AI0-001 Practice Test 3 — 10 Questions

Question 1

A data scientist notices the model overfits. Which change to the exhibit's configuration would most likely reduce overfitting?

Accepted Answer

Add L2 regularization to dense layers. Adding L2 regularization to dense layers penalizes large weights by adding a squared magnitude term to the loss function, which forces the model to learn simpler patterns and reduces overfitting. This directly addresses the core issue of the model memorizing noise in the training data.

Answer

Remove dropout layers

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Increase learning rate to 0.01

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Increase units in the first dense layer to 512

Question 2

A data scientist notices that a model's performance on the training set is excellent, but validation accuracy is poor. The team used the same dataset for feature engineering and model selection. What is the MOST likely cause?

Accepted Answer

Train/test leakage caused by using the same data for feature engineering and model selection. Reusing the same data for feature engineering and model selection creates data leakage from validation set into training decisions, leading to overfitting and poor generalization.

Answer

The dataset is too small for the model complexity

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The learning rate is too high

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The model is overfitting due to high variance

Question 3

Which TWO of the following are effective techniques for detecting bias in an AI model?

Accepted Answer

Fairness metrics such as equal opportunity difference. Fairness metrics such as equal opportunity difference directly quantify bias by measuring the difference in true positive rates between privileged and unprivileged groups. A value of zero indicates perfect fairness, while non-zero values reveal disparate treatment, making it a standard technique for bias detection in AI models.

Answer

Feature importance scores

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Confusion matrix on the entire dataset

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Cross-validation accuracy

Question 4

A company uses an AI model to screen job applicants. A disparate impact analysis reveals that the model's rejection rate for a protected group is significantly higher than for others. Which THREE actions should the company take to address this?

Accepted Answer

Revisit training data for historical bias and consider reweighting. Revisiting the training data for historical bias and applying reweighting directly addresses the root cause of disparate impact. If the training data contains biased labels or skewed representation of the protected group, the model will learn and amplify those biases. Reweighting adjusts the loss function to give more importance to underrepresented or disadvantaged groups, helping to equalize error rates across groups.

Answer

Ignore the disparity because the model is accurate overall

Answer

Remove all demographic attributes from the dataset

Question 5

A data scientist is building a model to predict credit default using historical loan data. The dataset contains 100,000 records with 50 features, including income, debt-to-income ratio, and loan amount. The target variable is binary (default vs. no default). The goal is to maximize interpretability while maintaining high accuracy. Which algorithm is MOST appropriate?

Accepted Answer

Logistic regression. Logistic regression is interpretable (coefficients show feature impact) and performs well on binary classification with a large dataset. Decision trees are interpretable but may overfit; random forests and gradient boosting are less interpretable.

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Random forest

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Gradient boosting machine

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Decision tree

Question 6

Which TWO deployment strategies allow for testing a new model version before fully rolling it out?

Accepted Answer

Canary deployment. Canary deployment is correct because it routes a small percentage of live traffic to the new model version while the majority continues using the stable version. This allows real-world validation of the new model's performance and error rates under production load before a full rollout, minimizing blast radius if issues arise.

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Shadow deployment

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Direct cutover

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Blue-Green deployment

Question 7

Which THREE factors are most critical to consider when designing a continuous integration/continuous deployment (CI/CD) pipeline for machine learning?

Accepted Answer

Data quality and schema validation. Data quality and schema validation (A) are critical because ML pipelines are highly sensitive to data drift, missing values, and schema mismatches that can silently degrade model performance. Without automated validation at the CI stage, bad data can pass through and corrupt model training or inference, leading to unreliable outputs in production.

Answer

A/B testing framework for comparing models

Answer

Automated unit testing of application code

Question 8

An e-commerce company deploys a recommendation model that must serve predictions with sub-100 ms latency for millions of users during peak hours. The model is a large neural network. Which architecture is most suitable?

Accepted Answer

Use a distributed system with load balancers and model replicas.. Distributing the model across multiple servers with load balancers and replicas allows horizontal scaling to handle millions of concurrent users while maintaining sub-100 ms latency. This architecture provides fault tolerance and can dynamically adjust to peak traffic loads, which is essential for real-time inference with large neural networks.

Answer

Batch process predictions every hour.

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Deploy the model on a single powerful GPU server.

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Use serverless functions with auto-scaling.

Question 9

A data scientist is training a convolutional neural network (CNN) for object detection. The training loss decreases rapidly but then plateaus at a high value, and the validation loss starts increasing. Which action should the scientist take to improve the model?

Accepted Answer

Reduce the model complexity. The training loss decreasing rapidly then plateauing at a high value while validation loss increases is classic overfitting. Reducing model complexity (Option C) directly addresses overfitting by decreasing the number of parameters or applying regularization (e.g., dropout, L2), which forces the network to learn more generalizable features rather than memorizing noise in the training data.

Answer

Increase the learning rate

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Increase the number of epochs

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Add more convolutional layers

Question 10

A financial institution is training a risk assessment model. The dataset includes customer credit scores, income, age, and past loan defaults. During feature engineering, a data engineer creates a new feature 'income_to_debt_ratio'. Which type of feature engineering technique is this?

Accepted Answer

Feature combination. 'income_to_debt_ratio' is created by combining two existing features (income and debt) into a single derived feature. This is a classic example of feature combination (also known as feature crossing or feature construction), where arithmetic operations or logical rules are applied to existing variables to generate new predictive signals. The goal is to capture interactions or relationships that the original features alone may not express linearly.

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Feature encoding

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Feature scaling

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Feature selection