data with upweighting to create a sample with 10% positive examples
D. Use a convolutional neural network with max pooling and softmax activation
正解：C
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 Professional-Machine-Learning-Engineer 試験問題:

1. You are an ML engineer at a global shoe store. You manage the ML models for the company's website. You are asked to build a model that will recommend new products to the user based on their purchase behavior and similarity with other users. What should you do?

A) Build a collaborative-based filtering model
B) Build a regression model using the features as predictors
C) Build a knowledge-based filtering model
D) Build a classification model

2. You work at a bank. You need to develop a credit risk model to support loan application decisions You decide to implement the model by using a neural network in TensorFlow Due to regulatory requirements, you need to be able to explain the models predictions based on its features When the model is deployed, you also want to monitor the model's performance overtime You decided to use Vertex Al for both model development and deployment What should you do?

A) Use Vertex Explainable Al with the XRAI method and enable Vertex Al Model Monitoring to check for feature distribution skew.
B) Use Vertex Explainable Al with the sampled Shapley method, and enable Vertex Al Model Monitoring to check for feature distribution drift.
C) Use Vertex Explainable Al with the XRAI method, and enable Vertex Al Model Monitoring to check for feature distribution drift.
D) Use Vertex Explainable Al with the sampled Shapley method, and enable Vertex Al Model Monitoring to check for feature distribution skew.

3. You are developing ML models with Al Platform for image segmentation on CT scans. You frequently update your model architectures based on the newest available research papers, and have to rerun training on the same dataset to benchmark their performance. You want to minimize computation costs and manual intervention while having version control for your code. What should you do?

A) Use Cloud Build linked with Cloud Source Repositories to trigger retraining when new code is pushed to the repository
B) Use Cloud Functions to identify changes to your code in Cloud Storage and trigger a retraining job
C) Create an automated workflow in Cloud Composer that runs daily and looks for changes in code in Cloud Storage using a sensor.
D) Use the gcloud command-line tool to submit training jobs on Al Platform when you update your code

4. 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

5. You are investigating the root cause of a misclassification error made by one of your models. You used Vertex Al Pipelines to tram and deploy the model. The pipeline reads data from BigQuery. creates a copy of the data in Cloud Storage in TFRecord format trains the model in Vertex Al Training on that copy, and deploys the model to a Vertex Al endpoint. You have identified the specific version of that model that misclassified: and you need to recover the data this model was trained on. How should you find that copy of the data'?

A) Use Vertex Al Feature Store Modify the pipeline to use the feature store; and ensure that all training data is stored in it Search the feature store for the data used for the training.
B) Use the logging features in the Vertex Al endpoint to determine the timestamp of the models deployment Find the pipeline run at that timestamp Identify the step that creates the data copy; and search in the logs for its location.
C) Find the job ID in Vertex Al Training corresponding to the training for the model Search in the logs of that job for the data used for the training.
D) Use the lineage feature of Vertex Al Metadata to find the model artifact Determine the version of the model and identify the step that creates the data copy, and search in the metadata for its location.

質問と回答：

質問 # 1
正解： A

質問 # 2
正解： B

質問 # 3
正解： A

質問 # 4
正解： B

質問 # 5
正解： D

Professional-Machine-Learning-Engineer 関連試験: Cloud-Digital-Leader-JPN - Google Cloud Digital Leader (Cloud-Digital-Leader日本語版); Professional-Cloud-Security-Engineer - Google Cloud Certified - Professional Cloud Security Engineer Exam; Professional-Cloud-Database-Engineer - Google Cloud Certified - Professional Cloud Database Engineer; Professional-Cloud-Architect-JPN - Google Certified Professional - Cloud Architect (GCP) (Professional-Cloud-Architect日本語版); Professional-Collaboration-Engineer-JPN - Google Cloud Certified - Professional Collaboration Engineer (Professional-Collaboration-Engineer日本語版)

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