AI0-001 Practice Test 27 — 15 Questions

Question 1

A company is deploying a real-time object detection model on a fleet of IoT cameras. The model must run at 30 FPS on a device with limited memory and no internet connectivity. Which combination of techniques is MOST suitable?

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

Accepted Answer

Apply INT8 quantization and pruning, then deploy using TensorFlow Lite. INT8 quantization reduces model size and latency, while pruning removes redundant weights, making the model suitable for memory-constrained edge devices. TensorFlow Lite is optimized for on-device inference with no internet dependency, supporting real-time 30 FPS object detection on IoT cameras.

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Use FP16 inference and deploy via Docker containers

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Use model distillation to create a smaller model and deploy via ONNX Runtime

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Deploy on a GPU-based edge server with a full PyTorch model

Question 2

An AI governance team is implementing the NIST AI Risk Management Framework. They have identified a high-risk AI system and are in the 'Measure' function. Which activity is most appropriate for this function?

Accepted Answer

Conduct bias and fairness impact assessments on the model. In the NIST AI Risk Management Framework (AI RMF), the 'Measure' function focuses on assessing and analyzing risks associated with AI systems. For a high-risk AI system, conducting bias and fairness impact assessments is a core activity within this function, as it quantifies and evaluates potential harms related to fairness, accuracy, and transparency. This aligns with the framework's emphasis on quantitative and qualitative risk measurement before moving to risk treatment in the 'Manage' function.

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Implement technical controls to mitigate identified risks

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Document the system's intended purpose and data sources

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Establish an AI ethics board to oversee risk decisions

Question 3

An AI system for autonomous vehicles uses reinforcement learning (RL) to navigate. The reward function encourages reaching the destination quickly but penalizes collisions heavily. The agent learns to drive aggressively, causing minor accidents. Which modification to the reward function would best align the agent's behavior with desired safe driving?

Accepted Answer

Add a penalty term for high acceleration and jerky movements.. Adding a penalty for high acceleration and jerky movements directly addresses the root cause of the aggressive driving behavior—smoothness and safety—without undermining the primary goal of reaching the destination. This modification shapes the reward function to penalize unsafe driving patterns, aligning the agent's learned policy with desired safe navigation while preserving the time-based incentive for efficiency.

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Increase the collision penalty to a very large negative value.

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Remove the time-based reward and only reward reaching the destination.

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Use a potential-based reward shaping to encourage progress toward destination.

Question 4

A team is building a model to predict stock prices based on time series data. They need to capture long-term dependencies and avoid vanishing gradients. Which architecture is best suited?

Accepted Answer

LSTM. LSTM (Long Short-Term Memory) networks are specifically designed to capture long-term dependencies in sequential data through their gating mechanisms (input, forget, and output gates), which regulate the flow of information and mitigate the vanishing gradient problem that plagues standard RNNs. This makes them ideal for time series forecasting tasks like stock price prediction, where historical context over many time steps is critical.

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Standard RNN

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Autoencoder

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CNN

Question 5

A healthcare company is developing a predictive model to identify patients at risk of readmission within 30 days. The data engineering team has built a pipeline that collects data from multiple sources, including electronic health records (EHR), lab results, and wearable device data. During initial testing, the model's performance is poor, with high false positives. Upon investigation, the team discovers that the data contains significant temporal misalignment: lab results are timestamped when ordered, not when collected; wearable data is aggregated hourly; and EHR data has inconsistent update frequencies. The data pipeline currently joins all features on the patient ID without aligning timestamps. The data volume is large, and processing time is a concern. Which action should the data engineering team take to most effectively address the issue and improve model performance?

Accepted Answer

Implement a window-based feature aggregation (e.g., 6-hour windows) and align all features to the same time windows before joining.. Temporal misalignment causes features to be joined at incorrect times, leading to data leakage or irrelevant features that degrade model performance. Implementing window-based aggregation aligns all features to consistent time windows (e.g., 6-hour) before joining, ensuring that features reflect the patient's state at the same point in time. This addresses the root cause and improves model accuracy.

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Discard all records where timestamps do not match exactly across sources, and only use records with perfect alignment.

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Leave the pipeline unchanged and instead adjust the model's classification threshold to reduce false positives.

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Use a data imputation algorithm to fill in missing timestamps and then join on the nearest timestamp.

Question 6

A security analyst is investigating a potential adversarial attack on a production image classifier. The attack involves tiny perturbations that are invisible to the human eye but cause the model to misclassify a stop sign as a speed limit sign. Which type of attack is this?

Accepted Answer

Adversarial example. This is an adversarial example attack, where imperceptible perturbations are added to the input (e.g., a stop sign) to cause the model to misclassify it (e.g., as a speed limit sign). The perturbations are crafted using gradient-based methods (like FGSM or PGD) to maximize the model's loss, exploiting its linearity in high-dimensional spaces. This differs from other attacks because it targets the inference phase, not the training data or model parameters.

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Data poisoning

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Model inversion

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Membership inference

Question 7

An AI security analyst is reviewing the OWASP LLM Top 10. Which of the following is listed as the top vulnerability?

Accepted Answer

Prompt injection. Prompt injection is listed as the top vulnerability in the OWASP LLM Top 10 because it directly exploits the way large language models process and execute user-supplied input. By crafting malicious prompts, an attacker can override the model's intended behavior, bypass safety guardrails, and cause the LLM to execute unauthorized actions or leak sensitive data. This vulnerability is considered the most critical due to its ease of exploitation and the severe impact it can have on LLM-integrated applications.

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Sensitive information disclosure

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Supply chain vulnerabilities

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Insecure output handling

Question 8

An organization is deploying an AI model on edge devices with limited computational resources. Which model optimization technique is most appropriate?

Accepted Answer

Apply model quantization. Model quantization reduces the precision of the model's weights and activations (e.g., from 32-bit floating point to 8-bit integer), which significantly decreases memory footprint and computational requirements. This makes it ideal for deployment on edge devices with limited resources, as it enables faster inference with minimal accuracy loss.

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Perform additional feature engineering

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Use an ensemble of models

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Increase the training dataset size

Question 9

A data science team is deploying a real-time fraud detection model on edge devices in retail stores. The model must infer under 10ms and fit within 50MB memory. Which combination of techniques should the team apply?

Accepted Answer

Model quantization to INT8 and pruning of low-weight connections. INT8 quantization reduces each weight from 32-bit float to 8-bit integer, cutting model size ~4x and enabling faster integer arithmetic that meets the sub-10ms latency target. Pruning removes low-magnitude weight connections, further shrinking the 50MB footprint and reducing compute. Together they are the standard edge-optimization pairing for latency- and memory-constrained inference.

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Model parallelism and distributed inference

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Increase batch size and use FP16 precision

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Train a larger model and use distillation to transfer knowledge

Question 10

During model training, the data science team discovers that many input features contain missing values. Which step should be taken to improve data quality?

Accepted Answer

Implement data validation checks to handle missing data appropriately (e.g., imputation).. Data validation checks, such as imputation (e.g., mean, median, or KNN imputation), directly address missing values by estimating plausible replacements based on the available data. This improves data quality and prevents bias or loss of information that could degrade model performance. In the context of AI implementation, handling missing data is a fundamental data preprocessing step to ensure robust model training.

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Increase the model complexity to handle missing data.

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Ignore missing values and train the model.

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Remove all records with missing values.

Question 11

A generative AI model is asked to 'Write a poem about AI' and returns a very short, generic response. The user wants longer, more creative outputs. Which parameter adjustment is MOST likely to help?

Accepted Answer

Increase the temperature parameter. Increasing the temperature parameter raises the randomness of token selection, encouraging the model to explore less probable word sequences and produce more varied, creative, and longer outputs. A low temperature (e.g., 0.1) makes the model deterministic and repetitive, often yielding short, generic responses. By increasing temperature (e.g., to 0.8 or 1.0), the model is more likely to generate diverse and expansive text, directly addressing the user's request for longer, more creative poems.

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Decrease the top-p value

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Increase the frequency penalty

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Decrease the max tokens limit

Question 12

Which TWO of the following are essential components of a responsible AI governance framework?

Accepted Answer

Assignment of a responsible owner for each AI system's outcomes. Assigning a responsible owner for each AI system's outcomes ensures accountability, which is a core principle of AI governance. This owner is typically a designated individual or team that oversees the system's lifecycle, including monitoring for bias, compliance with regulations, and handling incidents. Without clear ownership, there is no single point of contact for ethical or legal issues, making governance ineffective.

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Using ensemble methods to reduce overfitting

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Automated hyperparameter tuning to improve accuracy

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Deploying models on dedicated hardware to reduce latency

Question 13

An image classification model misclassifies a stop sign as a speed limit sign after a few pixels are altered. What is the most effective defense against such attacks?

Accepted Answer

Adversarial training. Adversarial training is the most effective defense because it explicitly incorporates adversarial examples—like the perturbed stop sign—into the model's training data. By training on both clean and adversarially altered images, the model learns to be robust against small, malicious perturbations that cause misclassification. This directly addresses the root cause of the vulnerability, unlike other options that only mitigate symptoms or ignore the attack vector.

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Use a larger validation dataset

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Reduce the input image resolution

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Increase the model's complexity

Question 14

A team is training a deep learning model for image classification. The training loss decreases rapidly but validation loss starts increasing after a few epochs. Which regularization technique should be applied to mitigate this issue?

Accepted Answer

Early stopping. Early stopping halts training when validation loss starts increasing, preventing overfitting. Option A (data augmentation) is wrong because it increases data diversity but does not stop training when validation loss increases. Option B (L2 regularization) is wrong because it penalizes large weights but does not directly address the issue of validation loss increasing. Option D (dropout) is wrong because while it helps generalize by randomly dropping neurons, it does not stop training when overfitting occurs.

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Data augmentation

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L2 regularization

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Dropout

Question 15

What is the primary function of an AI ethics board within an organization?

Accepted Answer

Reviewing AI projects for ethical compliance. The primary function of an AI ethics board is to review AI projects for ethical compliance, ensuring that the organization's AI systems adhere to established ethical principles, legal standards, and governance frameworks. This board typically assesses risks related to bias, fairness, transparency, and accountability before deployment, rather than engaging in technical development or operational tasks.

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Developing algorithms

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Managing cloud infrastructure

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Marketing AI products