Generative AI for Security Fundamentals Exam Questions and Answers

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Question 1

Which of the following best explains why AI is needed in modern cybersecurity?

A. To replace human security analysts completely
B. To eliminate compliance requirements in organizations
C. To handle alert overload, detect patterns, and automate decisions for faster threat response
D. To focus only on reducing costs rather than improving security

Answer

C. To handle alert overload, detect patterns, and automate decisions for faster threat response

Explanation

AI addresses alert fatigue, improves detection, and speeds up response.

Question 2

Which real-world application of AI helps predict potential cyber threats before they occur?

A. Malware signature database updates
B. Threat forecasting using AI to analyze trends and predict attack patterns
C. Manual log analysis by human analysts
D. Firewall configurations managed by IT staff

Answer

B. Threat forecasting using AI to analyze trends and predict attack patterns

Explanation

AI can forecast future threats by analyzing patterns and data.

Question 3

How does AI security differ from traditional rule-based security?

A. Traditional systems adapt automatically while AI systems remain fixed
B. AI security evolves with new threats, while traditional systems rely on static rules
C. AI security eliminates the need for any security monitoring
D. Traditional security is faster at analyzing large data compared to AI

Answer

B. AI security evolves with new threats, while traditional systems rely on static rules

Explanation

AI continuously learns, unlike rule-based systems.

Question 4

Which pairing correctly matches a GAN component with its role?

A. Discriminator — generates candidate samples to fool the classifier
B. Generator — assigns class labels to real versus fake inputs
C. Generator — maps noise to synthetic samples aimed at fooling the discriminator
D. Discriminator — encodes inputs into a latent manifold for reconstruction

Answer

C. Generator — maps noise to synthetic samples aimed at fooling the discriminator

Explanation

The generator transforms latent inputs into data-like samples to confuse the discriminator.

Question 5

Which use case best reflects “AI-powered attack hunting”?

A. Generating synthetic noise to increase alert counts for broader coverage
B. Scanning massive data to spot hidden threats and uncover risks faster for analyst follow-up
C. Randomly sampling events to reduce investigation workload
D. Locking accounts preemptively for all users to minimize false negatives

Answer

B. Scanning massive data to spot hidden threats and uncover risks faster for analyst follow-up

Explanation

GenAI can sift large telemetry volumes to highlight suspicious patterns for human review.

Question 6

Which triad is most closely associated with outcomes of automated incident response?

A. Data Protection, Behavior Monitoring, Insider Risk
B. Access Control, Anomaly Detection, Behavior Monitoring
C. Instant Detection, Rapid Containment, Reduced Delay
D. Threat Simulation, Red Teaming, Purple Team Fusion

Answer

C. Instant Detection, Rapid Containment, Reduced Delay

Explanation

These outcomes are commonly highlighted for automated incident response.

Question 7

In practice, what is a prudent way to deploy LLMs as assistive tools for security analysts?

A. Allow unfettered model actions on production systems
B. Disable any human review to reduce latency
C. Draft summaries with humans approving high-impact steps
D. Replace detection rules with free-form generation

Answer

C. Draft summaries with humans approving high-impact steps

Explanation

Assistance + oversight improves speed without sacrificing control.

Question 8

Which option correctly identifies a named variant within a contemporary GPT family?

A. GPT-4q Audio-Only
B. GPT-4o Mini
C. GPT-3.9 Binary
D. GPT-4e Legacy

Answer

B. GPT-4o Mini

Explanation

It’s a named member alongside GPT-4o and GPT-4o Realtime.

Question 9

Which description best captures what large language models are designed to do?

A. Learn from extensive text to understand and generate human-like language
B. Train image classifiers by maximizing pixel-level likelihood only
C. Replace compilers by directly executing source code
D. Manage databases by enforcing relational integrity constraints

Answer

A. Learn from extensive text to understand and generate human-like language

Explanation

LLMs are trained on large text corpora to model and produce natural language.

Question 10

In an autoregressive language model, what is the fundamental generation mechanism?

A. Classify entire documents in a single step
B. Denoise masked tokens using bidirectional attention only
C. Predict the next token given previous tokens, repeatedly
D. Encode/Decode images to reconstruct pixel intensities

Answer

C. Predict the next token given previous tokens, repeatedly

Explanation

Autoregressive models sample sequentially from conditional token distributions.

Question 11

Which of the following best shows AI’s role in phishing detection?

A. AI deletes all suspicious emails without review
B. AI guarantees zero phishing attempts in organizations
C. AI analyzes patterns in emails to detect suspicious links and fraudulent messages
D. AI replaces email servers entirely with automated platforms

Answer

C. AI analyzes patterns in emails to detect suspicious links and fraudulent messages

Explanation

AI spots phishing by analyzing anomalies in communication patterns.

Question 12

What distinguishes a Variational Autoencoder (VAE) from a plain autoencoder?

A. It learns a probabilistic latent distribution and samples from it
B. It removes the decoder and performs classification instead of reconstruction
C. It replaces the encoder with a discriminator trained adversarially
D. It prevents any stochasticity to guarantee identical reconstructions each run

Answer

A. It learns a probabilistic latent distribution and samples from it

Explanation

VAEs optimize a reconstruction term plus a regularizer (via latent distributions) to enable sampling.

Question 13

In pure text-based scenarios, which AI model generally offers faster performance?

A. Learn 𝑝(𝑦∣𝑥) to separate classes with minimal classification error
B. Compress datasets without modeling structure for downstream generation
C. Encode fixed symbolic rules for deterministic inference
D. Learn a data distribution to synthesize coherent new samples

Answer

D. Learn a data distribution to synthesize coherent new samples

Explanation

Generative models learn distributions/patterns and can produce novel outputs after training.

Question 14

What is one major advantage of using AI in cybersecurity modernization?

A. AI prevents all zero-day attacks without any limitations
B. AI enables real-time detection and response to evolving threats
C. AI makes security tools obsolete and unnecessary
D. AI eliminates the need for trained security professionals

Answer

B. AI enables real-time detection and response to evolving threats

Explanation

AI improves detection and response speed against new threats.

Question 15

Which mapping aligns a generative-AI capability with a cybersecurity application?

A. Insider risk — disable access control to reduce false positives
B. Attack hunting — scan massive data and uncover risks
C. Incident response — increase dwell time to ensure thoroughness
D. Intelligence analysis — suppress summaries to avoid bias

Answer

B. Attack hunting — scan massive data and uncover risks

Explanation

Attack hunting leverages generative/analytic synthesis to highlight suspicious patterns at scale.

Question 16

Which feature makes transformer models particularly suitable for cybersecurity applications involving large-scale sequential data?

A. They use convolutional layers to process images
B. They rely entirely on manual feature engineering
C. They apply self-attention, enabling efficient processing and parallelization
D. They store sensitive information within model weights

Answer

C. They apply self-attention, enabling efficient processing and parallelization

Explanation

The self-attention mechanism allows transformers to handle complex data relationships and scale efficiently, crucial for security systems analyzing vast data streams.

Question 17

Which description matches a modern open LLM release with multiple parameter sizes?

A. A closed model available only as a managed API with a single size
B. A rules engine with no learned parameters
C. An open family offered in 8B and 70B variants
D. A speech codec model specialized only for audio compression

Answer

C. An open family offered in 8B and 70B variants

Explanation

This aligns with recent open releases providing several sizes.

Question 18

What is a core limitation to account for when operationalizing LLMs in high-stakes workflows?

A. Guaranteed immunity to misleading outputs due to pretraining scale
B. Lack of any need for observability or oversight
C. Inability to generate any long-form content
D. Possibility of inaccurate responses that can mislead users

Answer

D. Possibility of inaccurate responses that can mislead users

Explanation

Inaccuracies can erode trust when not subject to verification.

Question 19

What is a key difference between decoder-only and encoder-decoder transformer architectures?

A. Encoder-decoder models do not generate any output
B. Decoder-only models use convolutional layers for input processing
C. Decoder-only models predict the next token using only past context, while encoder-decoder models process input and generate output separately
D. Both architectures are only used for language translation tasks

Answer

C. Decoder-only models predict the next token using only past context, while encoder-decoder models process input and generate output separately

Explanation

Decoder-only (GPT) models excel at generation tasks, whereas encoder-decoder architectures (T5, BERT) are suited for tasks like translation and Q&A.