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| Section | Objectives |
|---|---|
| Topic 1: Serving and scaling models | - Batch prediction - Online prediction (Vertex AI Prediction) - Hardware accelerators (GPU/TPU) in serving - Model optimization (Quantization, Distillation) |
| Topic 2: Collaborating within and across teams to manage data and models | - Data management and governance - Version control and reproducibility (e.g., DVC, MLOps) - Collaboration between Data Scientists, Data Engineers, and ML Engineers |
| Topic 3: Scaling prototypes into ML models | - Frameworks (TensorFlow, PyTorch, JAX, Scikit-learn) - Hyperparameter tuning - Training at scale (Distributed training, TPUs) |
| Topic 4: Architecting low-code ML solutions | - AutoML capabilities and implementation - Implementing BigQuery ML for basic models - Leveraging pre-built ML models as a service (e.g., Vision AI, Speech-to-Text, Recommendations AI) |
| Topic 5: Monitoring ML solutions | - Model retraining strategies - Logging and alerting (Cloud Monitoring) - Performance monitoring and drift detection |
| Topic 6: Automating and orchestrating ML pipelines | - CI/CD for ML systems - Triggering and scheduling pipelines - Vertex AI Pipelines (Kubeflow Pipelines) |
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NEW QUESTION # 186
You work for a magazine distributor and need to build a model that predicts which customers will renew their subscriptions for the upcoming year. Using your company's historical data as your training set, you created a TensorFlow model and deployed it to AI Platform. You need to determine which customer attribute has the most predictive power for each prediction served by the model. What should you do?
Answer: D
Explanation:
Option A is incorrect because using AI Platform notebooks to perform a Lasso regression analysis on your model, which will eliminate features that do not provide a strong signal, is not a suitable way to determine which customer attribute has the most predictive power for each prediction served by the model. Lasso regression is a method of feature selection that applies a penalty to the coefficients of the linear model, and shrinks them to zero for irrelevant features1. However, this method assumes that the model is linear and additive, which may not be the case for a TensorFlow model. Moreover, this method does not provide feature attributions for each prediction, but rather for the entire dataset.
Option B is incorrect because streaming prediction results to BigQuery, and using BigQuery's CORR(X1, X2) function to calculate the Pearson correlation coefficient between each feature and the target variable, is not a valid way to determine which customer attribute has the most predictive power for each prediction served by the model. The Pearson correlation coefficient is a measure of the linear relationship between two variables, ranging from -1 to 12. However, this method does not account for the interactions between features or the non-linearity of the model. Moreover, this method does not provide feature attributions for each prediction, but rather for the entire dataset.
Option C is correct because using the AI Explanations feature on AI Platform, and submitting each prediction request with the 'explain' keyword to retrieve feature attributions using the sampled Shapley method, is the best way to determine which customer attribute has the most predictive power for each prediction served by the model. AI Explanations is a service that allows you to get feature attributions for your deployed models on AI Platform3. Feature attributions are values that indicate how much each feature contributed to the prediction for a given instance4. The sampled Shapley method is a technique that uses the Shapley value, a game-theoretic concept, to measure the contribution of each feature to the prediction5. By using AI Explanations, you can get feature attributions for each prediction request, and identify the most important features for each customer.
Option D is incorrect because using the What-If tool in Google Cloud to determine how your model will perform when individual features are excluded, and ranking the feature importance in order of those that caused the most significant performance drop when removed from the model, is not a practical way to determine which customer attribute has the most predictive power for each prediction served by the model. The What-If tool is a tool that allows you to visualize and analyze your ML models and datasets. However, this method requires manually editing or removing features for each instance, and observing the change in the prediction. This method is not scalable or efficient, and may not capture the interactions between features or the non-linearity of the model.
Reference:
Lasso regression
Pearson correlation coefficient
AI Explanations overview
Feature attributions
Sampled Shapley method
[What-If tool overview]
NEW QUESTION # 187
You work for a gaming company that manages a popular online multiplayer game where teams with 6 players play against each other in 5-minute battles. There are many new players every day. You need to build a model that automatically assigns available players to teams in real time.
User research indicates that the game is more enjoyable when battles have players with similar skill levels. Which business metrics should you track to measure your model's performance?
Answer: A
NEW QUESTION # 188
You manage a team of data scientists who use a cloud-based backend system to submit training jobs. This system has become very difficult to administer, and you want to use a managed service instead. The data scientists you work with use many different frameworks, including Keras, PyTorch, theano, scikit-learn, and custom libraries. What should you do?
Answer: A
NEW QUESTION # 189
You maintain a credit risk model that scores the company's entire portfolio of 80 million accounts once per month. Results are written to BigQuery for downstream reporting, and there is no interactive consumer of the predictions. Your current design keeps a large online endpoint running continuously. You need to reduce cost without affecting the reporting schedule. What should you do?
Answer: D
Explanation:
Batch prediction provisions compute only for the duration of the job and integrates natively with BigQuery as both source and destination. Because no low-latency consumer exists, an always-on endpoint incurs continuous cost for no benefit, so undeploying it removes the largest cost driver.
NEW QUESTION # 190
Your data science team is training a PyTorch model for image classification based on a pre- trained RestNet model. You need to perform hyperparameter tuning to optimize for several parameters. What should you do?
Answer: B
Explanation:
Vertex AI supports custom models hyperparameter tuning.
NEW QUESTION # 191
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