AI-103 Exam Flashcards

Q: What do AI-103 flashcards help you review?

These AI-103 flashcards review Microsoft Foundry planning, model deployments, generative AI, agents, RAG, Azure AI Search, computer vision, speech, text analysis, and information extraction.

Q: Are these AI-103 flashcards enough to pass?

Flashcards are useful for recall, but AI-103 also requires hands-on Foundry practice, scenario questions, and review of the current Microsoft Learn skills outline.

Q: What is the Microsoft AI-103 retake policy?

Use the current Microsoft certification retake policy when scheduling. Local metadata follows the common Microsoft pattern of a 24-hour wait after the first failed attempt and longer waits for later attempts.

Question 1

What is Microsoft Foundry used for on AI-103?

Accepted Answer

Microsoft Foundry is the primary environment for building, managing, evaluating, and deploying Azure AI apps and agents.

Question 2

What should guide model selection?

Accepted Answer

Choose the model based on task requirements such as reasoning depth, latency, cost, modality, context needs, and deployment constraints.

Question 3

When is a small language model a good fit?

Accepted Answer

Use a small language model when the task is narrow, latency or cost matters, and deep reasoning is not required.

Question 4

When is a multimodal model needed?

Accepted Answer

Use a multimodal model when the app must reason over more than one input type, such as text with images, audio, or video context.

Question 5

What is keyless authentication?

Accepted Answer

Keyless authentication uses Microsoft Entra ID and managed identity instead of embedded API keys. It improves secret management and access control.

Question 6

Why use managed identity for Azure AI apps?

Accepted Answer

Managed identity lets Azure-hosted compute access services through assigned identity and RBAC, avoiding hard-coded credentials.

Question 7

What does private networking protect?

Accepted Answer

Private networking reduces public exposure for services and data paths. It is used when AI resources, search indexes, storage, or apps must stay inside controlled network boundaries.

Question 8

What are quotas and rate limits?

Accepted Answer

They control how much model capacity an app can use over time. Monitor them to avoid throttling, failed requests, or unexpected scaling limits.

Question 9

What should AI monitoring track?

Accepted Answer

Track model performance, latency, cost, safety events, drift, search relevance, grounding quality, and app errors.

Question 10

What is responsible AI instrumentation?

Accepted Answer

It is the use of evaluators, safety checks, trace logs, provenance, and oversight workflows to measure and govern AI behavior.

Question 11

What do content filters do?

Accepted Answer

Content filters classify and block or flag unsafe inputs and outputs. They are guardrails, not a replacement for app-level validation and monitoring.

Question 12

What is prompt injection?

Accepted Answer

Prompt injection is an attempt to override instructions or misuse tools through user input, retrieved content, or embedded data. Defenses include isolation, filtering, tool limits, and validation.

Question 13

What is retrieval-augmented generation?

Accepted Answer

RAG retrieves relevant grounding content and adds it to the model context so generated answers can be based on approved sources.

Question 14

What is grounding?

Accepted Answer

Grounding connects model responses to trusted data, retrieved documents, or tool results. It helps reduce unsupported answers.

Question 15

What is function calling?

Accepted Answer

Function calling lets a model request a structured tool or API call. The app executes the call and returns results for the model to use.

Question 16

What is structured output?

Accepted Answer

Structured output asks the model to return data in a defined schema such as JSON. It helps apps validate, parse, and route model responses.

Question 17

What should prompt engineering control?

Accepted Answer

Prompt engineering sets role, task, constraints, examples, output format, and safety instructions so the model produces useful and predictable responses.

Question 18

What does temperature affect?

Accepted Answer

Temperature affects response variability. Lower values are better for consistent outputs; higher values can make responses more creative but less predictable.

Question 19

What is model evaluation?

Accepted Answer

Evaluation measures whether a model or app meets goals for relevance, quality, safety, groundedness, and task success before and after deployment.

Question 20

What does tracing show?

Accepted Answer

Tracing records the steps in an AI workflow, such as prompts, tool calls, retrieval, latency, and outputs. It helps debug and audit behavior.

Question 21

What is token analytics used for?

Accepted Answer

Token analytics helps track usage, cost, prompt size, response size, and performance trends across AI app calls.

Question 22

What is an agent role?

Accepted Answer

An agent role defines what the agent is responsible for, how it should behave, and what boundaries apply to its responses and actions.

Question 23

What is a tool schema?

Accepted Answer

A tool schema describes a callable tool's name, purpose, inputs, and expected structure so the model can request tool use correctly.

Question 24

What is conversation memory?

Accepted Answer

Conversation memory preserves relevant prior context for an agent. It should be scoped carefully so old or sensitive context does not pollute future actions.

Question 25

What is multi-agent orchestration?

Accepted Answer

Multi-agent orchestration coordinates specialized agents or steps to complete a broader workflow, often with routing, handoffs, and approval gates.

Question 26

When should an agent require human approval?

Accepted Answer

Require approval before high-impact, irreversible, costly, or sensitive actions such as sending external messages, changing records, or executing privileged tools.

Question 27

What is visual question answering?

Accepted Answer

Visual question answering uses a model to answer questions grounded in image or video content, such as identifying visible defects or comparing visual elements.

Question 28

What is image captioning?

Accepted Answer

Image captioning generates short or detailed text descriptions of visual content. It can support search, accessibility, and review workflows.

Question 29

What is alt text generation?

Accepted Answer

Alt text generation creates concise descriptions of images for accessibility. It should describe meaningful content without adding unsupported details.

Question 30

What is inpainting?

Accepted Answer

Inpainting edits a selected region of an image while preserving the surrounding context. It is used for controlled image modification.

Question 31

What risk is unique to text inside images?

Accepted Answer

Text embedded in images can carry indirect prompt injection instructions. Treat visual text as untrusted input before using it to guide an agent.

Question 32

What is object detection?

Accepted Answer

Object detection identifies and locates objects or regions within an image or video frame. It is useful when the app needs more than a general caption.

Question 33

What is text analysis in AI-103?

Accepted Answer

Text analysis extracts meaning from text, such as entities, topics, sentiment, summaries, sensitive content, or structured data.

Question 34

What is entity extraction?

Accepted Answer

Entity extraction identifies important items in text, such as people, organizations, dates, products, locations, or custom business terms.

Question 35

What is sentiment analysis?

Accepted Answer

Sentiment analysis estimates the emotional tone of text, such as positive, negative, neutral, or mixed. It is useful for feedback and support scenarios.

Question 36

What is sensitive content detection?

Accepted Answer

Sensitive content detection finds content such as personal data, unsafe material, or policy-relevant text so the app can redact, block, route, or review it.

Question 37

What is speech-to-text used for?

Accepted Answer

Speech-to-text converts audio into text so apps and agents can transcribe, analyze, summarize, or respond to spoken input.

Question 38

What is text-to-speech used for?

Accepted Answer

Text-to-speech converts text responses into spoken audio. It is used for voice agents, accessibility, IVR, and conversational apps.

Question 39

What does translation add to an AI app?

Accepted Answer

Translation lets an app accept or produce content across languages. It may use dedicated translation services or model-based translation flows.

Question 40

What is domain customization?

Accepted Answer

Domain customization adapts prompts, schemas, examples, or models to a specific business vocabulary, format, or compliance requirement.

Question 41

What is information extraction?

Accepted Answer

Information extraction turns documents or multimodal content into usable data, such as fields, summaries, markdown, tables, entities, or grounded chunks.

Question 42

What is OCR used for?

Accepted Answer

OCR converts text in images, scans, or documents into machine-readable text for search, extraction, review, or RAG pipelines.

Question 43

What is Azure AI Search used for in RAG?

Accepted Answer

Azure AI Search indexes content and retrieves relevant chunks for grounding. It can support keyword, vector, hybrid, and semantic search patterns.

Question 44

What is vector search?

Accepted Answer

Vector search finds content by semantic similarity using embeddings. It is useful when exact keyword matches are not enough.

Question 45

What is hybrid search?

Accepted Answer

Hybrid search combines keyword and vector retrieval. It can improve relevance when queries need both exact terms and semantic similarity.

Question 46

What is semantic ranking?

Accepted Answer

Semantic ranking reorders search results based on meaning and relevance. It is commonly layered on top of retrieved candidates.

Question 47

What is indexing?

Accepted Answer

Indexing prepares content for search by extracting, chunking, enriching, embedding, and storing fields that retrieval can query.

Question 48

What is enrichment in a retrieval pipeline?

Accepted Answer

Enrichment adds derived information such as OCR text, entities, captions, translations, key phrases, or embeddings before content is indexed.

Question 49

What is Content Understanding used for?

Accepted Answer

Content Understanding extracts structured or markdown outputs from multimodal content such as documents, images, audio, or video for downstream reasoning.

Question 50

What makes a retrieval pipeline safe for agents?

Accepted Answer

Use trusted sources, access controls, security trimming, prompt-injection defenses, citation/provenance data, and monitoring for grounding quality.

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Microsoft Azure AI App and Agent Developer (AI-103)

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