Microsoft Azure (AI-103) Exam Questions 2026
Microsoft Azure (AI-103) Exam Questions 2026
Visit Official SkillCertPro Website :-
For a full set of 600 questions. Go to
https://skillcertpro.com/product/microsoft-azure-ai-103-exam-questions-2026/
SkillCertPro offers detailed explanations to each question which helps to understand the concepts better.
It is recommended to score above 85% in SkillCertPro exams before attempting a real exam.
SkillCertPro updates exam questions every 2 weeks.
You will get life time access and life time free updates
SkillCertPro assures 100% pass guarantee in first attempt.
Question 1:
In “Prompt Flow,“ what is the purpose of the dag.yaml file?
A. To write Python code
B. To define the structure and execution order (Directed Acyclic Graph) of the flow nodes
C. To store the index
D. To store API keys
Answer: B
Explanation:
B. To define the structure and execution order (Correct)
In Prompt Flow, the dag.yaml file is the orchestration engine. It acts as the configuration file that links different tools (LLMs, Python scripts, Prompt templates) together.
The "DAG" concept: It represents a Directed Acyclic Graph, ensuring that tasks flow in a specific direction without circular dependencies.
What it contains: It lists the nodes (steps), their types, and their inputs/outputs to determine which step must finish before the next one begins.
A. To write Python code (Incorrect)
While Python is a primary tool used within a flow, the code itself is stored in separate .py files. The dag.yaml merely references these Python files and defines how the functions within them are called.
C. To store the index (Incorrect)
Indices (like those used in RAG patterns via Azure AI Search) are external data structures. While a flow node might query an index, the index data or its configuration is not stored in the dag.yaml.
D. To store API keys (Incorrect)
Storing API keys in a YAML file is a major security risk and a "red flag" in Azure certification exams. In Prompt Flow, connections to LLMs and services are handled via Connections (Azure Key Vault-backed assets). The dag.yaml refers to the name of the connection, never the secret key itself.
Question 2:
You are building an agent for a museum that describes paintings to visually impaired visitors. The model needs to provide rich, artistic descriptions. Which prompting technique is most suitable?
A. Chain-of-Thought with a “Storyteller“ persona
B. Zero-shot
C. Negative prompting only
D. One-word instructions
Answer: A
Explanation:
A. Chain-of-Thought with a “Storyteller“ persona (Correct)
This is the most effective choice for "rich, artistic descriptions" for several reasons:
Persona Prompting: By assigning the model a "Storyteller" persona, you guide the tone, vocabulary, and emotional resonance of the output.
Chain-of-Thought (CoT): This technique encourages the model to process the image's details step-by-step (e.g., "First, look at the brushstrokes, then the lighting, then the emotion"). For the visually impaired, a structured "walkthrough" of the painting is far more effective than a flat summary.
Azure Context: Microsoft explicitly recommends using System Messages (personas) to define the model's behavior and grounding in Azure OpenAI Service.
B. Zero-shot (Incorrect)
Zero-shot prompting involves giving a task without any examples. While an LLM can describe a painting in zero-shot mode, it often results in generic, factual descriptions. For a museum setting requiring "rich, artistic" detail, the model needs more guidance on how to describe the art, rather than just what is in the art.
C. Negative prompting only (Incorrect)
Negative prompting tells the model what not to do (e.g., "Do not use technical jargon"). While useful for refinement, it doesn't provide the creative direction or the "richness" required for an artistic description. You cannot create a masterpiece solely by listing things you don't want.
D. One-word instructions (Incorrect)
Instructions like "Describe" or "Artistic" are too brief to ensure consistency or high-quality detail. LLMs perform significantly better when provided with context, constraints, and structural guidance (Prompt Weighting/Persona), which one-word instructions lack.
Question 3:
You are building a “Streaming“ UI. Which event should you listen for to know that a “Tool Call“ (like a search) has started?
A. thread.run.queued
B. thread.message.delta
C. thread.run.step.created
D. thread.run.completed
Answer: C
Explanation:
C. thread.run.step.created (Correct)
When using the Assistants API in streaming mode, the "Run" is broken down into Steps.
Why it fits: A "Tool Call" (such as a search or function execution) is considered a distinct step in the model's execution logic.
The Trigger: The thread.run.step.created event is fired the moment the assistant decides it needs to use a tool. This is the exact signal a UI needs to display a "Searching..." or "Calculating..." spinner to the user.
The Payload: This event contains a step_details object which specifies the type (e.g., tool_calls).
A. thread.run.queued (Incorrect)
This event occurs at the very beginning of the process. It simply means the request has been received by the Azure OpenAI service and is waiting for a thread to become available. At this stage, the model hasn't even processed the prompt yet, so it doesn't know if a tool call is required.
B. thread.message.delta (Incorrect)
This event is used for content streaming. It fires when the model is generating actual text tokens to be displayed to the user. While it's vital for a "Streaming UI" to show text appearing character-by-character, it does not signal the initiation of a backend tool or search function.
D. thread.run.completed (Incorrect)
This event fires only after the entire process is finished—including all tool calls, data processing, and final response generation. Listening for this would be the opposite of "streaming," as the UI would remain static until the very end of the cycle.
Question 4:
A company is using “Azure AI Vision“ to count the number of people in a lobby. The model is struggling because the camera is at a sharp angle. What is the best fix?
A. Change the model to GPT-4o
B. Use a text-only prompt
C. Increase Temperature
D. Train a “Custom Vision“ model with images from that specific camera angle
Answer: D
Explanation:
D. Train a “Custom Vision“ model (Correct)
Azure AI Vision offers two main paths: Image Analysis (prebuilt) and Custom Vision.
Prebuilt Models: These are trained on standard, clear, "eye-level" photos. If a camera is at a "sharp angle" (like a ceiling security camera), the prebuilt model may fail to recognize human shapes because they appear distorted.
The Solution: Custom Vision allows you to upload your own images. By providing 50+ images of people taken from that specific camera angle, you train the model to recognize those specific silhouettes and distortions, drastically improving accuracy.
A. Change the model to GPT-4o (Incorrect)
While GPT-4o has impressive vision capabilities (Multimodal LLM), it is generally overkill and more expensive for a simple, repetitive task like "counting people in a lobby." Furthermore, in the specific context of Azure AI Search and Vision services, "Custom Vision" is the architecturally correct tool for specialized object detection tasks rather than a generative model.
B. Use a text-only prompt (Incorrect)
A "text-only prompt" applies to Large Language Models (LLMs), not traditional Computer Vision models. Since the problem is a visual recognition issue (camera angle), a text prompt cannot fix the underlying lack of visual data or the distortion in the video feed.
C. Increase Temperature (Incorrect)
"Temperature" is a parameter used in generative AI (like Azure OpenAI) to control the randomness or "creativity" of text output. It has no effect on the Object Detection or Spatial Analysis algorithms used by Azure AI Vision to count people.
Question 5:
An international NGO wants an AI agent to translate field reports from local dialects to English. The reports contain handwritten notes. Which sequence of services is correct?
A. Azure AI Speech -> Azure AI Vision
B. Azure OpenAI -> Azure AI Search
C. Azure AI Language -> Azure SQL
D. Azure AI Document Intelligence -> Azure AI Translator -> Azure OpenAI
Answer: D
Explanation:
D. Azure AI Document Intelligence -> Azure AI Translator -> Azure OpenAI (Correct)
This sequence follows the logical "Extract → Transform → Refine" workflow required for this specific problem:
Azure AI Document Intelligence: Necessary to handle the handwritten notes. Its OCR capabilities are specifically designed to extract text from unstructured, physical documents.
Azure AI Translator: Once the text is digitized, this service handles the translation from the local dialect to English at scale.
Azure OpenAI: This final step is used for "refinement." It can summarize the translated report, fix grammatical nuances lost in literal translation, or extract key insights for the NGO's AI agent.
A. Azure AI Speech -> Azure AI Vision (Incorrect)
Azure AI Speech is for audio-to-text or text-to-speech. Since the reports are "handwritten notes," there is no audio component involved. Azure AI Vision (specifically OCR) could extract the text, but the sequence lacks any service to handle the translation or the agent's logic.
B. Azure OpenAI -> Azure AI Search (Incorrect)
Azure AI Search is used for indexing and retrieving data (RAG pattern). While you might store the finished reports here later, this sequence doesn't address the primary problem: extracting text from handwriting and translating it.
C. Azure AI Language -> Azure SQL (Incorrect)
Azure AI Language is great for sentiment analysis or summarization, but it cannot "read" handwritten images—it requires digital text as an input. Azure SQL is a database for storage and does not provide AI processing capabilities.
For a full set of 600 questions. Go to
https://skillcertpro.com/product/microsoft-azure-ai-103-exam-questions-2026/
SkillCertPro offers detailed explanations to each question which helps to understand the concepts better.
It is recommended to score above 85% in SkillCertPro exams before attempting a real exam.
SkillCertPro updates exam questions every 2 weeks.
You will get life time access and life time free updates
SkillCertPro assures 100% pass guarantee in first attempt.
Question 6:
What happens if a Shaper skill refers to a source that is missing or null for a specific document?
A. The indexer crashes
B. The output property is omitted or set to null
C. The API key expires
D. The document is deleted
Answer: B
Explanation:
B. The output property is omitted or set to null (Correct)
Azure AI Search indexers are designed to be "fault-tolerant" during the enrichment phase.
Graceful Handling: If a Shaper skill is configured to map a source field (e.g., /document/entities) to a new structure, but that source field doesn't exist for a particular document (perhaps no entities were found), the Shaper skill doesn't fail the whole process.
Behavior: It simply skips that specific mapping or assigns a null value to the target property in the resulting JSON object. This ensures that the rest of the document's metadata can still be indexed.
A. The indexer crashes (Incorrect)
If an indexer crashed every time a document was missing a field, it would be impossible to crawl large, unstructured datasets (like a mix of PDFs, images, and text files). Azure handles missing data as a "Warning" in the execution history, not as a fatal "Error" that stops the service.
C. The API key expires (Incorrect)
API key expiration is a security and billing event governed by Azure Role-Based Access Control (RBAC) or the resource lifecycle. It is never triggered by the internal logic or data quality of a specific cognitive skill or document.
D. The document is deleted (Incorrect)
Missing data in an enrichment step does not trigger a deletion. The document will still proceed through the pipeline and be added to the search index, though the specific field generated by the Shaper skill will simply be empty.
Question 7:
A retail bot is designed to suggest outfits. The developer wants the bot to avoid suggesting leather products. Where should this constraint be placed?
A. In the API Key
B. In the Model Name
C. In the System Prompt as a “Negative Constraint“
D. In the User message
Answer: C
Explanation:
C. In the System Prompt as a “Negative Constraint“ (Correct)
The System Prompt (also known as the System Message) is the foundational layer that defines the model's behavior, boundaries, and rules.
Why it works: By placing the constraint here, you are giving the model a permanent rule that applies to every interaction in that session.
Negative Constraints: These explicitly tell the model what not to do (e.g., "Do not suggest leather products"). This is the standard Azure design pattern for ensuring safety, brand alignment, and specific product exclusions.
A. In the API Key (Incorrect)
An API key is solely for authentication. It tells the Azure service who is making the request so it can grant access and track billing. It has no capability to influence the logic, content, or constraints of the AI model's output.
B. In the Model Name (Incorrect)
The Model Name (e.g., gpt-4o or gpt-35-turbo) identifies which underlying architecture to use. While different models have different capabilities, the name itself does not contain custom business logic or specific product filters like "no leather."
D. In the User message (Incorrect)
While you could technically ask the user to say "suggest outfits but no leather," this is not a robust design. A developer-led constraint should be invisible to the user and enforced automatically. If the constraint is only in the user message, the model might suggest leather if the user forgets to mention the restriction.
Question 8:
Which SDK method is used to download the actual bytes of a file from the Azure AI Agent service using its file_id?
A. client.files.content(file_id)
B. client.get_image(file_id)
C. client.download(file_id)
D. os.open(file_id)
Answer: A
Explanation:
A. client.files.content(file_id) (Correct)
This is the standard SDK method for both the Azure OpenAI and the new Azure AI Foundry (Projects) libraries.
How it works: When an agent generates a file (like a CSV, PDF, or image), it stores that file in the service’s storage and provides a file_id.
The Result: Calling client.files.content(file_id) returns the raw bytes (content) of the file, which you can then stream or save locally using standard file-writing methods.
Compatibility: This works consistently across the Python, JavaScript, and .NET SDKs for Azure AI services.
B. client.get_image(file_id) (Incorrect)
While an agent might generate an image, there is no generic get_image method in the official Azure AI SDKs. The service treats all generated artifacts (images, charts, text files) as "Files," meaning you use the universal files.content method regardless of the file type.
C. client.download(file_id) (Incorrect)
Although "download" is the logical word for what you are doing, it is not the name of the method in the SDK. The Azure SDKs follow the REST API naming conventions, where retrieving the body of a file resource is mapped to .content().
D. os.open(file_id) (Incorrect)
The os module is a standard Python library for interacting with your local operating system. It cannot communicate with Azure cloud services. To use os.open or open(), you must first download the bytes from Azure and save them to a local path.
Question 9:
In a Custom Skill JSON request, what is the purpose of the recordId property?
A. To define language
B. To uniquely identify and map the response back to the correct input
C. To delete data
D. To store the API Key
Answer: B
Explanation:
B. Trace View (in Prompt Flow) (Correct)
When you build a complex AI orchestration (a "flow") in Azure AI Foundry, it often involves multiple steps like LLM calls, Python scripts, and vector searches.
Why it fits: Trace View provides a timeline and detailed log of every node's execution.
Debugging: It allows you to see the exact input passed to a node and the output it generated. If your final answer is wrong, you can use Trace View to find exactly which intermediate step (e.g., a specific prompt or a data retrieval step) caused the issue.
OpenTelemetry: It is built on OpenTelemetry standards, allowing for deep inspection of latency and token usage per node.
A. Model Catalog (Incorrect)
The Model Catalog is a library where you discover, compare, and deploy models (like GPT-4, Llama, or Mistral). It is used for the deployment phase, not for debugging the execution logic of a multi-node workflow.
C. Content Safety (Incorrect)
Azure AI Content Safety is a service used to detect and block harmful content (hate speech, violence, self-harm). While it can be a node within your flow, the service itself is for moderation, not for debugging the data flow or intermediate outputs of other nodes.
D. Azure Monitor (Incorrect)
While Azure Monitor (and Application Insights) is used for high-level logging and performance metrics across your entire Azure subscription, it doesn't provide the granular, step-by-step visual debugging interface required to inspect the "intermediate outputs" of a specific AI flow logic.
Question 10:
In a Custom Skill JSON request, what is the purpose of the recordId property?
A. To define language
B. To uniquely identify and map the response back to the correct input
C. To delete data
D. To store the API Key
Answer: B
Explanation:
B. To uniquely identify and map the response (Correct)
When an Azure AI Search indexer calls a custom skill, it sends data in batches to optimize performance.
The Challenge: The external Web API (like an Azure Function) might process these records asynchronously or return them in a different order than they were received.
The Solution: The recordId acts as a correlation token. Every record in the JSON request has a recordId. The custom skill must return the exact same recordId for each result so that the indexer knows which output belongs to which document in the pipeline.
A. To define language (Incorrect)
Language definition is typically handled by a defaultLanguageCode parameter or by the Language Detection Skill earlier in the pipeline. While your custom skill might use the language as an input, the recordId itself is strictly a structural tracking ID, not a metadata field.
C. To delete data (Incorrect)
The recordId is used for enrichment (adding data), not deletion. To delete data from an index, you would typically use an Indexing Action (like delete) within the Indexer configuration or a separate API call to the search service, not a property within a Custom Skill's data packet.
D. To store the API Key (Incorrect)
Security credentials like API keys are never stored within the document records themselves. For a custom skill, the API key (or "Host Key" for an Azure Function) is stored in the Skillset definition under the httpHeaders or as part of the uri. Putting an API key in a recordId would expose it to the logs of every document processed.
For a full set of 600 questions. Go to
https://skillcertpro.com/product/microsoft-azure-ai-103-exam-questions-2026/
SkillCertPro offers detailed explanations to each question which helps to understand the concepts better.
It is recommended to score above 85% in SkillCertPro exams before attempting a real exam.
SkillCertPro updates exam questions every 2 weeks.
You will get life time access and life time free updates
SkillCertPro assures 100% pass guarantee in first attempt.