AI0-001 Practice Test 45 — 15 Questions

Question 1

A company uses an LLM to generate code. They want to ensure that the model does not accidentally output sensitive internal logic. Which practice should they implement?

Accepted Answer

Output filtering to block sensitive patterns. Output filtering is the correct practice because it directly inspects the model's generated text for patterns that match sensitive internal logic (e.g., API keys, source code snippets, or proprietary algorithms) and blocks or redacts them before the output is returned to the user. This is a reactive security control that operates at the application layer, ensuring that even if the LLM inadvertently generates sensitive content, it is never exposed. Rate limiting, red teaming, and federated learning address different concerns (availability, vulnerability discovery, and data privacy during training, respectively) and do not prevent the accidental leakage of internal logic in real-time outputs.

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Rate limiting API calls

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Red teaming the model

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Federated learning

Question 2

A cybersecurity firm is developing an AI system to detect zero-day malware using behavior analysis. The team collects a dataset of 1,000 malware samples and 10,000 benign files from corporate endpoints. The model is a random forest classifier. After deployment, the false positive rate is 5%, which is acceptable, but the detection rate for new malware variants drops to 30%. The security analyst suspects the model is overfitting to the specific malware families in the training set. Which improvement should the team implement first?

Accepted Answer

Engineer features that capture generic behavioral patterns. The core issue is that the model has overfitted to the specific malware families in the training set, causing poor generalization to unseen zero-day variants. Engineering features that capture generic behavioral patterns (e.g., API call sequences, file system interactions, network connection anomalies) reduces reliance on family-specific signatures, improving detection of novel malware. This directly addresses the root cause of the 30% detection rate drop without introducing new model complexity or data imbalance issues.

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Use a boosting ensemble instead of bagging

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Collect more malware samples from the same families

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Replace the random forest with a deep neural network

Question 3

Which TWO of the following are effective defenses against adversarial evasion attacks on image classifiers?

Accepted Answer

Gradient masking. Adversarial training and gradient masking (e.g., defensive distillation) are common defenses. Data augmentation helps generalization but not specifically against adversarial perturbations; feature squeezing reduces input complexity; input validation is generic.

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Data augmentation

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Input validation

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Feature squeezing

Question 4

A company wants to store unstructured text data for AI model training while enabling SQL-based queries for analytics. Which storage solution should they use as the primary data source?

Accepted Answer

A data lake like Amazon S3. Amazon S3 is a highly scalable object storage service that can store unstructured text data in its native format (e.g., CSV, JSON, Parquet) and supports SQL-based queries via services like Amazon Athena or S3 Select. This makes it ideal as a primary data source for AI model training while enabling analytics without requiring data transformation or loading into a separate system.

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A vector database like Pinecone

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A data warehouse like Snowflake

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A NoSQL database like DynamoDB

Question 5

A data science team needs to implement privacy-preserving ML for a healthcare model. They require that individual patient records cannot be distinguished in the training output. Which technique should be applied?

Accepted Answer

Differential privacy. Differential privacy is the correct technique because it adds calibrated noise to the training process or query outputs, ensuring that the inclusion or exclusion of any single patient record does not significantly affect the model's output. This provides a formal mathematical guarantee that individual records cannot be distinguished, which directly meets the requirement for privacy-preserving ML in healthcare.

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Homomorphic encryption

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Model pruning

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Federated learning

Question 6

A company implements a chatbot using a rule-based system. Users complain the chatbot cannot handle new queries. Which AI approach should be considered to improve flexibility?

Accepted Answer

Machine learning. Machine learning (ML) enables a chatbot to learn from new data and adapt to unseen queries, unlike a static rule-based system. By training on historical conversations, an ML model can generalize patterns and handle novel inputs without requiring explicit rules for every scenario.

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Expert system

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Natural language processing (NLP)

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Robotic process automation

Question 7

An AI engineer is training a deep neural network for image recognition. The training loss decreases steadily for the first few epochs but then plateaus and starts to oscillate. Which adjustment is most likely to improve convergence?

Accepted Answer

Reduce the learning rate. The plateau and oscillation of the training loss indicate that the optimizer is overshooting the minimum due to a learning rate that is too high. Reducing the learning rate allows the optimizer to take smaller, more precise steps, dampening oscillations and enabling convergence to a lower loss. This is a standard technique in gradient descent optimization, often implemented via learning rate schedules or adaptive methods like Adam.

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Add more layers

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Increase the learning rate

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Increase the batch size

Question 8

An organization uses an LLM to generate financial reports. They want to ensure the model does not output sensitive customer data that it may have memorized during training. Which technique should be implemented in the AI pipeline to detect and block such outputs?

Accepted Answer

Output filtering. Output filtering is the correct technique because it operates after the LLM generates a response, scanning the output for sensitive data patterns (e.g., PII, financial account numbers) and blocking or redacting them before delivery. This directly addresses the risk of the model regurgitating memorized customer data from its training set, which input validation cannot catch since the sensitive data appears only in the output.

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Input validation

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Rate limiting

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Federated learning

Question 9

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 10

An organization needs to store sensitive customer data for training a machine learning model. The data must be encrypted at rest and in transit, and access must be audited. Which combination of practices should be implemented?

Accepted Answer

Use TLS for transfer, AES-256 for storage, and AWS CloudTrail for auditing. It combines TLS (Transport Layer Security) for encrypting data in transit, AES-256 for strong encryption at rest, and AWS CloudTrail for auditing API-level access. TLS ensures confidentiality and integrity during transmission, AES-256 provides robust symmetric encryption for stored data, and CloudTrail logs all AWS API calls for compliance and audit trails. This triad satisfies the requirements of encryption in transit, at rest, and audited access.

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Use FTP for transfer, AES-128 for storage, and manual log review

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Use SSH for transfer, store data in a database, and enable access logs

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Use MD5 for hashing, store data in plaintext, and enable server logs

Question 11

An organisation is deploying a fine-tuned LLM for internal use. They need to ensure the API endpoint is secure and cost-effective. Which TWO measures should they implement? (Choose 2)

Accepted Answer

Implement API key authentication. API key authentication (Option A) is a fundamental security measure that ensures only authorized clients can access the LLM endpoint. It provides a simple, lightweight mechanism to validate requests without the overhead of full OAuth, making it both secure and cost-effective for internal deployments.

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Enable content filtering

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Disable logging to reduce storage costs

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Use gRPC instead of REST

Question 12

A company deploys an AI model to predict equipment failure. The model performs well on historical data but fails to generalize to new data from a different factory. Which concept best describes this issue?

Accepted Answer

Overfitting. (Overfitting) is correct because the model learned patterns specific to the historical data from the original factory, including noise and factory-specific nuances, rather than generalizable features. When applied to new data from a different factory, those learned patterns do not hold, causing poor performance. This is the classic symptom of overfitting: high accuracy on training data but low accuracy on unseen data.

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Transfer learning

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Underfitting

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Bias-variance tradeoff

Question 13

An AI system in a self-driving car misinterprets a stop sign due to a small sticker placed on it. This is an example of which security vulnerability?

Accepted Answer

Adversarial example attack. The sticker on the stop sign creates a small perturbation that causes the AI model's image classifier to misclassify the sign (e.g., as a speed limit sign). This is the defining characteristic of an adversarial example attack, where crafted input perturbations exploit model vulnerabilities to cause incorrect predictions.

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Supply chain attack

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Model inversion attack

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Data poisoning attack

Question 14

A data scientist is using LIME to explain a black-box model. Which TWO characteristics of LIME are true?

Accepted Answer

It creates an interpretable surrogate model locally around a prediction. LIME (Local Interpretable Model-agnostic Explanations) creates local surrogate models to explain individual predictions. It does not provide global feature importance or an inherent measure of model confidence.

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It provides a measure of model confidence in its prediction

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It requires access to the model's internal parameters

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It provides a global ranking of feature importance across the entire dataset

Question 15

A team trained a ResNet-50 model with the configuration shown. The high training accuracy and lower validation accuracy suggest overfitting. Which change to the training configuration is MOST likely to reduce overfitting?

Accepted Answer

Add dropout layers after convolutional layers.. Adding dropout layers after convolutional layers is a regularization technique that randomly drops a fraction of neurons during training, which forces the network to learn more robust features and reduces overfitting. This directly addresses the symptom of high training accuracy with lower validation accuracy by preventing the model from relying too heavily on specific neurons.

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Reduce number of epochs to 5.

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Increase batch size to 64.

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Increase learning rate to 0.01.