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Exam Review

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 review concepts for the eAIS exam. 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.

About the eAIS Exam

The AI Systems Security Specialist (eAIS) is a practical, role-aligned certification from INE Security that validates foundational skills to identify, test, and secure AI-powered systems. Domains cover AI/LLM foundations for security (15%), AI abuse and exploitation (25%), secure AI design and controls (30%), AI security testing and validation (20%), and safe operational use in IT/security and the SDLC (10%). Topics include models, retrieval, tools, guardrails, prompt injection, jailbreaks, data exfiltration, tool misuse, agentic workflow abuse, RAG/embedding exposure, access control, validation/monitoring, residual risk, and human oversight in SOC and development workflows. The official exam emphasizes practical real-world capability with auto-graded results; this free bank is an English-language MCQ study adaptation and is not an official hands-on simulation.

Exam sponsor: INE Security. The requirements and fees below concern the certification or admission exam, separate from our free practice resources.

Assessment

Question count not published by the exam provider

Time Limit

Not published by INE Security

Passing Score

Not published by INE Security

Exam / Certification Fees

INE subscription plus eAIS exam voucher

Exam sponsor website

Reported exam pass rate: Not published. INE Security does not publish an official eAIS pass rate. Exam sponsor website

Fees, eligibility, and exam policies can change. Confirm them with the exam sponsor before applying or paying.

Our practice resources: topics covered

We aim to reflect publicly available exam outlines and topic information in our study resources. Coverage, format, and difficulty may differ from the actual exam, and we cannot guarantee that every detail is accurate or current. Confirm exam requirements, fees, and policies with the official exam sponsor.

15%

AI/LLM Foundations for Security

Models, retrieval layers, tools, guardrails, and data-flow risks for prompts, embeddings, and stored information

25%

AI Abuse & Exploitation

Prompt injection, jailbreaks, data exfiltration, tool misuse, agentic workflow abuse, and dependency exploitation

30%

Secure AI Design & Controls

Access control, prompt/data handling, safe tool usage, validation, monitoring, and abuse prevention

20%

AI Security Testing & Validation

Security testing for injection, leakage, and tool misuse; documenting findings, validating fixes, residual risk

10%

Safe Operational Use in IT/Security & SDLC

Safe AI use in SOC and development workflows with validation, data handling, and human oversight

Preparing for the eAIS Exam

What You Need to Know

  • Passing score: Not published by INE Security
  • Assessment: Question count not published by the exam provider
  • Time limit: Not published by INE Security
  • Exam / certification fees: INE subscription plus eAIS exam voucher Official sources

Using Our Practice Resources

  • Work through all 100 available questions
  • Review every answer and explanation
  • Track weak areas and revisit them
  • Use our AI tutor for tough concepts

eAIS: Suggested Study Strategy

1Prioritize Secure AI Design & Controls (30%) and AI Abuse & Exploitation (25%) — together they are more than half the blueprint
2Map every attack (injection, jailbreak, exfil, tool misuse) to a concrete control: least privilege, validation, monitoring, or human-in-the-loop
3Practice tracing data flow through prompts, embeddings, retrieval stores, tools, and logs — foundation questions test exposure points
4For testing domains, learn how to document findings, retest fixes, and state residual risk clearly
5Use this MCQ bank for concept checks, then complete INE hands-on labs before the practical auto-graded exam

Frequently Asked Questions

What is the INE eAIS certification?

eAIS (AI Systems Security Specialist) is a practical, role-aligned certification from INE Security that validates foundational skills to identify, test, and secure AI-powered systems. It sits between general AI literacy and advanced AI red teaming, targeting IT, SOC, DevOps, and security practitioners who need security-first AI readiness rather than deep ML research skills.

Is this practice bank the same format as the official eAIS exam?

No. The official eAIS exam is a practical, role-aligned individual certification exam with auto-graded results that emphasizes real-world capability. This OpenExamPrep bank is an English-language multiple-choice study adaptation covering topics from the official domain weights. It is not an official hands-on simulation and does not replace INE labs or the actual exam experience.

How much does the eAIS exam cost?

You need both an INE subscription and an eAIS exam voucher. The official cert page states the voucher is available with an INE Premium subscription and also offers eAIS+ Prep bundle options; it does not publish a single fixed public USD list price for the voucher alone. Budget for an INE subscription plus the eAIS exam voucher.

What domains does the eAIS exam cover?

The official blueprint has five 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%). Secure design is the largest domain; safe operational use is the smallest.

What is the eAIS retake and validity policy?

Exam vouchers expire 180 days after purchase. If you do not pass on the first attempt, one free retake is included and must be completed within 14 days of the first attempt, still before the voucher expires. Passing credentials are valid for three years from the date awarded, with renewal options through INE.

Who should take eAIS?

INE positions eAIS for IT support, help desk, system administrators, junior security analysts, SOC teams, DevOps/platform/cloud ops, students, and career-changers. Anyone may attempt the exam, but it is designed for practitioners expected to identify AI-specific risk, apply baseline controls, and use AI safely—not primarily for AI research engineers.

Does INE publish the number of questions, time limit, or passing score?

No. As of the official eAIS certification page, INE does not publish a fixed question count, exam duration, or passing score. Results are auto-graded and typically delivered within a few hours, with a score report showing performance by domain. Treat study goals as domain mastery rather than chasing an unpublished cut score.