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| Section | Objectives |
|---|---|
| Topic 1: Scaling prototypes into ML models | - Frameworks (TensorFlow, PyTorch, JAX, Scikit-learn) - Hyperparameter tuning - Training at scale (Distributed training, TPUs) |
| Topic 2: Monitoring ML solutions | - Performance monitoring and drift detection - Logging and alerting (Cloud Monitoring) - Model retraining strategies |
| Topic 3: Automating and orchestrating ML pipelines | - Triggering and scheduling pipelines - Vertex AI Pipelines (Kubeflow Pipelines) - CI/CD for ML systems |
| Topic 4: Architecting low-code ML solutions | - Implementing BigQuery ML for basic models - Leveraging pre-built ML models as a service (e.g., Vision AI, Speech-to-Text, Recommendations AI) - AutoML capabilities and implementation |
| Topic 5: Serving and scaling models | - Batch prediction - Online prediction (Vertex AI Prediction) - Model optimization (Quantization, Distillation) - Hardware accelerators (GPU/TPU) in serving |
| Topic 6: Collaborating within and across teams to manage data and models | - Version control and reproducibility (e.g., DVC, MLOps) - Data management and governance - Collaboration between Data Scientists, Data Engineers, and ML Engineers |
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NEW QUESTION # 227
You work at a subscription-based company. You have trained an ensemble of trees and neural networks to predict customer churn, which is the likelihood that customers will not renew their yearly subscription. The average prediction is a 15% churn rate, but for a particular customer the model predicts that they are 70% likely to churn. The customer has a product usage history of 30%, is located in New York City, and became a customer in 1997. You need to explain the difference between the actual prediction, a 70% churn rate, and the average prediction. You want to use Vertex Explainable AI. What should you do?
Answer: C
Explanation:
* Option A is incorrect because training local surrogate models to explain individual predictions is not a feature of Vertex Explainable AI, but rather a general technique for interpreting black-box models. Local surrogate models are simpler models that approximate the behavior of the original model around a specific input1.
* Option B is correct because configuring sampled Shapley explanations on Vertex Explainable AI is a way to explain the difference between the actual prediction and the average prediction for a given
* input. Sampled Shapley explanations are based on the Shapley value, which is a game-theoretic concept that measures how much each feature contributes to the prediction2. Vertex Explainable AI supports sampled Shapley explanations for tabular data, such as customer churn3.
* Option C is incorrect because configuring integrated gradients explanations on Vertex Explainable AI is not suitable for explaining the difference between the actual prediction and the average prediction for a given input. Integrated gradients explanations are based on the idea of computing the gradients of the prediction with respect to the input features along a path from a baseline input to the actual input4. Vertex Explainable AI supports integrated gradients explanations for image and text data, but not for tabular data3.
* Option D is incorrect because measuring the effect of each feature as the weight of the feature multiplied by the feature value is not a valid way to explain the difference between the actual prediction and the average prediction for a given input. This method assumes that the model is linear and additive, which is not the case for an ensemble of trees and neural networks. Moreover, this method does not account for the interactions between features or the non-linearity of the model5.
References:
* Local surrogate models
* Shapley value
* Vertex Explainable AI overview
* Integrated gradients
* Feature importance
NEW QUESTION # 228
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?
Answer: B
Explanation:
* Option A is not the best answer because it requires storing the pickled model in Cloud Storage, which may incur additional cost and latency for loading the model. It also requires building a Flask-based app, which may not be necessary for a simple data preprocessing step.
* Option B is not the best answer because it requires building a Flask-based app, which may not be necessary for a simple data preprocessing step. It also requires packaging the app andthe pickled model in a custom container image, which may increase the size and complexity of the image.
* Option C is not the best answer because it requires packaging the pickled model in a custom container image, which may increase the size and complexity of the image. It also does not leverage the Vertex built-in container image, which may provide some optimizations and integrations for XGBoost models.
* Option D is the best answer because it leverages the Vertex built-in container image, which may provide some optimizations and integrations for XGBoost models. It also allows storing the pickled model in Cloud Storage, which may reduce the size and complexity of the image. It also allows building a custom predictor class based on XGBoost Predictor from the Vertex AI SDK, which may simplify the data preprocessing step and the prediction logic.
NEW QUESTION # 229
Your demand-forecasting model triggers a Model Monitoring alert for prediction drift. Retraining is currently a manual process that an engineer starts after receiving the alert email, and the delay is typically several days. You need to automate retraining while ensuring that a degraded model is never promoted to production. What should you do?
Answer: A
Explanation:
Publishing monitoring alerts to Pub/Sub allows an event-driven trigger to launch retraining without human latency. A conditional evaluation step that gates registration and deployment on beating the incumbent prevents automatic promotion of a worse model, which unconditional nightly deployment would allow.
NEW QUESTION # 230
You work for a company that sells corporate electronic products to thousands of businesses worldwide. Your company stores historical customer data in BigQuery. You need to build a model that predicts customer lifetime value over the next three years. You want to use the simplest approach to build the model and you want to have access to visualization tools. What should you do?
Answer: A
NEW QUESTION # 231
You want to rebuild your ML pipeline for structured data on Google Cloud. You are using PySpark to conduct data transformations at scale, but your pipelines are taking over 12 hours to run. To speed up development and pipeline run time, you want to use a serverless tool and SQL syntax. You have already moved your raw data into Cloud Storage. How should you build the pipeline on Google Cloud while meeting the speed and processing requirements?
Answer: C
NEW QUESTION # 232
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