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| Section | Weight | Objectives |
|---|---|---|
| AI Governance, Risk and Compliance | 19% | - Governance frameworks and policies
|
| Securing AI Systems | 40% | - Security controls for AI systems
|
| AI-assisted Security | 24% | - AI for threat detection and response
|
| Basic AI Concepts Related to Cybersecurity | 17% | - AI applications in security
|
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108. Frage
A human resources officer is using AI to evaluate resumes and help select candidates that meet minimum criteria. To improve the results, the human resources officer adjusts the query parameters and includes an example resume that matches a successful candidate.
Which of the following best describes this query?
Antwort: A
Begründung:
Basic Concept: Prompting techniques determine how effectively an LLM is guided to produce desired outputs. Providing a single example within a prompt is a well-established technique known as one-shot prompting, which leverages in-context learning. CompTIA SecAI+ Study Guide covers prompting strategies under basic AI concepts.
Why C is Correct: One-shot prompting involves providing exactly one example of the desired input-output format within the prompt to guide the model ' s responses. In this scenario, the HR officer includes one example resume matching a successful candidate to show the model what a qualifying candidate looks like.
This single example instructs the model on the evaluation criteria through demonstration rather than explicit description.
Why A is Wrong: Distillation is a model training technique where a smaller student model is trained to replicate the behavior of a larger teacher model. It is a model compression methodology, not a prompting technique used at query time.
Why B is Wrong: A prompt template is a reusable, structured format for prompts with placeholders that can be filled in for different queries. While templates may incorporate examples, the term specifically describes the structural framework, not the act of providing a single example.
Why D is Wrong: A system role defines the AI model ' s persona, context, and behavioral guidelines at the system level before user interaction begins. It sets the model ' s overall behavior, not a specific technique of providing examples within queries.
109. Frage
A cybersecurity analyst wants to choose a machine learning (ML) model to classify log entries while providing the best explainability.
Which of the following models should the analyst use?
Antwort: C
Begründung:
Basic Concept: Different ML model architectures offer varying degrees of explainability. In cybersecurity, understanding why a model classified a log entry as malicious or benign is critical for analyst trust, investigation, and regulatory compliance. CompTIA SecAI+ covers model explainability under responsible AI and basic AI concepts.
Why C is Correct: Decision trees are inherently interpretable models that classify data through a series of transparent if-then rules. Every classification decision can be traced through the exact path of conditions that led to it, showing precisely which log entry features triggered the classification. Analysts can read and understand the decision path, making decision trees the gold standard for explainable ML classification in security applications where understanding the reason for a classification is as important as the classification itself.
Why A is Wrong: Large language models are complex transformer architectures with hundreds of billions of parameters. They function as black boxes - their internal decision-making processes are not human- interpretable, making them poor choices when explainability is the primary requirement.
Why B is Wrong: Neural networks are non-linear black box models. While they can achieve high classification accuracy, their multi-layer architecture makes it extremely difficult to explain why specific decisions were made in human-understandable terms.
Why D is Wrong: Generative adversarial networks are designed for generating synthetic data, not for classification tasks. They consist of competing generator and discriminator networks and are fundamentally unsuitable for log entry classification with explainability requirements.
110. Frage
Which of the following describes the number of training cycles used in an AI model for threat detection?
Antwort: C
Begründung:
Basic Concept: Training an AI model involves repeatedly exposing it to training data so it can learn optimal parameters. The terminology for training cycles is fundamental to understanding AI model training processes.
CompTIA SecAI+ Study Guide covers core AI training concepts under basic AI concepts.
Why D is Correct: An epoch refers to one complete pass through the entire training dataset. When a model trains for multiple epochs, it sees each training example multiple times, allowing it to refine its parameters progressively. The number of training epochs is a key hyperparameter that directly affects model performance
- too few and the model underfits; too many and it may overfit. For a threat detection model, specifying epochs controls how thoroughly the model has learned from the training data.
Why A is Wrong: k-means clustering is an unsupervised machine learning algorithm that groups data points into k clusters based on feature similarity. It is a data clustering algorithm, not a term describing training cycles or iterations.
Why B is Wrong: Tokens are the discrete units that LLMs use to process text inputs and outputs. Token count measures text processing volume and LLM utilization, not the number of times a model has processed its training dataset.
Why C is Wrong: Temperature is an inference parameter that controls the randomness or creativity of an LLM ' s output generation. Higher temperature produces more varied outputs; lower temperature produces more deterministic responses. It is not related to training cycles or iterations.
111. Frage
A healthcare company deploys an AI chatbot that implements retrieval-augmented generation (RAG) using the company ' s historical data set. The chatbot output contains patient information.
Which of the following is the most effective technique to mitigate this vulnerability?
Antwort: B
Begründung:
Basic Concept: When an AI chatbot powered by RAG retrieves and outputs sensitive patient information such as names, medical histories, or identifiers, the risk of Protected Health Information (PHI) disclosure must be mitigated. CompTIA SecAI+ Study Guide covers data protection techniques for AI systems handling sensitive health data.
Why A is Correct: Masking replaces sensitive data values such as patient names, dates of birth, medical record numbers, and diagnoses with redacted or anonymized equivalents in the chatbot ' s output. Even if the RAG system retrieves records containing patient information, masking ensures that the sensitive fields are obscured before the response is presented to the user. This directly prevents PHI disclosure while allowing the chatbot to provide useful responses based on the underlying data patterns.
Why B is Wrong: Classification involves categorizing data by its sensitivity level such as public, internal, confidential, or restricted. While it identifies which data requires protection, classification alone does not transform or obscure the sensitive values in chatbot outputs.
Why C is Wrong: Data minimization is a privacy principle that limits data collection to only what is necessary for the specified purpose. While valuable as a design principle when building the RAG knowledge base, it is a data governance strategy rather than a technical output control that can be applied to mitigate existing PHI disclosure in chatbot responses.
Why D is Wrong: Normalization is a data processing technique that scales numerical values to a standard range or standardizes data formats. It is a preprocessing step for improving model training efficiency, not a data protection technique for preventing patient information disclosure in AI outputs.
112. Frage
A disgruntled employee changed the company policies that a chatbot references in order to create confusion and disrupt the business. Which of the following AI-generated vulnerabilities is the employee exploiting?
Antwort: D
Begründung:
Altering the source data (policy content) that the chatbot relies on corrupts its knowledge and behavior, which is characteristic of data poisoning attacks.
113. Frage
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