How to Use This Quick Reference
This independent Quick Reference is for candidates preparing for the Cisco AI Technical Practitioner (810-110 AITECH) exam. Use it to review high-yield distinctions, scenario decision points, and common traps for the 810-110 AITECH exam.
For Cisco-style AI scenarios, do not stop at “which model is best.” Also identify:
- Business outcome and risk tolerance.
- Data type, quality, sensitivity, and ownership.
- AI approach: rules, ML, deep learning, generative AI, RAG, or agentic workflow.
- Placement: edge, campus, branch, data center, cloud, or hybrid.
- Network, security, observability, and operations impact.
- Governance, monitoring, rollback, and continuous improvement needs.
AI, ML, and Generative AI Terms
| Term | Exam-ready meaning | Common trap |
|---|
| Artificial intelligence | Systems that perform tasks associated with human intelligence, such as perception, reasoning, prediction, or generation | AI is broader than machine learning |
| Machine learning | AI technique where models learn patterns from data | ML is not always generative |
| Deep learning | ML using multi-layer neural networks | Requires more data/compute than many classical models |
| Generative AI | AI that creates text, code, images, audio, or other outputs | Output may be plausible but false |
| Foundation model | Large model trained on broad data and adaptable to many tasks | Not automatically safe, private, or accurate |
| Large language model | Foundation model optimized for language tasks | Does not “know” truth; predicts likely tokens |
| Token | Unit of text processed by a model | Token count affects context, latency, and cost |
| Context window | Maximum input/output tokens the model can consider in one interaction | Larger context does not guarantee better reasoning |
| Embedding | Numeric vector representing semantic meaning | Embeddings support similarity search, not exact truth |
| Vector database/index | Stores and searches embeddings by similarity | Retrieval quality depends on chunks, embeddings, metadata, and ranking |
| Inference | Using a trained model to produce predictions or outputs | Different from training or fine-tuning |
| Training | Learning model parameters from data | Usually more expensive and data-intensive than inference |
| Fine-tuning | Additional training on task/domain examples | Not the default fix for missing facts |
| Prompt engineering | Designing instructions and context for model output | Prompting cannot fully replace controls and evaluation |
| RAG | Retrieval-augmented generation; retrieves external content and injects it into prompt context | RAG can still hallucinate if retrieval or grounding is poor |
| Agentic AI | AI system that plans steps and uses tools/APIs to complete tasks | Tool authorization and auditability are critical |
| Hallucination | Confident but incorrect or unsupported output | Reduced by grounding, evaluation, and guardrails; not eliminated |
| Model drift | Degradation as real-world data changes | Requires monitoring and retraining/update strategy |
| Bias | Systematic unfairness or skew in data/model outputs | Can exist even with high overall accuracy |
| Explainability | Ability to understand why a model made a decision | More important in regulated, high-risk, or user-impacting decisions |
AI Workload Lifecycle
flowchart LR
A[Define business problem] --> B[Identify data sources]
B --> C[Prepare and label data]
C --> D[Choose AI approach]
D --> E[Train, configure, or prompt]
E --> F[Evaluate]
F --> G[Deploy]
G --> H[Monitor]
H --> I[Improve or retire]
I --> B
| Phase | Key questions | Exam focus |
|---|
| Problem framing | Is the goal prediction, classification, generation, search, summarization, automation, or anomaly detection? | Match AI method to outcome |
| Data sourcing | What data is available, trusted, labeled, current, and permitted for use? | Data quality and governance |
| Preparation | Clean, normalize, transform, label, split, deduplicate, and protect data | Avoid leakage and bias |
| Model or approach selection | Rules, classical ML, deep learning, LLM, RAG, fine-tuning, or agent? | Choose the simplest approach that meets requirements |
| Evaluation | What metric reflects the business risk? | Accuracy is not always enough |
| Deployment | Where should inference run? What latency, cost, privacy, and connectivity constraints exist? | Edge/data center/cloud tradeoffs |
| Operations | How will drift, failure, abuse, and infrastructure issues be detected? | MLOps/LLMOps and observability |
Choosing the Right AI Approach
| Scenario | Prefer | Why | Watch for |
|---|
| Known rules, stable logic, low ambiguity | Rules or automation | Deterministic, auditable, simpler | Do not add ML where rules are enough |
| Predict a numeric value | Supervised regression | Learns relationship between features and continuous target | Outliers and changing data distributions |
| Assign item to known category | Supervised classification | Learns from labeled examples | Class imbalance and false positive/negative cost |
| Discover groups without labels | Clustering | Finds natural groupings | Clusters may not map to business categories |
| Detect unusual behavior | Anomaly detection | Useful for operations, security, fraud, performance | Needs baseline and tuning |
| Optimize actions through feedback | Reinforcement learning | Learns policies from rewards | Can be complex and risky in production |
| Understand images/video | Computer vision / CNNs / vision transformers | Extracts visual patterns | Data labeling and edge compute requirements |
| Summarize or generate text | LLM / generative AI | Produces natural-language output | Hallucination, privacy, prompt injection |
| Answer using enterprise documents | RAG | Grounds answers in retrieved content | Poor chunking/retrieval causes wrong answers |
| Adapt tone, format, or domain behavior | Prompting or fine-tuning | Prompt first; fine-tune when examples are needed | Fine-tuning does not reliably add fresh knowledge |
| Execute multi-step tasks with tools | Agentic workflow | Plans, calls APIs, and uses memory/tools | Tool permissions, audit logs, and guardrails |
Model and Algorithm Selection
| Model/technique | Best fit | Strengths | Limitations/traps |
|---|
| Linear regression | Numeric prediction with linear relationships | Simple, explainable, fast | Poor for complex nonlinear patterns |
| Logistic regression | Binary or multiclass classification | Interpretable baseline | Name says regression but used for classification |
| Decision tree | Classification/regression with explainability | Easy to visualize | Can overfit |
| Random forest | General tabular prediction | Reduces overfitting vs single tree | Less interpretable |
| Gradient boosting | High-performing tabular prediction | Strong accuracy on structured data | Tuning and overfitting risk |
| k-nearest neighbors | Similarity-based classification/regression | Simple concept | Slow at scale; sensitive to feature scaling |
| Support vector machine | Classification with clear margins | Effective in some high-dimensional spaces | Less transparent; scaling concerns |
| k-means | Clustering into predefined number of groups | Fast and common | Must choose k; assumes roughly spherical clusters |
| DBSCAN | Density-based clustering | Finds irregular clusters and noise | Sensitive to parameters and density variation |
| Isolation forest | Anomaly detection | Good for outlier discovery | Needs tuning and validation |
| Neural network | Complex nonlinear patterns | Flexible | Requires more data, compute, and monitoring |
| CNN | Images/spatial data | Strong visual feature extraction | Training data and compute intensive |
| RNN/LSTM | Sequential/time-series data | Captures sequence patterns | Often replaced by transformer approaches for many language tasks |
| Transformer | Language, multimodal, sequence tasks | Parallelizable, strong context modeling | Compute, latency, and governance concerns |
| LLM | Text generation, summarization, reasoning assistance | Flexible natural-language interface | Probabilistic and may hallucinate |
Data Reference
Data Types
| Data type | Examples | AI handling notes |
|---|
| Structured | Tables, logs with fields, CRM records, metrics | Common for classical ML and analytics |
| Semi-structured | JSON, XML, event records, emails with metadata | Requires parsing and schema management |
| Unstructured | Text, images, audio, video, PDFs | Often needs embeddings, OCR, NLP, CV, or multimodal models |
| Time-series | Telemetry, sensor data, network metrics | Preserve time order; avoid random leakage |
| Graph data | Network topology, identity relationships, dependencies | Useful for relationship and path analysis |
| Streaming data | Live telemetry, alerts, events | Requires low-latency processing and monitoring |
Data Preparation Decisions
| Task | Purpose | Exam trap |
|---|
| Cleaning | Remove errors, duplicates, invalid values | Do not silently remove meaningful outliers |
| Normalization/scaling | Put numeric features on comparable scales | Especially important for distance-based methods |
| Encoding | Convert categories to numeric representation | Ordinal vs one-hot encoding matters |
| Tokenization | Break text into model-processable units | Token limits affect prompt design |
| Chunking | Split documents for retrieval | Chunks too small lose context; too large reduce precision |
| Labeling | Add target values/classes | Label quality often limits model quality |
| Train/validation/test split | Separate training, tuning, and final evaluation | Never tune on the test set |
| Cross-validation | Repeated train/validate cycles | Useful with limited data |
| Feature engineering | Create useful inputs from raw data | Can introduce data leakage |
| Deduplication | Remove repeated records | Duplicates across train/test inflate scores |
| Class balancing | Address rare classes | Accuracy can hide poor minority-class performance |
| Data masking | Protect sensitive data | Mask before using data in prompts or training when required |
Evaluation Metrics
| Use case | Metric | Meaning | When to prefer |
|---|
| Balanced classification | Accuracy = correct / total | Overall correctness | Classes are balanced and errors have similar cost |
| Imbalanced classification | Precision = TP / (TP + FP) | How many predicted positives are correct | False positives are costly |
| Imbalanced classification | Recall = TP / (TP + FN) | How many actual positives are found | False negatives are costly |
| Classification tradeoff | F1 = 2 × precision × recall / (precision + recall) | Balances precision and recall | Need one score for imbalanced classification |
| Threshold evaluation | ROC-AUC | Separability across thresholds | Compare classifiers independent of one threshold |
| Regression | MAE | Average absolute error | Easy to interpret |
| Regression | MSE/RMSE | Penalizes larger errors | Large mistakes are especially costly |
| Regression | R-squared | Variance explained | Not enough alone for operational fit |
| Clustering | Silhouette score | Cluster separation/cohesion | Internal clustering assessment |
| Ranking/retrieval | Top-k accuracy, MRR, NDCG | Quality of ranked results | Search, recommendation, RAG retrieval |
| Generative AI | Faithfulness/groundedness | Output supported by provided source | RAG and enterprise Q&A |
| Generative AI | Toxicity/safety score | Harmful or unsafe content risk | Public-facing or user-impacting systems |
| Operations | Latency, throughput, error rate | Runtime performance | Production readiness |
| Business | Cost per task, containment rate, analyst time saved | Outcome value | Executive and operational alignment |
Confusion Matrix Terms
| Term | Meaning | Example |
|---|
| True positive | Predicted positive and actually positive | Correctly flagged threat |
| False positive | Predicted positive but actually negative | Benign activity flagged as threat |
| True negative | Predicted negative and actually negative | Correctly ignored benign activity |
| False negative | Predicted negative but actually positive | Missed actual threat |
High-yield trap: A model can have high accuracy but poor recall if the positive class is rare.
Generative AI and RAG
RAG Pipeline
flowchart LR
A[Source documents] --> B[Clean and split into chunks]
B --> C[Create embeddings]
C --> D[Store in vector index]
E[User question] --> F[Embed question]
F --> G[Retrieve relevant chunks]
G --> H[Optional rerank/filter]
H --> I[Prompt with context]
I --> J[Generate answer]
J --> K[Evaluate, cite, log]
| RAG component | Purpose | Failure mode |
|---|
| Source selection | Choose trusted knowledge | Inaccurate or outdated sources cause bad answers |
| Chunking | Create retrievable units | Bad chunk size loses context or lowers precision |
| Embedding model | Convert text to vectors | Poor semantic match reduces retrieval quality |
| Vector index | Store and search embeddings | Missing metadata/filtering returns irrelevant content |
| Retriever | Finds candidate chunks | Low recall misses needed facts |
| Reranker | Improves ordering of results | Adds latency |
| Prompt template | Combines task, rules, and retrieved context | Weak instructions allow unsupported answers |
| Generator | Produces final response | May hallucinate if context is weak |
| Guardrails | Enforce safety, format, and policy | Overly broad guardrails block useful responses |
| Evaluation set | Tests answer quality | No eval set means quality changes go unnoticed |
Prompting, RAG, Fine-Tuning, or Training?
| Need | Best first choice | Why |
|---|
| Change response format or tone | Prompt engineering | Fastest and lowest complexity |
| Answer from current enterprise knowledge | RAG | Keeps knowledge external and updateable |
| Reduce hallucination with citations | RAG plus grounding rules | Model can reference retrieved sources |
| Teach a model specialized style or repeated task pattern | Fine-tuning | Learns behavior from examples |
| Add private facts that change often | RAG, not fine-tuning | Updating documents/index is easier than retraining |
| Build model for unique domain with large proprietary dataset | Training or deep fine-tuning | Only when simpler options cannot meet requirements |
| Enforce deterministic business logic | Tool/function call or rules engine | LLM text generation is probabilistic |
| Execute workflow across systems | Agent with tools and guardrails | Requires permissions, logging, and human approval for risky actions |
Prompt Engineering Patterns
| Pattern | Use | Example instruction |
|---|
| Role | Set context | “Act as a network operations assistant.” |
| Task | Specify required output | “Summarize the incident in five bullets.” |
| Constraints | Limit scope | “Use only the provided context.” |
| Format | Make output parseable | “Return JSON with fields: severity, cause, next_action.” |
| Few-shot examples | Show desired behavior | Provide examples of input and output |
| Chain-of-thought alternative | Ask for concise rationale or steps without exposing hidden reasoning | “Provide a brief justification.” |
| Grounding | Tie answer to sources | “Cite the source chunk ID for each claim.” |
| Refusal rule | Prevent unsafe output | “If context is insufficient, say you do not know.” |
Key Generative AI Parameters
| Parameter | Effect | Exam note |
|---|
| Temperature | Higher values increase randomness | Use lower values for consistency |
| Top-p | Samples from most probable token mass | Another creativity/control setting |
| Max tokens | Caps generated output length | Prevents runaway responses but may truncate |
| System message | High-priority behavioral instruction | Useful for policy and role constraints |
| Stop sequence | Ends generation at defined text | Helpful for structured outputs |
| Context length | Amount of input/output the model can process | More context can increase cost and latency |
Agentic AI Reference
| Component | Function | Control requirement |
|---|
| Planner | Breaks goal into steps | Limit scope and validate plans |
| Tools/functions | APIs, databases, scripts, ticketing, network automation | Least privilege and allowlists |
| Memory | Stores prior context or state | Retention and privacy controls |
| Orchestrator | Coordinates model, tools, and state | Logging and error handling |
| Human-in-the-loop | Human approval for sensitive actions | Required for high-impact changes |
| Guardrails | Policy, safety, schema, and action constraints | Test with adversarial inputs |
| Audit trail | Records prompts, tool calls, outputs, and approvals | Needed for troubleshooting and accountability |
Exam trap: An agent that can call tools is not just a chatbot. It becomes an automation system and must be governed like one.
Cisco-Oriented Architecture Decision Points
In Cisco-focused scenarios, map AI requirements to network, security, observability, collaboration, and data center design. The exam may describe AI in a branch, campus, data center, cloud, security operations, contact center, or network operations context.
| Domain | AI use cases | Design priorities | Watch for |
|---|
| Campus/branch | Local inference, smart cameras, user assistance, operational analytics | Latency, segmentation, device identity, bandwidth, physical constraints | Do not send sensitive data to cloud by default |
| Data center | Model hosting, inference clusters, training, vector databases, data pipelines | High-throughput fabric, east-west traffic, storage performance, telemetry | AI traffic can be bursty and bandwidth-intensive |
| Cloud/hybrid | Managed AI services, burst compute, SaaS integrations | Secure connectivity, identity, data residency, cost visibility | Cloud reduces operations burden but not governance responsibility |
| Edge | Low-latency decisions, disconnected operation, local privacy | Small models, accelerators, lifecycle management | Edge devices may have limited compute and update windows |
| Security operations | Alert enrichment, anomaly detection, triage, summarization | Explainability, audit logs, false positive control | AI can assist analysts but should not blindly auto-remediate high-risk events |
| Network operations | AIOps, telemetry correlation, root-cause assistance, predictive maintenance | Data quality, baselines, topology context, observability | Correlation is not causation |
| Collaboration/contact center | Transcription, summarization, virtual agents, sentiment, routing | PII handling, user consent where applicable, quality monitoring | Generated summaries must be validated for accuracy |
| Application operations | Code assistance, log analysis, incident summaries | Secure SDLC, secrets handling, prompt/data controls | Do not expose credentials or proprietary code without controls |
Infrastructure for AI Workloads
| Component | Training priority | Inference priority | Exam distinction |
|---|
| CPU | Data preprocessing, orchestration, lightweight models | Low-volume or simple inference | General purpose but slower for many deep learning tasks |
| GPU/accelerator | Parallel matrix operations, model training | High-throughput inference | Critical for deep learning performance |
| Memory | Large batches, model parameters, feature sets | Model size and context handling | Memory pressure causes failures or latency |
| Storage | Large datasets, checkpoints, artifacts | Model files, embeddings, logs | Throughput and data locality matter |
| Network | Distributed training, data movement, storage access | API calls, service-to-service traffic | AI clusters can generate heavy east-west traffic |
| Observability | Job metrics, utilization, failures | Latency, errors, quality, drift | Monitor both infrastructure and model behavior |
Placement Decisions
| Requirement | Prefer | Reason |
|---|
| Lowest latency near data source | Edge/local inference | Reduces round-trip time |
| Sensitive data should remain local | Edge or private data center | Limits data exposure |
| Large-scale model training | Data center or cloud with accelerators | Needs compute, storage, and high-speed networking |
| Variable demand | Cloud or elastic hybrid design | Scales with workload |
| Disconnected operation | Edge | Continues without reliable WAN |
| Centralized governance and shared services | Data center or cloud | Easier policy and lifecycle management |
| Cost-sensitive predictable workload | Compare on-premises/private vs cloud | Utilization and operations model matter |
Networking Considerations for AI
| Concern | Why it matters | Design response |
|---|
| East-west traffic | Distributed training and microservices exchange high volumes internally | Use high-throughput, low-latency fabric design |
| North-south traffic | Users, APIs, and cloud services access AI applications | Secure ingress/egress and policy enforcement |
| Latency | Affects interactive inference and real-time automation | Place inference near users/data when needed |
| Bandwidth | Large datasets, embeddings, and model artifacts are heavy | Plan data movement and storage locality |
| Jitter/packet loss | Can affect streaming analytics and interactive services | Use QoS and resilient paths where appropriate |
| Segmentation | AI systems may touch sensitive data and tools | Isolate workloads and enforce least privilege |
| Telemetry | AI operations need logs, metrics, traces, and network context | Collect end-to-end visibility |
| API connectivity | Agents and AI apps call tools/services | Secure APIs with authentication, authorization, and rate controls |
Security and Governance
AI Risk-to-Control Matrix
| Risk | Description | Controls |
|---|
| Prompt injection | User or document instructions manipulate model behavior | Input filtering, context isolation, tool allowlists, instruction hierarchy |
| Data leakage | Sensitive data appears in prompts, logs, outputs, or training data | Data classification, masking, encryption, access control, retention limits |
| Model hallucination | Unsupported or false output | RAG, citations, refusal rules, evaluation, human review |
| Data poisoning | Training or retrieval data is maliciously altered | Source validation, integrity checks, approval workflows |
| Model theft | Unauthorized access to model weights or endpoints | Strong IAM, network segmentation, monitoring |
| Adversarial input | Crafted input causes incorrect output | Robust testing, anomaly detection, guardrails |
| Bias/fairness issue | Outputs disadvantage groups or users | Representative data, bias testing, review process |
| Insecure tool use | Agent calls risky APIs or changes systems incorrectly | Least privilege, scoped tokens, human approval, audit logs |
| Over-permissioned service account | AI service has more access than needed | Role-based access control and periodic review |
| Unlogged decisions | No audit trail for AI-assisted actions | Prompt/output/tool-call logging with privacy controls |
| Shadow AI | Unapproved tools used with enterprise data | Policy, approved platforms, monitoring, user education |
Security Principles to Apply
| Principle | AI-specific application |
|---|
| Least privilege | Models, agents, vector stores, and pipelines get only required access |
| Zero Trust | Verify users, devices, services, and context before access |
| Defense in depth | Combine IAM, segmentation, encryption, logging, and guardrails |
| Secure by design | Build controls into the AI workflow, not after deployment |
| Human oversight | Require approval for high-impact, irreversible, or risky actions |
| Data minimization | Use only data required for the task |
| Separation of duties | Separate model development, approval, deployment, and monitoring roles |
| Continuous monitoring | Watch quality, safety, security, and infrastructure signals |
MLOps and LLMOps
| Practice | ML focus | LLM/GenAI focus |
|---|
| Versioning | Data, features, code, model artifacts | Prompts, model versions, retrieval corpora, vector indexes |
| Registry | Approved models and metadata | Approved models, prompt templates, guardrails |
| CI/CD | Test and deploy application/model pipeline | Test prompts, eval sets, safety checks, integrations |
| Monitoring | Accuracy, drift, latency, resource use | Groundedness, hallucination rate, toxicity, retrieval quality, latency |
| Rollback | Revert model/application version | Revert prompt, model, index, or guardrail version |
| Experiment tracking | Hyperparameters and metrics | Prompts, model settings, retrieval parameters, eval results |
| Approval workflow | Promote model to production | Approve model, data sources, tools, and policies |
| Incident response | Model failure or degraded quality | Unsafe output, data exposure, prompt injection, tool misuse |
Production Readiness Checklist
- Defined business owner and technical owner.
- Documented data sources, sensitivity, and allowed use.
- Baseline model or non-AI alternative compared.
- Evaluation metrics aligned to business risk.
- Security controls for identity, network, data, and APIs.
- Logging for prompts, responses, tool calls, and errors where appropriate.
- Monitoring for latency, cost, quality, drift, and abuse.
- Rollback plan for model, prompt, retrieval index, or application release.
- Human review path for high-impact decisions.
- User communication that AI output may need verification.
Compact Code and Workflow Snippets
Basic ML Evaluation Pattern
## Conceptual pattern: split, train, validate, test
X_train, X_test, y_train, y_test = split_data(X, y, stratify=y)
model.fit(X_train, y_train)
predictions = model.predict(X_test)
print(classification_report(y_test, predictions))
Exam points:
- Use stratified splitting for imbalanced classification when appropriate.
- Do not train on the test set.
- For time-series data, split by time rather than random order.
RAG Application Pattern
question = "What is the recommended remediation for this alert?"
query_vector = embed(question)
chunks = vector_index.search(query_vector, top_k=5, filters={"source": "approved"})
prompt = build_prompt(question=question, context=chunks, rule="Answer only from context.")
answer = llm.generate(prompt, temperature=0)
log_interaction(question, chunks, answer)
Exam points:
- Retrieval quality is as important as generation quality.
- Use metadata filters for source, tenant, sensitivity, and freshness.
- Low temperature improves consistency but does not guarantee correctness.
if user_is_authorized and action in approved_actions:
plan = agent.create_plan(request)
if plan.requires_change:
require_human_approval(plan)
result = agent.call_tool(plan.tool, scoped_token)
audit_log(plan, result)
else:
deny_request()
Exam points:
- Tool access requires authorization separate from chat access.
- High-impact actions should use approval gates.
- Audit the plan, tool call, result, and user identity.
Troubleshooting AI Systems
| Symptom | Likely area | What to check |
|---|
| High inference latency | Infrastructure/model/API | Model size, accelerator use, network path, batching, token count |
| Poor answer quality | Data/model/prompt | Prompt clarity, source quality, evaluation set, retrieval quality |
| RAG answers cite irrelevant content | Retrieval | Chunking, embeddings, metadata filters, reranking |
| RAG says “not found” for known content | Indexing | Document ingestion, freshness, permissions, top-k, chunk size |
| Sudden model performance drop | Drift or data pipeline | Input distribution, schema changes, new user behavior |
| Many false positives | Threshold/metric mismatch | Precision, threshold, class balance, business cost |
| Many false negatives | Recall issue | Threshold, training labels, minority-class representation |
| Biased or unfair outputs | Data/model governance | Dataset representation, label bias, evaluation by subgroup |
| GPU underutilization | Pipeline bottleneck | Data loading, batch size, CPU preprocessing, network/storage |
| Excessive cost | Usage/model design | Token count, model size, caching, batching, placement |
| Unsafe or policy-violating output | Guardrails/security | System prompt, content filters, jailbreak tests, logging |
| Agent performs wrong action | Tool governance | Permissions, tool schema, approval flow, validation |
Common Exam Traps
| Trap | Correct exam thinking |
|---|
| “Use AI for everything” | Choose rules or automation when deterministic logic is enough |
| “Accuracy is the best metric” | Match metric to risk, especially with imbalanced classes |
| “More data always improves the model” | Quality, relevance, labeling, and leakage matter |
| “Fine-tuning adds current knowledge” | RAG is usually better for changing enterprise facts |
| “RAG eliminates hallucinations” | RAG reduces risk but still needs grounding and evaluation |
| “Cloud means no security responsibility” | Identity, data protection, monitoring, and governance still apply |
| “Training and inference are the same” | Training learns parameters; inference uses the model |
| “LLMs are deterministic search engines” | LLMs generate probabilistic outputs |
| “Unsupervised learning uses labeled examples” | Unsupervised learning finds patterns without labels |
| “Correlation proves root cause” | AIOps findings need validation and context |
| “Agents are just prompts” | Agents can take actions and require tool security |
| “Encryption fixes AI safety” | Encryption protects data in transit/at rest; it does not validate output |
| “Test set can be reused for tuning” | Use validation for tuning and test for final unbiased evaluation |
| “Edge is always better for privacy” | Edge helps data locality but still needs lifecycle, access, and logging controls |
Cisco Scenario Checklist
Use this checklist when a question describes an AI solution in a Cisco environment:
Identify the outcome
- Prediction, classification, anomaly detection, summarization, Q&A, automation, or generation.
Classify the data
- Structured, telemetry, logs, documents, audio, video, sensitive, regulated, or proprietary.
Choose the AI pattern
- Rules, supervised ML, unsupervised ML, deep learning, LLM, RAG, or agentic workflow.
Place the workload
- Edge for low latency/locality.
- Data center for controlled high-performance hosting.
- Cloud for managed services and elasticity.
- Hybrid when data, latency, or governance requirements are mixed.
Design the network
- Account for bandwidth, latency, segmentation, API paths, telemetry, and resilience.
Apply security
- Identity, least privilege, encryption, segmentation, prompt/data protection, and audit logging.
Plan operations
- Model/prompt versioning, monitoring, incident response, rollback, and lifecycle management.
Validate the answer
- Prefer the option that aligns AI method, data, infrastructure, security, and business risk.
Final Review Priorities
Before sitting for Cisco AI Technical Practitioner (810-110 AITECH), be able to quickly explain:
- Difference between AI, ML, deep learning, generative AI, RAG, and agents.
- When to use supervised, unsupervised, reinforcement, and deep learning methods.
- Why data quality, leakage, labeling, and bias affect model outcomes.
- How to choose metrics based on false positive and false negative costs.
- How RAG works and when it is better than fine-tuning.
- How prompt engineering, model parameters, and guardrails affect LLM behavior.
- How AI placement affects latency, privacy, cost, network design, and operations.
- How Cisco-style architectures connect AI with networking, security, observability, and automation.
- How to troubleshoot AI systems across model, data, prompt, retrieval, infrastructure, and security layers.
Next Step
Practice with mixed scenario questions: for each one, state the AI approach, data requirement, placement decision, security control, evaluation metric, and operational monitoring plan before checking the answer.