AI-102 Multiple Select — 50 Questions

Question 1

Which THREE practices should be followed to secure an Azure AI solution that uses Azure OpenAI Service and Azure AI Search?

Accepted Answer

Use managed identities to authenticate between Azure OpenAI and Azure AI Search.. Option B is correct because managed identities let Azure OpenAI and Azure AI Search authenticate to each other through Microsoft Entra ID without embedding secrets or connection strings in code, eliminating credential leakage and rotation overhead. Option D is correct because requiring HTTPS with TLS 1.2 or higher protects data in transit between client applications and the AI services, preventing interception or downgrade attacks on prompts, responses, and search queries. Option E is correct because enabling firewall rules and private endpoints on the AI service endpoints removes public internet exposure and restricts traffic to approved virtual networks, which is a core network-isolation control for Azure AI workloads. Option A is not appropriate because using API keys directly in application code exposes secrets in source control, logs, and memory even if the keys are originally stored in Azure Key Vault. Option C is not appropriate because placing AI services in a DMZ subnet with public IP addresses increases the attack surface rather than securing the solution; private endpoints and restricted access are preferred.

Answer

Store API keys in Azure Key Vault but use them directly in application code.

Answer

Place all AI services in a DMZ subnet with public IP addresses.

Question 2

Which THREE components are required to build a custom chat application using Azure OpenAI Service that can answer questions based on your own private data?

Accepted Answer

The 'Add your data' feature configured in Azure OpenAI Studio.. The 'Add your data' feature in Azure OpenAI Studio provides a no-code interface to connect your private data sources (e.g., Azure Blob Storage, local files) to an Azure OpenAI chat model. It automatically chunks the data, creates an Azure AI Search index, and configures the retrieval-augmented generation (RAG) pipeline, enabling the model to answer questions grounded in your proprietary content without fine-tuning.

Answer

A fine-tuned custom model.

Answer

An Azure OpenAI embeddings model deployment.

Question 3

Which THREE considerations are important when designing a custom skill for Azure AI Search that calls an external API for specialized data extraction?

Accepted Answer

The API endpoint must be reachable from the search service. Option A is correct because a custom skill in Azure AI Search is essentially a Web API skill that the indexer invokes over HTTPS, so the external API endpoint must be network-reachable from the search service (including any required private endpoint or firewall rules) or enrichment will fail. Option D is correct because the Web API skill has a documented maximum request payload size of 16 MB; larger payloads will be rejected, so the skill design must keep the serialized input within that limit. Option E is correct because the Web API skill enforces a maximum execution time of 230 seconds per call, after which the request times out, so long-running extraction logic must be optimized or split. Option B is wrong because a custom skill can accept multiple inputs and produce multiple outputs via the inputs and outputs mappings. Option C is wrong because the skill can be implemented in any language or framework (for example C#, Node.js, or Java) as long as it exposes a compatible HTTP endpoint.

Answer

The skill can only accept one input and produce one output

Answer

The skill must be written in Python

Question 4

Which THREE components are required to build a custom copilot using Microsoft Copilot Studio that can answer questions from a SharePoint document library?

Accepted Answer

A Microsoft Copilot Studio copilot. A Microsoft Copilot Studio copilot is the core conversational interface that orchestrates the interaction. Without the copilot itself, there is no runtime environment to process user queries, manage conversation state, or invoke configured knowledge sources. It is the mandatory host for all custom copilot functionality.

Answer

A Power Automate flow to trigger the copilot

Answer

An Azure OpenAI Service deployment

Question 5

Which THREE actions can be performed using the Azure Custom Vision service?

Accepted Answer

Export a trained model to ONNX format for offline inference.. Option B is correct because Azure Custom Vision supports exporting trained models in several formats, including ONNX, TensorFlow, CoreML, and Docker, enabling offline or edge inference. Option C is correct because Custom Vision is designed for image classification, allowing you to train a model that assigns images to labeled classes such as product types. Option D is correct because Custom Vision also supports object detection, which returns bounding boxes and labels for multiple objects within an image. Option A is not correct because extracting text from scanned receipts is an OCR task handled by Azure AI Vision (Computer Vision Read API) or Document Intelligence, not Custom Vision. Option E is not correct because identifying specific individuals via facial recognition is provided by Azure AI Face, not Custom Vision.

Answer

Extract text from scanned receipts.

Answer

Identify specific individuals in a crowd using facial recognition.

Question 6

Which TWO of the following are best practices for securing Azure AI services?

Accepted Answer

Enable diagnostic settings to audit usage and detect anomalies.. Option C is correct because enabling diagnostic settings on Azure AI services streams resource logs and metrics to destinations such as Log Analytics, Azure Storage, or Event Hubs, which supports auditing usage, monitoring for anomalies, and meeting compliance requirements. Option E is correct because managed identities let applications authenticate to Azure AI services via Microsoft Entra ID tokens, eliminating the need to store or rotate API keys in code or configuration. Option A is incorrect since publicly exposing endpoints increases the attack surface; access should be restricted with private endpoints, network ACLs, or Azure Private Link. Option B is incorrect because disabling diagnostic logging removes the audit trail needed to detect misuse and investigate incidents. Option D is incorrect because sharing API keys across applications prevents per-app revocation and least-privilege scoping, so keys should be unique, stored in Key Vault, and rotated regularly.

Answer

Expose endpoints publicly to simplify client access.

Answer

Disable diagnostic logging to reduce data exposure.

Answer

Share API keys among multiple applications for simplicity.

Question 7

Which THREE factors are critical to consider when designing a custom vision solution for a manufacturing quality inspection system?

Accepted Answer

Imbalance between defective and non-defective product samples.. Class imbalance is a critical factor in custom vision solutions for manufacturing quality inspection. If defective samples are rare compared to non-defective ones, the model may become biased toward predicting the majority class, leading to poor recall for defects. Azure Custom Vision allows adjusting the probability threshold and using techniques like oversampling or weighted loss to mitigate this, but the imbalance must be accounted for during dataset preparation.

Answer

The need for optical character recognition (OCR) of product serial numbers.

Answer

Multilingual support for labeling.

Question 8

Which TWO Azure AI services can be used to extract text from images and PDFs? (Select two.)

Accepted Answer

Azure AI Vision OCR. Azure AI Vision OCR (option C) is correct because its Read/OCR capability extracts printed and handwritten text directly from images and scanned documents. Azure AI Document Intelligence (option D) is correct because it uses prebuilt and custom models to extract text, key-value pairs, and tables from PDFs, images, and forms. Azure AI Translator (A) performs text translation, not optical character recognition, so it cannot extract text from images or PDFs. Azure AI Search (B) is a search indexing service that can consume extracted text but does not itself perform OCR on images or PDFs. Azure AI Language (E) provides NLP features such as sentiment analysis and entity recognition on existing text, not text extraction from images or PDFs.

Answer

Azure AI Translator

Answer

Azure AI Search

Answer

Azure AI Language

Question 9

Which THREE factors should you consider when choosing between Azure AI Custom Vision and Azure AI Vision pre-built models for an image classification task? (Choose three.)

Accepted Answer

Availability of labeled training data specific to the domain. Azure AI Custom Vision is specifically designed for scenarios where you have domain-specific labeled training data that is not covered by pre-built models. Custom Vision allows you to upload your own labeled images and train a custom model tailored to your unique classification needs, which is essential when off-the-shelf models fail to recognize your target classes.

Answer

Image format support (JPEG, PNG)

Answer

Need for real-time inference latency

Question 10

Which THREE are valid parameters when calling the Azure OpenAI Service chat completions API?

Accepted Answer

messages. The `messages` parameter is required in the Azure OpenAI Service chat completions API call. It defines the conversation history and user input as an array of message objects, each with a `role` (system, user, assistant) and `content`. Without this parameter, the API cannot determine the context or prompt for generating a response.

Answer

index_name

Answer

embedding_model

Question 11

You are using Microsoft Purview to create a knowledge map of your organization's data assets. The solution must automatically scan and classify sensitive data in Azure Blob Storage. You need to configure the scanning and classification. Which THREE actions should you perform?

Accepted Answer

Run a full scan of the Blob Storage to discover and classify data.. Option E is correct because before Microsoft Purview can scan any asset, the Azure Blob Storage account must first be registered as a data source in the Purview governance portal, which establishes the connection and allows the account to be managed and scanned. Option D is correct because a scan rule set defines which file types and classification rules are applied during a scan; you must create or select a rule set that includes the desired system or custom classification rules so sensitive data types are detected. Option A is correct because after registering the source and configuring the rule set, you must run a scan (a full scan for initial discovery and classification) so Purview can crawl the Blob Storage, apply the rule set, and populate the knowledge map with classified assets. Option B is not required because Purview provides built-in system classification rules for common sensitive data types, and custom rules are only needed for organization-specific patterns, which the scenario does not require. Option C is not part of the scanning and classification configuration; sensitivity labels are applied through Microsoft Purview Information Protection and are a separate labeling concern from the data map scanning process.

Answer

Create custom classification rules for sensitive data types.

Answer

Apply sensitivity labels to the classified data.

Question 12

You are deploying a generative AI model using Azure AI Foundry. The model must be accessible only from within a specific virtual network. Additionally, you need to monitor all API calls for auditing. Which two configurations are required? (Choose two.)

Accepted Answer

Enable diagnostic settings to send logs to a Log Analytics workspace.. Enabling diagnostic settings to send logs to a Log Analytics workspace allows you to capture and audit all API calls made to the model deployment. This is essential for monitoring, security auditing, and compliance, as it records detailed telemetry such as request timestamps, caller IPs, and operation names. Option E is correct because disabling public network access and configuring a private endpoint ensures that the model is only accessible from within the specified virtual network, meeting the isolation requirement.

Answer

Assign a managed identity to the model deployment.

Answer

Configure CORS to allow only the VNet's domain.

Answer

Enable public network access from selected IP addresses.

Question 13

Which TWO actions should you take to optimize the performance of an Azure AI Search solution that indexes large volumes of data?

Accepted Answer

Use the appropriate search tier (S1, S2, etc.) based on document size. Option A is correct because Azure AI Search tiers (Basic, S1, S2, S3, etc.) differ in storage size, document limits, partitions, and indexer throughput, so selecting a tier that matches your document size and volume is essential for indexing performance. Option D is correct because the Azure AI Search indexing API supports batching up to 1000 documents (or about 16 MB of payload) per index operation, which amortizes network and service overhead and dramatically improves indexing throughput. Option B is wrong because the Free tier is for evaluation only, with strict limits on storage, indexes, and indexers, and is not suitable for production workloads. Option C is wrong because replicas provide high availability and increased query throughput, not indexing throughput; indexing scale is driven by partitions. Option E is wrong because scoring profiles affect query-time ranking, not indexing speed, and disabling them would not optimize indexing performance.

Answer

Use the free tier for production workloads

Answer

Increase the replica count for better indexing throughput

Answer

Disable scoring profiles to speed up indexing

Question 14

You are using Azure OpenAI Service to generate code snippets. The output must be safe and free of security vulnerabilities. Which TWO practices should you follow? (Select TWO.)

Accepted Answer

Use Azure AI Content Safety to filter outputs. Azure AI Content Safety is a dedicated service that provides an additional layer of filtering for harmful or inappropriate content, including security vulnerabilities, beyond what the model itself offers. It allows you to define custom severity thresholds and blocklists, ensuring that generated code snippets are safe before they reach the user. This is a recommended practice for production deployments to mitigate risks like injection attacks or exposure of sensitive patterns.

Answer

Increase the temperature parameter to encourage diversity

Answer

Rely on the model's built-in safety features

Answer

Fine-tune the model with a dataset of secure code examples

Question 15

An agent uses Azure OpenAI with function calling to perform actions. The agent is not executing functions correctly. Which THREE factors should the team check to diagnose the issue?

Accepted Answer

The token limit is too low, truncating the function definitions.. Option B is correct because function definitions are serialized into the prompt sent to the model, so if the token limit (max_tokens/context window) is too low, the function schema can be truncated and the model cannot emit valid function calls. Option C is correct because Azure OpenAI function calling relies on a strict JSON Schema for each function's parameters; incorrect or incomplete schemas (wrong types, missing required fields) cause the model to produce arguments that fail validation or the call to be rejected. Option D is correct because the model selects functions based on their natural-language descriptions, so ambiguous or missing descriptions lead to wrong or no function selection. Option A is not a primary cause: temperature affects randomness, not whether a syntactically valid function call is produced, and function calling can work at high temperature. Option E is not a required check: while newer models may improve function-calling reliability, an outdated model version is not a standard diagnostic factor for functions failing to execute.

Answer

The temperature parameter is set too high.

Answer

The model version is outdated.

Question 16

You are building a document processing solution that extracts information from invoices. The invoices come in various formats and languages. You need to extract line items, totals, and supplier names. Which THREE services should you combine?

Accepted Answer

Azure AI Translator. Azure AI Translator is correct because invoices arrive in various languages, and translating extracted text to a common language (e.g., English) is necessary for downstream processing like entity extraction and validation. Without translation, multilingual invoice data would be inconsistent or unprocessable by language-specific models.

Answer

Azure AI Custom Vision

Answer

Azure AI Content Safety

Question 17

Which TWO Azure services can be used to perform optical character recognition (OCR) on documents? (Select two.)

Accepted Answer

Azure AI Document Intelligence. Azure AI Document Intelligence (option C) is correct because it is the Azure service purpose-built for document processing, and its prebuilt Read and Layout models extract printed and handwritten text from documents and images using OCR. Azure AI Vision (option E) is also correct because its Image Analysis and Read capabilities include an OCR engine that extracts text from images and documents via the Read API. Azure AI Metrics Advisor (option A) is an anomaly-detection service for time-series data and performs no OCR. Azure AI Language (option B) provides natural language processing such as sentiment analysis, key phrase extraction, and entity recognition, not optical character recognition. Azure AI Personalizer (option D) is a reinforcement-learning-based recommendation service and has no OCR functionality.

Answer

Azure AI Metrics Advisor

Answer

Azure AI Language

Answer

Azure AI Personalizer

Question 18

Which TWO actions are required to enable a custom chatbot built with Azure OpenAI to answer questions based on a company's internal PDF documents?

Accepted Answer

Ingest the PDFs into an Azure Cognitive Search index. Azure Cognitive Search provides the indexing and retrieval capabilities needed to make PDF content searchable. By ingesting PDFs into an Azure Cognitive Search index, the chatbot can perform vector or keyword searches over the extracted text, enabling it to retrieve relevant passages to answer user questions. This is the standard approach for grounding a custom chatbot on proprietary documents without modifying the underlying model.

Answer

Use Azure AI Document Intelligence to extract text from PDFs before indexing

Answer

Deploy Azure AI Content Safety to filter responses

Answer

Fine-tune the GPT model on the PDF content

Question 19

A company is planning to use Azure AI services. They require the ability to audit all API calls for compliance. Which THREE components should they enable?

Accepted Answer

Azure Monitor. Azure Monitor (A) is correct because it is the platform that collects, stores, and surfaces activity and resource logs, enabling the audit trail of API calls required for compliance. A Log Analytics workspace (B) is correct because it is the destination where diagnostic logs are ingested and queried with KQL, allowing retention and analysis of all API call records. Diagnostic settings for the AI service (C) is correct because it is the mechanism that routes the service's resource logs (e.g., Audit, RequestResponse) and metrics to Azure Monitor/Log Analytics, which is what actually captures the API calls. Azure RBAC roles (D) only control authorization to the resource and do not record API call history, and a managed identity (E) only provides an authentication credential for the service, so neither produces the audit data required.

Answer

Azure RBAC roles for the service

Answer

Managed identity for the service

Question 20

Which TWO Azure AI services can be used together to build a solution that transcribes customer service calls and detects sentiment?

Accepted Answer

Azure AI Language (sentiment analysis). Azure AI Speech (option B) is correct because its speech-to-text capability transcribes the audio of customer service calls into text, which is the required first step for this solution. Azure AI Language (option A) is correct because its sentiment analysis feature evaluates the transcribed text and returns sentiment scores/labels (positive, negative, neutral, mixed), satisfying the detection requirement. Together, B feeds transcription output into A for sentiment evaluation. Option C (Azure AI Translator) is not needed since the scenario does not require language translation. Option D (text-to-speech) is the reverse of what is needed—it synthesizes speech from text rather than transcribing calls. Option E (conversational language understanding) extracts intents and entities for conversational apps, not sentiment, so it does not fulfill the requirement.

Answer

Azure AI Translator

Answer

Azure AI Speech (text-to-speech)

Answer

Azure AI Language (conversational language understanding)