You work for a social media company. You need to detect whether posted images contain cars.
Each training example is a member of exactly one class. You have trained an object detection neural network and deployed the model version to AI Platform Prediction for evaluation. Before deployment, you created an evaluation job and attached it to the AI Platform Prediction model version. You notice that the precision is lower than your business requirements allow. How should you adjust the model's final layer softmax threshold to increase precision?

• A. Decrease the number of false negatives.
• B. Decrease the recall.
• C. Increase the recall.
• D. Increase the number of false positives.

解説: (CertShiken メンバーにのみ表示されます)

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 AI 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. Configure a model monitoring job for the existing Vertex AI 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.
• B. Capture incoming prediction requests in BigQuery. Create an experiment in Vertex AI 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 model's performance metrics are better than the previous model, deploy the new model to production.
• C. Create a new Vertex AI 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.
• D. Deploy the new model to the existing Vertex AI 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.

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. Using Cloud Build, set an automated trigger to execute the unit tests when changes are pushed to your development branch.
• 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. 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.
• D. Write a script that sequentially performs the push to your development branch and executes the unit tests on Cloud Run.

解説: (CertShiken メンバーにのみ表示されます)

You built a deep learning-based image classification model by using on-premises data. You want to use Vertex AI to deploy the model to production. Due to security concerns, you cannot move your data to the cloud. You are aware that the input data distribution might change over time. You need to detect model performance changes in production. What should you do?

• A. Use Vertex Explainable AI for model explainability. Configure feature-based explanations.
• B. Create a Vertex AI Model Monitoring job. Enable training-serving skew detection for your model.
• C. Create a Vertex AI Model Monitoring job. Enable feature attribution skew and drift detection for your model.
• D. Use Vertex Explainable AI for model explainability. Configure example-based explanations.

Your team is working on an NLP research project to predict political affiliation of authors based on articles they have written. You have a large training dataset that is structured like this:

You followed the standard 80%-10%-10% data distribution across the training, testing, and evaluation subsets. How should you distribute the training examples across the train-test-eval subsets while maintaining the 80-10-10 proportion?

• A. Distribute paragraphs of texts (i.e., chunks of consecutive sentences) across the train-test-eval subsets:
  Train set: [SentenceA11, SentenceA12, SentenceD11, SentenceD12 ...]
  Test set: [SentenceA13, SentenceB13, SentenceB21, SentenceD23, SentenceC12, SentenceD13 ...] Eval set: [SentenceA11, SentenceA22, SentenceB13, SentenceD22, SentenceC23, SentenceD11 ...]
• B. Distribute sentences randomly across the train-test-eval subsets:
  Train set: [SentenceA11, SentenceA21, SentenceB11, SentenceB21, SentenceC11, SentenceD21 ...] Test set: [SentenceA12, SentenceA22, SentenceB12, SentenceC22, SentenceC12, SentenceD22 ...] Eval set: [SentenceA13, SentenceA23, SentenceB13, SentenceC23, SentenceC13, SentenceD31 ...]
• C. Distribute texts randomly across the train-test-eval subsets:
  Train set: [TextA1, TextB2, ...]
  Test set: [TextA2, TextC1, TextD2, ...]
  Eval set: [TextB1, TextC2, TextD1, ...]
• D. Distribute authors randomly across the train-test-eval subsets: (*)
  Train set: [TextA1, TextA2, TextD1, TextD2, ...]
  Test set: [TextB1, TextB2, ...]
  Eval set: [TexC1,TextC2 ...]

解説: (CertShiken メンバーにのみ表示されます)