Cisco AITECH 810-110: Generative AI Models

Topic snapshot

Sample questions

These original IT Mastery practice questions are aligned to this topic area. Use them for self-assessment, scope review, and deciding what to drill next.

Question 1

Topic: Generative AI Models

A team is building a RAG assistant for internal support engineers. The source set contains long troubleshooting guides, release notes, and security advisories. Answers must retrieve the most relevant procedure, respect document access level, and prefer content for the product and software version named in the user question. Which indexing approach best maps to these requirements?

Options:

• A. Split every document into equal 100-character chunks only

• B. Chunk by sections, add metadata, and index embeddings with filters

• C. Store generated summaries only in a keyword index

• D. Embed each full document as one vector without metadata

Best answer: B

Explanation: RAG retrieval quality depends on making source content retrievable at the right granularity and with the right constraints. For long technical documents, section- or topic-based chunks usually preserve enough context for a useful answer without burying the relevant text inside an entire document. Metadata such as product, software version, document type, effective date, and access level lets the retriever filter or rank results before generation. Embedding the chunks supports semantic retrieval, while metadata filtering helps enforce security and relevance requirements. The key is to index the source material in a way that matches how users ask questions and how the organization must control the data.

• Whole-document vectors make precise retrieval harder because one embedding must represent many unrelated procedures.
• Tiny fixed chunks can break steps, warnings, and prerequisites apart, reducing answer quality.
• Summary-only indexing may omit critical procedural details and does not satisfy access-level filtering by itself.

Question 2

Topic: Generative AI Models

A support operations team needs a model to summarize confidential customer tickets in English and Spanish. Security requires processing inside a private VPC, and governance requires a license that permits internal commercial use. Which approach best maps to the model-card facts?

Options:

• A. Use Model Beta through its hosted API.

• B. Translate tickets and use Model Gamma.

• C. Use Model Delta with prompt redaction.

• D. Deploy Model Alpha as a self-hosted private VPC service.

Best answer: D

Explanation: Model-card interpretation means matching the stated requirements to documented facts, not choosing the most general or newest-sounding model. The team needs text summarization, English and Spanish support, internal commercial use, and processing inside a private VPC. Alpha is the only card that satisfies all four: text task fit, both languages, Apache-2.0 licensing, and a supported self-hosted container. The other models each miss a hard requirement, such as license, deployment location, language support, or prompt data handling. A practical model selection should treat security and governance requirements as constraints, not preferences.

• Hosted-only processing fails because the scenario requires confidential ticket processing inside a private VPC.
• Research-only licensing fails because governance requires internal commercial use to be permitted.
• Translation workaround does not fix the vendor-hosted processing constraint for confidential data.
• Prompt redaction alone does not satisfy the private-processing requirement when prompts are logged by the hosted service.

Question 3

Topic: Generative AI Models

A support chatbot uses RAG to answer questions about internal incident reports. A user asks, “What was the root cause and final remediation for incident INC-4472?” The retriever returns this evidence:

Top retrieved passages for INC-4472
1. Status update: database latency increased at 14:05 UTC.
2. Mitigation note: traffic was shifted to the standby cluster.
3. Postmortem index: final RCA document exists, but content was not retrieved.

Which response-management approach should the chatbot use?

Options:

• A. Infer the root cause from the latency and mitigation notes

• B. Answer with the most likely remediation from similar incidents

• C. State only supported facts and disclose the missing RCA evidence

• D. Refuse to answer because the retrieval is incomplete

Best answer: C

Explanation: RAG grounding depends on the quality and completeness of retrieved evidence. In this case, the passages support that database latency occurred and traffic was shifted, but they do not contain the final root-cause analysis or final remediation. A good response-management approach should separate known facts from unknowns, explicitly say that the RCA document was not available in the retrieved context, and avoid filling gaps with plausible guesses. The chatbot can offer to retrieve the missing postmortem or escalate to a source of record. The key point is not to treat partial retrieval as full evidence.

• Inference from symptoms fails because latency plus failover does not prove the underlying root cause.
• Similar incident guessing fails because prior patterns are not evidence for this incident’s final remediation.
• Full refusal is too strict because the chatbot can still provide the supported facts while naming the evidence gap.

Question 4

Topic: Generative AI Models

A development team uses a cloud-hosted code-generation model to speed up creation of a customer-facing payment API. The generated code compiles and passes the model’s suggested unit tests, but the API will handle sensitive customer data and must be maintainable by the team after release. What is the best technical decision before promoting the code to production?

Options:

• A. Ask the model to certify the code as production-ready

• B. Run human code review, security testing, and maintainability assessment

• C. Replace the cloud model with a larger model and deploy the output

• D. Promote it because it compiles and includes unit tests

Best answer: B

Explanation: Code-generation models are useful for drafting, refactoring, test suggestions, and rapid prototyping, but they do not replace production readiness controls. For a sensitive customer-facing API, compiling and passing generated tests are not enough. The team still needs human review for architecture and maintainability, security review for data handling and vulnerabilities, and independent testing that reflects real requirements. The model’s output should be treated as a candidate implementation, not as final approval evidence.

The key takeaway is that AI-assisted code can speed development, but release decisions remain governed by engineering, security, and accountability practices.

• Compile-only approval misses security, privacy, and maintainability risks that generated tests may not cover.
• Model self-certification is not reliable evidence because the same system that generated the code cannot independently approve it.
• Larger-model substitution may improve output quality, but it does not remove the need for review and testing.

Question 5

Topic: Generative AI Models

A team needs an LLM to produce an auditable summary of a long design review. The summary must consider all sections, but the first model call is over the context limit.

Exhibit: Token budget note

What is the best next action?

Options:

• A. Paste the document and ask the model to ignore excess text

• B. Chunk the source and summarize or extract facts in stages

• C. Increase temperature to make the model more concise

• D. Remove the system instructions and examples entirely

Best answer: B

Explanation: Context budgeting means accounting for every token that must share the model context window: system instructions, examples, source material, and the generated response. Here, only about 25,000 tokens remain for source content after reserving 4,000 for instructions and 3,000 for output, but the source is 41,000 tokens. The practical action is to split the source into manageable chunks, extract or summarize key facts from each chunk, then synthesize a final answer from those intermediate outputs. This keeps each call within the context window while still covering the full document. If source traceability matters, keep section identifiers or citations with each chunk summary.

• Temperature tuning affects randomness, not how much source text fits in the context window.
• Ignoring excess text is unreliable because tokens beyond the window are truncated or unavailable to the model.
• Removing instructions may save tokens, but it weakens task control and still may not create enough room for the full source.

Question 6

Topic: Generative AI Models

A team is selecting a foundation model from a repository for a revenue-generating customer support chatbot. The organization requires commercial use rights, private deployment with no hosted inference calls, and a complete model card for governance review.

Exhibit: Repository candidates

Which interpretation is best supported by the exhibit?

Options:

• A. Atlas-7B is the only candidate that meets all stated constraints.

• B. Beacon-7B is acceptable because it can run privately.

• C. Civic-8B is acceptable because its license permits commercial use.

• D. Delta-8B is acceptable because it has downloadable weights.

Best answer: A

Explanation: Model selection from a repository is not based only on model capability. The license must permit the intended use, the deployment method must match operational and privacy constraints, and the model card must provide enough information for governance review. In this scenario, the chatbot is revenue-generating, must run privately, and needs documented training sources, evaluations, and limitations. Atlas-7B is the only listed model that satisfies all three. A model that fails any one of these constraints should not be selected without resolving that gap first.

• Private deployment only is not enough for Beacon-7B because the license blocks commercial use.
• Commercial license only is not enough for Civic-8B because hosted API inference violates the private deployment requirement.
• Downloadable weights only is not enough for Delta-8B because the incomplete model card does not meet governance needs.

Question 7

Topic: Generative AI Models

A network operations team wants an LLM assistant to answer incident-review questions from 18 months of internal tickets and runbooks. The total text is far beyond the model context window, the content is company-confidential, and answers must cite the source ticket or runbook section. What is the BEST technical decision?

Options:

• A. Chunk and embed the documents in an internal vector store

• B. Paste the full archive and request a shorter answer

• C. Use the largest available public cloud model

• D. Summarize the archive once, then discard the originals

Best answer: A

Explanation: Large-context tasks should avoid sending an entire corpus to the model. For confidential incident records that require citations, the strongest approach is to split documents into manageable chunks, store embeddings in an approved internal vector store, and retrieve only the most relevant chunks with metadata at question time. This keeps token usage within the context window, reduces latency and cost, protects sensitive data better than public upload, and preserves source references for grounding. A one-time summary can help exploration, but it may remove details needed for accurate, cited answers.

• Full archive prompting fails because a shorter answer request does not reduce the input tokens already exceeding the context window.
• Discarding originals fails because summaries can omit evidence needed for citations and later detailed questions.
• Largest public model fails because capacity alone does not address confidentiality or source-grounded retrieval.

Question 8

Topic: Generative AI Models

An enterprise AI team must choose a model artifact from a model hub to summarize confidential support tickets. Requirements: commercial use, English and Spanish support, deployment inside a private Kubernetes cluster with available GPUs, and governance evidence for intended use, evaluation, and limitations.

Which artifact should the team select?

Options:

• A. Select Artifact C

• B. Select Artifact B

• C. Select Artifact D

• D. Select Artifact A

Best answer: B

Explanation: Model hub selection should use the model card and deployment details together. A suitable artifact needs documentation that matches the scenario: intended use, evaluation evidence for the required languages and task, known limitations, license or usage terms, and an operational deployment path that satisfies privacy and governance constraints. A model that is technically capable but hosted only as an external API does not meet a private-cluster requirement if prompt retention violates policy. A downloadable model is also insufficient if its intended use, evaluation, or limitations are missing or mismatched. The key is to select the artifact whose documentation and deployment profile are adequate for the actual scenario, not merely the one with the broadest capability label.

• Research-only artifact fails because commercial use is required and evaluation evidence is missing.
• Hosted-only artifact fails because the private Kubernetes deployment and prompt-retention constraints are not met.
• Marketing-generation artifact fails because its intended use and undocumented limitations do not support governed ticket summarization.

Question 9

Topic: Generative AI Models

A team is selecting an LLM from a model hub for an internal incident-summary assistant. The assistant will process confidential support tickets, must run in the company’s private environment, and must produce grounded summaries with low hallucination risk. One candidate model advertises the highest public benchmark score on a general reasoning leaderboard, but its model card has limited information about training data, license terms, and deployment requirements. What is the best technical decision?

Options:

• A. Choose the smallest locally runnable model regardless of quality

• B. Run a representative private pilot and review the model card gaps

• C. Select the model because the benchmark score is highest

• D. Use the model only for nonconfidential tickets without further review

Best answer: B

Explanation: Model hub benchmark claims are useful screening signals, not final selection evidence. For this use case, the deciding factors include whether the model can be hosted privately, whether its license permits the intended use, whether the model card discloses enough risk information, and whether it performs well on representative incident-summary tasks. A practitioner should validate the model with sanitized or controlled internal examples, evaluate hallucination and grounding behavior, and confirm operational constraints before adoption. A high general reasoning score does not prove suitability for confidential support-ticket summarization in a private environment.

• Leaderboard-only choice fails because a general benchmark does not validate privacy, licensing, deployment, or domain-specific summarization quality.
• Nonconfidential-only use reduces exposure but still skips required model-card, license, and operational-fit review.
• Smallest local model satisfies hosting pressure but ignores whether the model can meet the required summary quality and grounding needs.

Question 10

Topic: Generative AI Models

A RAG assistant must answer a customer-facing question only when retrieved context is relevant, current, complete, and sufficient. Today is April 15, 2026. The user asks: “Can we tell EU customers that AcmeChat supports SAML SSO and SCIM provisioning this quarter?”

Retrieved context:

Which approach best meets the grounding requirement?

Options:

• A. Answer yes because SAML is global and SCIM is generally available.

• B. Answer no because the older roadmap says EU SCIM is only planned.

• C. Ignore the retrieved context and ask the model to infer the likely rollout status.

• D. Answer that SAML is supported, but retrieve current EU SCIM evidence before making a SCIM claim.

Best answer: D

Explanation: Grounded RAG responses depend on retrieved evidence being relevant, current, complete, and sufficient for the exact question. Here, the SAML source is directly relevant and states all-region support. The SCIM evidence does not establish EU support for the current quarter: the January release note covers only US tenants, and the older roadmap is not current confirmation. The March blog post is current but irrelevant. A safe response can state the supported SAML fact, but the assistant should not make a customer-facing SCIM claim until it retrieves a current EU-specific release note, admin guide, or policy source.

• Overgeneralizing SCIM fails because US tenant availability does not prove EU tenant availability.
• Relying on old roadmap data fails because a planned future release is not current confirmation.
• Inferring rollout status fails because grounding requires evidence, not model speculation.

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