AI0-001 Practice Test 14 — 15 Questions

Question 1

A data scientist is preparing a dataset for a classification model. The dataset has missing values in several features and features with very different scales. Which two data preparation steps should be applied?

Choose an answer to begin — your selection is scored in the full session.

Accepted Answer

Cleaning and normalization. Cleaning handles missing values (e.g., imputation), and normalization scales features to a similar range, which is important for many ML algorithms.

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Outlier removal and binning

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Feature selection and dimensionality reduction

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Data augmentation and one-hot encoding

Question 2

A deep learning model for sentiment analysis uses a softmax output layer. The hidden layers currently use tanh activation. Which activation function should replace tanh to mitigate vanishing gradients in deeper networks?

Accepted Answer

ReLU. ReLU (Rectified Linear Unit) is correct because it outputs zero for negative inputs and a positive linear slope for positive inputs, which avoids the saturation problem of tanh. In deeper networks, tanh gradients can vanish as activations approach ±1, slowing or halting learning. ReLU's non-saturating nature keeps gradients flowing for positive inputs, mitigating the vanishing gradient problem.

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Sigmoid

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Softmax

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Linear

Question 3

Which AI governance framework is specifically designed by the U.S. National Institute of Standards and Technology (NIST) to help organizations manage AI risks?

Accepted Answer

NIST AI Risk Management Framework. The NIST AI Risk Management Framework (AI RMF) is the specific governance framework developed by the U.S. National Institute of Standards and Technology to help organizations manage AI risks, including those related to trustworthiness, fairness, and robustness. It provides a structured approach for identifying, assessing, and mitigating risks throughout the AI lifecycle, aligning with NIST's role in setting standards for cybersecurity and risk management.

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ISO/IEC 27001

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COBIT

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GDPR

Question 4

A data science team wants to train a model on sensitive medical records while minimizing the risk of leaking individual patient information. They need to ensure that the model's outputs do not reveal whether a specific patient's data was used in training. Which privacy-preserving technique directly addresses this requirement?

Accepted Answer

Differential privacy. Differential privacy directly addresses the requirement by adding calibrated noise to the training process or model outputs, ensuring that the inclusion or exclusion of any single patient's data does not significantly affect the final model. This provides a formal mathematical guarantee (ε-differential privacy) that an adversary cannot infer whether a specific individual's records were used, even with auxiliary information.

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Homomorphic encryption

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Data anonymization

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Federated learning

Question 5

A company wants to adopt green AI practices to reduce the environmental impact of training large models. Which TWO actions are most effective?

Accepted Answer

Use efficient model architectures (e.g., pruning, quantization). Using efficient model architectures (A) and energy-efficient hardware (E) directly reduce energy consumption. Using larger datasets (B) increases energy use. Training models on weekends (C) does not affect total energy consumption. Training models in the cloud (D) may offload energy costs to the provider but does not reduce total energy used.

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Use larger datasets to improve accuracy

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Train models only on weekends

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Train models in the cloud to offload energy costs

Question 6

A large hospital system deploys an AI triage system for emergency rooms. The system uses patient vitals and symptoms to recommend treatment priority. Six months after deployment, complaints arise that the system frequently underestimates the severity of symptoms for patients from certain ethnic backgrounds. A data scientist runs a bias audit and finds that the model's false negative rate is 20% higher for the minority group. The hospital's AI governance board requires immediate corrective action. The data science team has limited resources and cannot retrain the entire model from scratch. They have access to the training data, which is imbalanced. The model is a gradient boosted tree. Which course of action best addresses the bias while minimizing operational impact?

Accepted Answer

Post-process the model's predictions by adjusting thresholds for the minority group. Post-processing by adjusting decision thresholds for the minority group directly compensates for the higher false negative rate without requiring retraining. Since the team has limited resources and cannot retrain the entire gradient boosted tree model, this approach minimizes operational impact while addressing the bias. The threshold adjustment effectively lowers the probability cutoff for the minority group, making the model more sensitive to their symptoms and reducing underestimation of severity.

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Rebalance the training data using SMOTE and retrain the model

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Use adversarial debiasing during training to remove protected attribute correlations

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Replace the model with a simpler logistic regression model to improve interpretability

Question 7

Which TWO of the following are common techniques to reduce overfitting in a neural network?

Accepted Answer

L2 regularization. L2 regularization (option C) reduces overfitting by adding a penalty term proportional to the squared magnitude of the weights to the loss function. This forces the network to keep weights small, preventing it from fitting noise in the training data and improving generalization.

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Increasing the number of hidden layers

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Using a larger learning rate

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Training for more epochs

Question 8

A company is building a multi-modal AI application that processes text, images, and audio. They need a unified platform to store embeddings for all modalities, perform hybrid search (vector + metadata filtering), and scale to millions of vectors. Which THREE services are suitable for this purpose? (Choose THREE.)

Accepted Answer

Weaviate. Weaviate is a purpose-built vector database that natively supports multi-modal embeddings (text, images, audio) through its vectorizer modules and provides hybrid search combining vector similarity with metadata filtering (e.g., using GraphQL or REST APIs). It is designed to scale to millions of vectors with built-in sharding and replication, making it a strong fit for the described unified platform.

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Amazon S3

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Snowflake

Question 9

A company is deploying a new AI system that processes personal data. To comply with privacy regulations, they want to minimize the risk of membership inference attacks. Which THREE practices should they adopt? (Select three.)

Accepted Answer

Use differential privacy during training. Option A (Use differential privacy during training) is correct because differential privacy adds calibrated noise to the training process, which bounds how much any single individual's data can influence the model, directly reducing the signal that membership inference attacks exploit. Option B (Implement access controls on the model API) is correct because membership inference typically requires repeated, query-based probing of the model's outputs; restricting who can query the API and how often limits an adversary's ability to run the statistical tests needed to infer training-set membership. Option D (Enable audit logging of all model interactions) is correct because logging queries and responses enables detection of the anomalous, high-volume probing patterns characteristic of membership inference attempts, supporting timely investigation and response. Option C (Increase model size to improve accuracy) is not correct because larger, higher-capacity models tend to overfit training data more, which increases rather than minimizes membership inference risk. Option E (Use homomorphic encryption for model inference) is not correct because homomorphic encryption protects data confidentiality during computation but does not prevent an authorized client from analyzing the returned outputs to infer membership.

Answer

Increase model size to improve accuracy

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Use homomorphic encryption for model inference

Question 10

A developer is building an AI-powered code completion tool. To ensure the model does not output malicious code when prompted with 'Write code to delete all files on the system', which defense is most effective?

Accepted Answer

Output filtering to detect and block dangerous code constructs. Output filtering can block generated code that contains dangerous patterns like file deletion commands.

Answer

Input validation to block the word 'delete'

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Rate limiting on the number of requests per user

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Retraining the model on safe code only

Question 11

A startup is building a medical diagnosis support system using a large language model. To prevent the model from generating harmful advice due to hallucinations, which TWO measures should they implement as part of their AI security strategy?

Accepted Answer

Ground the model using Retrieval-Augmented Generation (RAG) with curated medical databases. Option A is correct because Retrieval-Augmented Generation (RAG) grounds the LLM's responses in curated, authoritative medical databases, so the model retrieves verified evidence at inference time rather than relying solely on parametric memory, which directly reduces hallucinated medical advice. Option D is correct because output filtering and content moderation act as a defense-in-depth control that inspects the model's generated text and blocks harmful, unsafe, or unverified medical recommendations before they reach the user. Option B is not correct here because monitoring for anomalous inputs targets data poisoning detection, which protects training-data integrity but does not directly prevent hallucinated outputs. Option C is not correct because federated learning addresses privacy-preserving decentralized training, not hallucination prevention. Option E is not correct because adversarial training improves robustness against adversarial examples, not factual grounding or harmful medical advice generation.

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Monitor for anomalous inputs to detect data poisoning attempts

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Employ federated learning to train on decentralized patient data

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Use robust training techniques like adversarial training

Question 12

A company uses an AI model to screen job applications. The model is trained on historical hiring data that reflects past biases. After deployment, the model disproportionately rejects candidates from certain demographics. Which concept does this best illustrate?

Accepted Answer

Algorithmic bias. Algorithmic bias refers to systematic and unfair discrimination in AI outputs due to biased training data or model design. Option A (overfitting) is about a model that performs well on training data but poorly on new data due to excessive complexity. Option B (model drift) is about performance degradation over time due to changes in data distribution. Option D (underfitting) is when a model is too simple to capture patterns.

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Overfitting

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Model drift

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Underfitting

Question 13

Which THREE practices are recommended for versioning machine learning models in a production environment?

Accepted Answer

Use a model registry like MLflow or DVC.. Option A is correct because a dedicated model registry such as MLflow or DVC is purpose-built for tracking model artifacts, versions, and lifecycle stages, which is the recommended practice for production ML versioning. Option B is correct because storing metadata like hyperparameters and the training data hash ties each model version to its exact training configuration and dataset, enabling reproducibility and auditability. Option C is correct because automating deployment based on version tags ensures that only approved, traceable model versions reach production and keeps deployment consistent with the registry. Option D is not recommended because Git is designed for source code and text, not large binary model files, which bloat repositories and lack proper artifact lineage. Option E is wrong because discarding older models destroys rollback capability, reproducibility, and compliance auditing, and storage savings do not justify that risk.

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Use Git to version model binaries.

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Keep only the latest model to save storage.

Question 14

A self-driving car uses an AI model that learns by trial and error, receiving rewards for correct actions and penalties for mistakes. This type of learning is:

Accepted Answer

Reinforcement learning. Reinforcement learning (RL) is the correct answer because the self-driving car's AI model learns through trial and error, receiving rewards for correct actions and penalties for mistakes. This feedback-driven process, where an agent interacts with an environment to maximize cumulative reward, is the defining characteristic of reinforcement learning, not supervised or unsupervised learning.

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Supervised learning

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Unsupervised learning

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Transfer learning

Question 15

A healthcare organization uses an AI model to recommend treatment plans. The model was trained on data from a single hospital, and now treats patients from multiple demographics. Which ethical concern is most critical?

Accepted Answer

Fairness and bias in predictions. The model was trained on data from a single hospital, which likely has a homogeneous demographic profile. When deployed across multiple demographics, the model may produce biased or unfair predictions for underrepresented groups, making fairness and bias the most critical ethical concern. This directly violates the principle of distributive justice in AI ethics.

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Accountability for treatment outcomes

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Lack of transparency in model decisions

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Privacy violations in training data