Professional-Machine-Learning-Engineer試験無料問題集（283題）「Google Professional Machine Learning Engineer 認定」

出題：1

You trained a model on data stored in a Cloud Storage bucket. The model needs to be retrained frequently in Vertex AI Training using the latest data in the bucket. Data preprocessing is required prior to retraining. You want to build a simple and efficient near-real-time ML pipeline in Vertex AI that will preprocess the data when new data arrives in the bucket. What should you do?

A. Create a Cloud Run function that is triggered when new data arrives in the bucket. The function initiates a Vertex AI Pipeline to preprocess the new data and store the processed features in Vertex AI Feature Store.

B. Build a Dataflow pipeline to preprocess the new data in the bucket and store the processed features in BigQuery. Configure a cron job to trigger the pipeline execution.

C. Create a pipeline using the Vertex AI SDK. Schedule the pipeline with Cloud Scheduler to preprocess the new data in the bucket. Store the processed features in Vertex AI Feature Store.

D. Use the Vertex AI SDK to preprocess the new data in the bucket prior to each model retraining. Store the processed features in BigQuery.

正解：A 解答を投票する

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出題：2

You recently built the first version of an image segmentation model for a self-driving car. After deploying the model, you observe a decrease in the area under the curve (AUC) metric. When analyzing the video recordings, you also discover that the model fails in highly congested traffic but works as expected when there is less traffic. What is the most likely reason for this result?

A. The model is overfitting in areas with less traffic and underfitting in areas with more traffic.

B. AUC is not the correct metric to evaluate this classification model.

C. Too much data representing congested areas was used for model training.

D. Gradients become small and vanish while backpropagating from the output to input nodes.

正解：A 解答を投票する

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出題：3

You have a large corpus of written support cases that can be classified into 3 separate categories: Technical Support, Billing Support, or Other Issues. You need to quickly build, test, and deploy a service that will automatically classify future written requests into one of the categories. How should you configure the pipeline?

A. Use the Cloud Natural Language API to obtain metadata to classify the incoming cases.

B. Create a TensorFlow model using Google's BERT pre-trained model. Build and test a classifier, and deploy the model using Vertex AI.

C. Use AutoML Natural Language to build and test a classifier. Deploy the model as a REST API.

D. Use BigQuery ML to build and test a logistic regression model to classify incoming requests. Use BigQuery ML to perform inference.

正解：C 解答を投票する

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出題：4

You are an ML engineer responsible for designing and implementing training pipelines for ML models. You need to create an end-to-end training pipeline for a TensorFlow model. The TensorFlow model will be trained on several terabytes of structured data. You need the pipeline to include data quality checks before training and model quality checks after training but prior to deployment. You want to minimize development time and the need for infrastructure maintenance. How should you build and orchestrate your training pipeline?

A. Create the pipeline using TensorFlow Extended (TFX) and standard TFX components. Orchestrate the pipeline using Kubeflow Pipelines deployed on Google Kubernetes Engine.

B. Create the pipeline using TensorFlow Extended (TFX) and standard TFX components. Orchestrate the pipeline using Vertex AI Pipelines.

C. Create the pipeline using Kubeflow Pipelines domain-specific language (DSL) and predefined Google Cloud components. Orchestrate the pipeline using Vertex AI Pipelines.

D. Create the pipeline using Kubeflow Pipelines domain-specific language (DSL) and predefined Google Cloud components. Orchestrate the pipeline using Kubeflow Pipelines deployed on Google Kubernetes Engine.

正解：B 解答を投票する

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出題：5

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. Write a script that sequentially performs the push to your development branch and executes the unit tests on Cloud Run

B. 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.

C. Using Cloud Build, set an automated trigger to execute the unit tests when changes are pushed to your development branch.

D. 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

正解：C 解答を投票する

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出題：6

You are collaborating on a model prototype with your team. You need to create a Vertex Al Workbench environment for the members of your team and also limit access to other employees in your project. What should you do?

A. 1. Create a new service account and grant it the Notebook Viewer role.
2 Grant the Service Account User role to each team member on the service account.
3 Grant the Vertex Al User role to each team member.
4. Provision a Vertex Al Workbench user-managed notebook instance that uses the new service account.

B. 1 Create a new service account and grant it the Vertex Al User role.
2 Grant the Service Account User role to each team member on the service account.
3. Grant the Notebook Viewer role to each team member.
4 Provision a Vertex Al Workbench user-managed notebook instance that uses the new service account.

C. 1 Grant the Vertex Al User role to the primary team member.
2. Grant the Notebook Viewer role to the other team members.
3. Provision a Vertex Al Workbench user-managed notebook instance that uses the primary user's account.

D. 1. Grant the Vertex Al User role to the default Compute Engine service account.
2. Grant the Service Account User role to each team member on the default Compute Engine service account.
3. Provision a Vertex Al Workbench user-managed notebook instance that uses the default Compute Engine service account.

正解：B 解答を投票する

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出題：7

Your organization manages an online message board A few months ago, you discovered an increase in toxic language and bullying on the message board. You deployed an automated text classifier that flags certain comments as toxic or harmful. Now some users are reporting that benign comments referencing their religion are being misclassified as abusive Upon further inspection, you find that your classifier's false positive rate is higher for comments that reference certain underrepresented religious groups. Your team has a limited budget and is already overextended. What should you do?

A. Replace your model with a different text classifier.

B. Raise the threshold for comments to be considered toxic or harmful

C. Add synthetic training data where those phrases are used in non-toxic ways

D. Remove the model and replace it with human moderation.

正解：C 解答を投票する

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出題：8

You work for a gaming company that has millions of customers around the world. All games offer a chat feature that allows players to communicate with each other in real time. Messages can be typed in more than
20 languages and are translated in real time using the Cloud Translation API. You have been asked to build an ML system to moderate the chat in real time while assuring that the performance is uniform across the various languages and without changing the serving infrastructure.
You trained your first model using an in-house word2vec model for embedding the chat messages translated by the Cloud Translation API. However, the model has significant differences in performance across the different languages. How should you improve it?

A. Replace the in-house word2vec with GPT-3 or T5.

B. Add a regularization term such as the Min-Diff algorithm to the loss function.

C. Train a classifier using the chat messages in their original language.

D. Remove moderation for languages for which the false positive rate is too high.

正解：C 解答を投票する

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出題：9

You work for an organization that operates a streaming music service. You have a custom production model that is serving a "next song" recommendation based on a user's recent listening history. Your model is deployed on a Vertex Al endpoint. You recently retrained the same model by using fresh data. The model received positive test results offline. You now want to test the new model in production while minimizing complexity. What should you do?

A. Create a new Vertex Al endpoint for the new model and deploy the new model to that new endpoint Build a service to randomly send 5% of production traffic to the new endpoint Monitor end-user metrics such as listening time If end-user metrics improve between models over time gradually increase the percentage of production traffic sent to the new endpoint.

B. Configure a model monitoring job for the existing Vertex Al endpoint. Configure the monitoring job to detect prediction drift, and set a threshold for alerts Update the model on the endpoint from the previous model to the new model If you receive an alert of prediction drift, revert to the previous model.

C. Capture incoming prediction requests in BigQuery Create an experiment in Vertex Al Experiments Run batch predictions for both models using the captured data Use the user's selected song to compare the models performance side by side If the new models performance metrics are better than the previous model deploy the new model to production.

D. Deploy the new model to the existing Vertex Al endpoint Use traffic splitting to send 5% of production traffic to the new model Monitor end-user metrics, such as listening time If end-user metrics improve between models over time, gradually increase the percentage of production traffic sent to the new model.

正解：D 解答を投票する

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出題：10

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. 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.

B. 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.

C. 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.

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.

正解：D 解答を投票する

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出題：11

You developed a Vertex Al pipeline that trains a classification model on data stored in a large BigQuery table.
The pipeline has four steps, where each step is created by a Python function that uses the KubeFlow v2 API The components have the following names:

You launch your Vertex Al pipeline as the following:

You perform many model iterations by adjusting the code and parameters of the training step. You observe high costs associated with the development, particularly the data export and preprocessing steps. You need to reduce model development costs. What should you do?

A.

B.

C.

D.

正解：D 解答を投票する

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出題：12

You are developing a Kubeflow pipeline on Google Kubernetes Engine. The first step in the pipeline is to issue a query against BigQuery. You plan to use the results of that query as the input to the next step in your pipeline. You want to achieve this in the easiest way possible. What should you do?

A. Locate the Kubeflow Pipelines repository on GitHub Find the BigQuery Query Component, copy that component's URL, and use it to load the component into your pipeline. Use the component to execute queries against BigQuery

B. Use the BigQuery console to execute your query and then save the query results Into a new BigQuery table.

C. Use the Kubeflow Pipelines domain-specific language to create a custom component that uses the Python BigQuery client library to execute queries

D. Write a Python script that uses the BigQuery API to execute queries against BigQuery Execute this script as the first step in your Kubeflow pipeline

正解：A 解答を投票する

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出題：13

You have built a custom model that performs several memory-intensive preprocessing tasks before it makes a prediction. You deployed the model to a Vertex Al endpoint. and validated that results were received in a reasonable amount of time After routing user traffic to the endpoint, you discover that the endpoint does not autoscale as expected when receiving multiple requests What should you do?

A. Decrease the number of workers per machine

B. Use a machine type with more memory

C. Increase the CPU utilization target in the autoscaling configurations

D. Decrease the CPU utilization target in the autoscaling configurations

正解：D 解答を投票する

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出題：14

You received a training-serving skew alert from a Vertex Al Model Monitoring job running in production.
You retrained the model with more recent training data, and deployed it back to the Vertex Al endpoint but you are still receiving the same alert. What should you do?

A. Update the model monitoring job to use the more recent training data that was used to retrain the model.

B. Update the model monitoring job to use a lower sampling rate.

C. Temporarily disable the alert until the model can be retrained again on newer training data Retrain the model again after a sufficient amount of new production traffic has passed through the Vertex Al endpoint

D. Temporarily disable the alert Enable the alert again after a sufficient amount of new production traffic has passed through the Vertex Al endpoint.

正解：A 解答を投票する

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出題：15

You have trained a text classification model in TensorFlow using Al Platform. You want to use the trained model for batch predictions on text data stored in BigQuery while minimizing computational overhead. What should you do?

A. Submit a batch prediction job on Al Platform that points to the model location in Cloud Storage.

B. Use Dataflow with the SavedModel to read the data from BigQuery

C. Export the model to BigQuery ML.

D. Deploy and version the model on Al Platform.

正解：A 解答を投票する

解説: (GoShiken メンバーにのみ表示されます)

Professional-Machine-Learning-Engineer試験無料問題集「Google Professional Machine Learning Engineer 認定」