Google Professional Machine Learning Engineer exam dumps: questions 501 to 521 of 521

Correct answers: A, B, D

• A. Correct.

  Correct: Dataflow can be used to process and compute statistics on production data in real-time, enabling comparisons with training data statistics to detect skew.

• B. Correct.

  Correct: Vertex AI Feature Store provides a centralized repository to manage and monitor feature distributions over time, helping identify divergence between training and serving data.

• C. Incorrect.

  Incorrect: While model evaluation pipelines are useful for monitoring model performance, they do not directly help in detecting training-serving skew, which focuses on data mismatches rather than accuracy differences.

• D. Correct.

  Correct: Logging predictions and input features from production, and comparing them with training data using tools like TFDV, is a common and effective way to detect training-serving skew.

• E. Incorrect.

  Incorrect: AI Explanations in Vertex AI is used to interpret model predictions, but it does not directly address the detection of training-serving skew.

Correct answers: A, B, D

• A. Correct.

  Vertex AI Model Monitoring can track data drift and alert on changes in input features, but you must use custom setups or tools to monitor feature attribution drift specifically.

• B. Correct.

  Manually computing SHAP values for historical and recent predictions is a valid approach to compare feature importance and identify drift in attribution.

• C. Incorrect.

  Vertex AI Explainable AI provides explanations for model predictions, but it does not automatically monitor or alert you about feature attribution drift.

• D. Correct.

  Using BigQuery to store feature attribution data and analyzing it with SQL queries is a feasible way to monitor feature attribution drift manually.

• E. Incorrect.

  Deploying a custom model to predict feature attribution drift is unnecessary and not a standard practice for handling this issue.

Correct answers: A, B, D

• A. Correct.

  Correct. Explainable AI tools in Google Cloud can help you inspect and monitor changes in feature importance over time, allowing you to detect attribution drift.

• B. Correct.

  Correct. Vertex AI Model Monitoring provides robust tools for tracking feature distributions and detecting drift in real-time.

• C. Incorrect.

  Incorrect. Retraining the model on the same dataset does not address feature attribution drift, as the drift likely arises from changes in the incoming data, not the stability of the original dataset.

• D. Correct.

  Correct. Comparing new feature attribution scores with baseline scores can identify shifts in how the model interprets features, which is critical in detecting drift.

• E. Incorrect.

  Incorrect. While AutoML Tables offers automatic retraining, it does not specifically monitor or handle feature attribution drift, which requires more targeted tools and analysis.

Correct answers: A, D

• A. Correct.

  Using SHAP values is a standard approach to compute feature importance and analyze how it changes over time. This directly addresses feature attribution drift.

• B. Incorrect.

  Monitoring feature distributions is important for feature drift but does not directly address feature attribution drift, which focuses on the relationship between features and model predictions.

• C. Incorrect.

  Comparing distributions of model predictions helps detect prediction drift but does not provide insights into feature attribution drift.

• D. Correct.

  Google Cloud's Explainable AI tools allow you to analyze feature importance trends, making it a relevant step for monitoring feature attribution drift.

• E. Incorrect.

  Monitoring metrics like mean absolute error (MAE) can help detect performance degradation but does not directly relate to feature attribution drift.

Correct answers: A, B, D

• A. Correct.

  Enabling Vertex AI Model Monitoring provides automated tools to track and alert on various types of drift, including feature attribution drift, which makes it a critical step.

• B. Correct.

  Comparing feature importance (e.g., using SHAP values) between training and live inference data helps to identify shifts in how features contribute to model predictions, which is essential for monitoring feature attribution drift.

• C. Incorrect.

  Retraining the model immediately is not a recommended first step without proper analysis, as it may lead to overfitting or overlooking the root cause of the drift.

• D. Correct.

  Setting up alerts for deviations in feature attribution scores helps in proactively identifying and addressing drift issues, making it an important step in the monitoring process.

• E. Incorrect.

  Ignoring feature attribution drift is not advisable, as it can impact model interpretability and performance. Monitoring data drift in input features alone is not sufficient to address attribution drift.