Free Practice Questions for Microsoft Azure AI Engineer Associate (AI-103) Certification

- Choose an appropriate model for each task, including large language models (LLMs), small language models, multimodal models, and Foundry Tools - Choose the appropriate Foundry services for generative tasks, grounding, vector search, agent workflows, or multimodal processing - Choose an appropriate method for retrieval and indexing - Choose appropriate memory, tool, and knowledge integration services for agent solutions

- Design Azure infrastructure for AI apps and agent-based solutions - Choose appropriate deployment options - Configure model and agent deployments - Integrate Foundry projects with continuous integration and continuous deployment (CI/CD) pipelines

- Manage quotas, scaling, rate limits, and cost footprints for model and agent workloads - Monitor model performance, drift, safety events, and grounding quality - Monitor data ingestion quality, search index health, and relevance performance - Configure security, including managed identity, private networking, keyless credentials, and role policies

- Configure safety filters, guardrails, risk detection, and content moderation - Apply responsible AI instrumentation, including evaluators, safety evaluations, and explanation tooling - Implement auditing through trace logging, provenance metadata, and approval workflows - Govern agent behavior with oversight modes, constraints, and tool-access controls

- Deploy and consume LLMs, small models, code models, and multimodal models - Implement retrieval-augmented generation (RAG) in an application - Design workflows, tool-augmented flows, and multistep reasoning pipelines - Evaluate models and apps, including detecting fabrications, relevance, quality, and safety - Integrate generative workflows into applications by using Foundry SDKs and connectors - Configure an application to connect to a Foundry project

- Define agent roles, goals, conversation-tracking approach, and tool schemas - Build agents that integrate retrieval, function-calling, and conversation memory - Integrate agent tools, including APIs, knowledge stores, search, content understanding, and custom functions - Implement orchestrated multi-agent solutions - Build autonomous or semiautonomous workflows with safeguards and approval flow controls - Integrate monitoring into deployed agents, evaluate agent behavior, and perform error analysis

- Tune generation behavior, such as prompt engineering and adjusting model parameters - Implement model reflection, chain-of-thought evaluations, and self-critique loops - Set up observability by implementing tracing, token analytics, safety signals, and latency breakdowns - Orchestrate multiple models, flows, or hybrid LLM and rules engines

- Implement a solution that generates images from text prompts and reference media - Implement a solution that generates videos from text prompts and reference media - Configure image-editing workflows, including inpainting, mask-based edits, and prompt-driven modifications - Implement workflows to edit generated videos - Select and apply appropriate generation and editing controls provided by the platform

- Build a solution that analyzes visual context by using multimodal models - Configure apps to produce concise or detailed captions for single or multiple images - Implement a solution that enables question-answering grounded in visual evidence - Configure generation of alt-text and extended image descriptions aligned to accessibility guidelines - Implement visual understanding by configuring Azure Content Understanding in Foundry Tools to extract visual characteristics - Implement video analysis workflows to process and interpret video segments - Configure single-task and pro-mode Content Understanding pipelines - Implement solutions that identify objects, components, or regions within images or video

- Implement filters to classify unsafe or disallowed visual content - Detect and mitigate indirect prompt injection by using embedded text in images - Enforce visual policy rules, such as applying watermarks, flagging prohibited symbols, upholding brand usage requirements, and detecting potentially inappropriate content

- Implement solutions to extract entities, topics, summaries, and structured JSON outputs by using generative prompting and Foundry Tools - Configure detection of sentiment, tone, safety issues, and sensitive content - Build solutions that translate text by using Azure Translator in Foundry Tools or LLM-powered translation flows - Customize language model outputs for domain tasks, such as compliance summarization and domain extraction

- Implement workflows to convert speech to text and text to speech for agentic interactions - Integrate speech as an agent modality, including custom speech models - Enable multimodal reasoning from audio inputs - Translate speech into other languages by using language models and Foundry Tools

- Ingest and index content, such as documents, images, audio, and video - Configure semantic search, hybrid search, and vector search for grounding - Implement enrichment by using custom or built-in skills for text, images, and layout - Configure RAG ingestion flow, including documents and using optical character recognition (OCR) - Connect retrieval pipelines directly to workflows and agent tools

- Extract information by using multimodal pipelines that combine OCR, layout analysis, and field extraction - Produce clean, grounded representations to use with agents and RAG by using Content Understanding - Implement analyzers for generating structured or markdown outputs for downstream reasoning by using Content Understanding