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2026 Statistics
Key Facts: eAIS Exam
5
Official Domains
INE Security eAIS page
30%
Secure Design Weight
INE Security
25%
Abuse & Exploitation Weight
INE Security
3 yrs
Credential Validity
INE Security
180 days
Voucher Expiration
INE Security
Practical
Exam Emphasis
Auto-graded individual cert exam
INE Security eAIS (AI Systems Security Specialist) validates practical skills to identify, test, and secure AI systems across five weighted domains: AI/LLM Foundations for Security (15%), AI Abuse & Exploitation (25%), Secure AI Design & Controls (30%), AI Security Testing & Validation (20%), and Safe Operational Use in IT/Security & SDLC (10%). Candidates need an INE subscription plus an eAIS exam voucher; vouchers expire in 180 days and include one free retake within 14 days if the first attempt fails. Credentials are valid for three years. Official item count, duration, and passing score are not published. This free bank provides 100 English MCQs as study prep—not an official hands-on exam simulation.
Sample eAIS Practice Questions
Try these sample questions to test your eAIS exam readiness. Each question includes a detailed explanation. Start the interactive quiz above for the full 100+ question experience with AI tutoring.
1In a typical LLM application stack, which component primarily converts user text into numerical vectors used for similarity search?
A.The CI/CD runner that deploys model weights
B.The embedding model used by the retrieval layer
C.The reverse proxy that terminates TLS
D.The safety filter that blocks disallowed topics
Explanation: Embeddings map text into dense vectors so a retrieval layer can find similar documents for RAG. Safety filters act on content policy, reverse proxies handle transport security, and CI/CD deploys code or models but does not produce retrieval vectors. Security practitioners should treat embedding pipelines as sensitive data-processing stages because source text and vector stores can expose confidential content.
2What is the primary security purpose of guardrails in an LLM-powered system?
A.To encrypt model weights at rest exclusively
B.To constrain model inputs/outputs and block unsafe or policy-violating behavior
C.To replace authentication for all end users
D.To increase the model’s parameter count for better accuracy
Explanation: Guardrails enforce policy on prompts and responses (for example blocking jailbreaks, PII leakage, or disallowed actions). They do not replace identity controls or solely encrypt weights. Understanding where guardrails sit in the request path helps identify bypasses when attackers inject instructions that the model follows before or after filtering.
3Which statement best describes retrieval-augmented generation (RAG) from a security perspective?
A.The model is fine-tuned weekly on all enterprise emails automatically
B.Users share a single global chat history with no isolation
C.The model runs only offline with no network or tool access
D.The application retrieves relevant documents and injects them into the model context before generation
Explanation: RAG retrieves documents (often via embeddings) and adds them to the prompt/context so the model can ground answers. That creates new risks: over-permissioned retrieval, poisoned documents, and sensitive chunks leaking into answers or logs. Fine-tuning, offline-only, and shared-history designs are different architectures with their own risks.
4In an agentic LLM workflow, what do 'tools' typically enable the model to do?
A.Call external functions such as APIs, search, code execution, or ticket systems
B.Bypass TLS inspection on the corporate network
C.Delete the model’s training dataset at runtime
D.Only rephrase previous messages without external effects
Explanation: Tools give the model side effects beyond text generation—querying databases, opening tickets, running code, or calling SaaS APIs. From a security view, tools expand the blast radius of prompt injection and over-permissioning. Tool identity, arguments, and authorization must be designed carefully.
5Which data element is MOST likely to appear in both application logs and model context for an enterprise chatbot?
A.Kernel page tables from production hosts
B.BGP routing tables from the core network
C.User prompts and assistant responses
D.Hardware TPM endorsement keys for every laptop
Explanation: Chat systems almost always log or retain prompts and responses for debugging, analytics, or compliance. Those transcripts may contain secrets, PII, or proprietary data and become a secondary exposure channel. Security reviews should cover logging retention, redaction, access control, and whether full prompts are stored unnecessarily.
6An LLM app sends user prompts to a third-party model API, retrieves internal wiki chunks via embeddings, and may call a calendar tool. Where is sensitive employee data MOST likely to leave the corporate trust boundary first?
A.When the model’s tokenizer merges rare subwords
B.When the CSS theme is loaded from a CDN
C.When prompts and retrieved context are sent to the third-party model API
D.When the browser renders Markdown bold text
Explanation: Outbound calls to external model providers transmit prompt text and often retrieved context. That is a primary egress path for secrets and PII. Tokenization and UI rendering are local processing steps; CDN CSS is unrelated. Practitioners should inventory every outbound model/tool call as a data-flow risk.
7Why can vector embeddings of internal documents create a confidentiality risk even if raw files stay in a private object store?
A.Similarity search and inversion-style research can still leak information about the underlying text, and over-broad retrieval can surface sensitive chunks to unauthorized users
B.Embeddings mathematically guarantee irreversible one-way hashes like bcrypt
C.Embeddings always include full plaintext of every document in the response header
D.Embeddings disable all access-control lists by design
Explanation: Embeddings are not simple one-way password hashes. They preserve semantic structure, enable retrieval of sensitive passages, and in some research settings can leak information about source text. Combined with weak authorization on the vector store or retriever, they become a high-value exposure surface.
8Which component is BEST described as the 'system prompt' in an LLM application?
A.The end user’s latest free-text question only
B.The SSL certificate presented by the API gateway
C.Developer/operator instructions that set role, policies, and tool rules before user messages
D.The GPU firmware version on the inference host
Explanation: System (or developer) prompts establish durable instructions: persona, safety rules, tool policies, and output format. Attackers often try to override them via prompt injection. Defenders should treat system prompts as sensitive configuration, version them, and not rely on them alone for hard security boundaries.
9A RAG pipeline retrieves the top-k documents and concatenates them into the model context window. What security implication follows from a large context window?
A.More retrieved content increases the chance of including sensitive, irrelevant, or attacker-controlled documents that influence the answer
B.Large windows force the model offline automatically
C.Larger windows eliminate all injection risk permanently
D.Context windows only affect image models, not text LLMs
Explanation: Bigger context means more retrieval surface: more chance of oversharing, poisoned content, or cross-tenant leakage if filters fail. Injection risk is not eliminated. Designers should combine authorization-aware retrieval, ranking hygiene, and least-privilege document scopes—not only larger windows.
10Which statement correctly distinguishes model training data from RAG knowledge bases for security risk analysis?
A.Training data is always public; RAG is always encrypted end-to-end
B.RAG knowledge can never contain PII by definition
C.Both are identical; training data is always re-read live on every user request
D.Training data shapes model parameters offline; RAG knowledge is retrieved at inference time and can change without retraining
Explanation: Training/fine-tuning bakes patterns into weights; RAG attaches external knowledge at runtime. Runtime retrieval creates live authorization, poisoning, and exfiltration risks distinct from training-data leakage. Security reviews must cover both supply chains but with different controls.
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