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Cheat Sheets

Interview question

Q: Let's say we're building a loan approval model, and we discover one of the features is highly correlated with a protected characteristic like zip code, which can be a proxy for race. How would you approach this before deploying the model to production? Strong answer: The first thing I'd do is assemble a team including data scientists, ethicists, and potentially legal counsel. We'd need to thoroughly investigate the feature's correlation, understand the potential sources of bias, and assess the potential for disparate impact. We'd look at fairness metrics at both the individual and group levels. If the feature is deemed necessary, we'd explore mitigation techniques like re-weighting or adversarial debiasing. Documentation of the entire process, including the rationale for any decisions, is critical.

Understanding of fairness metrics
Consideration of disparate impact
Collaboration with diverse stakeholders
Documentation and transparency
Mitigation techniques

Caution: Simply removing the feature without understanding its impact could degrade model performance and mask underlying issues. Also, just saying 'we'll be fair' isn't enough; you need a concrete plan and metrics.

Question 1 An energy model utilizes a feature strongly correlated with protected traits. What is the most responsible ML action to take before deploying this model to production?

Answer choices

A. Keep the feature automatically because it improves AUC
B. Review the feature for bias and harm before production use
C. Disable monitoring to avoid surfacing fairness issues
D. Ignore group-level fairness metrics because the feature is predictive

Correct answer

Review the feature for bias and harm before production use

Features that proxy protected characteristics require responsible ML review before production. The team should assess bias, harm, disparate impact, and whether the feature is appropriate to use.

Wrong-answer review

A. Keep the feature automatically because it improves AUC: For an energy model, the misconception in "Keep the feature automatically because it improves AUC" is that a metric improvement does not remove the obligation to evaluate proxy features for unfair impact.
C. Disable monitoring to avoid surfacing fairness issues: For an energy model, the misconception in "Disable monitoring to avoid surfacing fairness issues" is that turning off monitoring hides risk instead of managing feature or fairness concerns.
D. Ignore group-level fairness metrics because the feature is predictive: For an energy model, the misconception in "Ignore group-level fairness metrics because the feature is predictive" is that predictive value does not remove the need to check whether the feature creates unfair group-level impact.

Extra learning features

Objective/domain: Data Preparation and Feature Management

Source: Responsible AI | Google Cloud

Question 2 An insurance model was trained using a single feature computation path and deployed with a separate one. What issue does this represent?

A. Concept drift from a changing population after deployment
B. Training-serving skew
C. Overfitting from too many model parameters
D. Data leakage from future labels during training

Training-serving skew

Source: About Vertex AI Feature Store

Question 3 A search team needs separate production, candidate, and rollback versions of the same model. Which registry practice should the engineer use?

A. Use model versioning in Model Registry
B. Overwrite the only model artifact each release
C. Store versions only in notebook filenames
D. Use monitoring alerts as version identifiers

Use model versioning in Model Registry

Objective/domain: MLOps, Pipelines, and Automation

Source: Model versioning with Model Registry

Question 4 A recommendation sponsor wants to deploy an ML model immediately, but the team has not established a learnable target or a baseline comparison against non-ML methods. What is the initial and most important step the engineer should take?

A. Collect embeddings before defining the recommendation objective
B. Question whether ML is appropriate before building a model
C. Launch model training before checking a rules baseline
D. Choose an endpoint type before proving that ML adds value

Question whether ML is appropriate before building a model

Objective/domain: Problem Framing and Use Case Selection

Source: Professional ML Engineer exam guide

Question 5 A telecom image classifier memorizes training examples and performs worse on validation data. Which generalization controls should be applied?

A. Increase model size and remove regularization
B. Train longer until the model memorizes training images
C. Use augmentation, dropout, and L2 regularization
D. Evaluate only on the training set

Use augmentation, dropout, and L2 regularization

Objective/domain: Model Development and Evaluation

Source: ML Crash Course: Generalization | Google Developers

Question 6 A bank needs to generate monthly fraud-risk scores for every active account and write the results to BigQuery for analyst review. Which serving mode should the engineer use?

A. Batch prediction
B. Online inference for each user click
C. Manual one-row prediction requests
D. Feature attribution analysis only

Batch prediction

Objective/domain: Serving, Scaling, and Monitoring

Question 7 A manufacturing company utilizes Vertex AI Pipelines to orchestrate a complex workflow involving preparation, training, evaluation, and deployment of machine learning models. To ensure repeatable execution and facilitate auditing, which specific Vertex AI feature should the engineer leverage?

A. Use Vertex AI Pipelines for explicit dependencies and repeatable orchestration
B. Run every step manually from the console
C. Track feature attribution instead of workflow steps
D. Use a serving endpoint as the pipeline scheduler

Use Vertex AI Pipelines for explicit dependencies and repeatable orchestration

Source: Introduction to Vertex AI Pipelines

Question 8 A claims model uses a variable correlated with protected traits and past decisions. What responsible ML action should the team take?

A. Retrain daily on the same biased history
B. Mix unrelated resumes only to balance counts
C. Audit for historical bias and evaluate disparate impact
D. Disable regularization so the model memorizes past decisions

Audit for historical bias and evaluate disparate impact

Source: Responsible AI practices | Google Cloud

Question 9 A legal assistant team requires a consistent and reliable method for evaluating prompt versions and model responses. How should the team structure their evaluation process?

A. Accept every response after informal review
B. Change the evaluation criteria for each run
C. Judge only by whether the prompt sounds polished
D. Use a repeatable evaluation process with defined criteria and metrics

Use a repeatable evaluation process with defined criteria and metrics

Source: Gen AI evaluation service overview

Question 10 A manufacturing prediction endpoint experiences fluctuating traffic throughout the shift and has strict latency requirements. Which serving setting should the engineer configure to dynamically adapt to these changing demands?

A. Use a fixed single replica for all traffic levels
B. Use batch prediction for every live request
C. Configure endpoint autoscaling with appropriate min and max replicas
D. Change the model label instead of scaling serving capacity

Configure endpoint autoscaling with appropriate min and max replicas

Source: Deploy a model to an endpoint | Vertex AI

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An energy model utilizes a feature strongly correlated with protected traits. What is the most responsible ML action to take before deploying this model to production?

An insurance model was trained using a single feature computation path and deployed with a separate one. What issue does this represent?

A search team needs separate production, candidate, and rollback versions of the same model. Which registry practice should the engineer use?

A recommendation sponsor wants to deploy an ML model immediately, but the team has not established a learnable target or a baseline comparison against non-ML methods. What is the initial and most important step the engineer should take?

A telecom image classifier memorizes training examples and performs worse on validation data. Which generalization controls should be applied?

A bank needs to generate monthly fraud-risk scores for every active account and write the results to BigQuery for analyst review. Which serving mode should the engineer use?

A manufacturing company utilizes Vertex AI Pipelines to orchestrate a complex workflow involving preparation, training, evaluation, and deployment of machine learning models. To ensure repeatable execution and facilitate auditing, which specific Vertex AI feature should the engineer leverage?

A claims model uses a variable correlated with protected traits and past decisions. What responsible ML action should the team take?

A legal assistant team requires a consistent and reliable method for evaluating prompt versions and model responses. How should the team structure their evaluation process?

A manufacturing prediction endpoint experiences fluctuating traffic throughout the shift and has strict latency requirements. Which serving setting should the engineer configure to dynamically adapt to these changing demands?

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