AI-103 Practice Test 2 — 25 Questions

Question 1

You are developing a global news aggregation application using the Azure AI Language service. You need to automatically identify the primary language of incoming user-submitted articles across 120 different languages before routing them to translation pipelines. Which API endpoint should you call?

Choose an answer to begin — your selection is scored in the full session.

Accepted Answer

POST /language/:analyze-text?api-version=2023-04-01 with the TextLanguageDetection task. The TextLanguageDetection task in the /language/:analyze-text endpoint is specifically designed to identify the language of a document and return the detected language code and confidence score. It supports the full range of Azure AI Language languages (currently 120+), making it the correct choice for routing multilingual articles.

Answer

POST /language/translate?api-version=3.0

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POST /language/:analyze-text?api-version=2023-04-01 with the CustomEntityRecognition task

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POST /language/analyze-text/jobs?api-version=2023-04-01 with the TextSentimentAnalysis task

Question 2

You are deploying an LLM in Azure AI Foundry and need to monitor for toxicity and jailbreak attempts in real-time. Which feature should you enable?

Accepted Answer

Azure AI Content Safety. Azure AI Content Safety is the service designed to detect harmful content, including toxicity and jailbreak attempts, in real time for LLM applications. It provides classifiers for hate, violence, sexual, and self-harm content, plus Prompt Shields for jailbreak and indirect prompt injection detection. Enabling it allows pre- and post-processing moderation of prompts and completions.

Answer

Azure Policy

Answer

Microsoft Purview Compliance Manager

Answer

Azure Monitor Log Analytics

Question 3

You are configuring an Azure AI Document Intelligence custom extraction pipeline that processes thousands of diverse medical forms daily. You notice that extraction confidence scores for certain hand-written fields are low. What is the most effective remediation step within the Document Intelligence workflow?

Accepted Answer

Add additional labeled training documents that specifically feature diverse handwriting samples for those low-confidence fields.. The most effective remediation for low-confidence extraction of handwritten fields in a custom Document Intelligence model is to provide more labeled training data that represents the variability causing the low confidence. Handwriting is highly variable, so adding diverse handwriting samples for the specific fields helps the model learn the patterns and improve accuracy. This directly addresses the root cause—insufficient training examples for those field variations—within the Document Intelligence workflow.

Answer

Disable the OCR engine parameter in the API call to bypass text recognition steps.

Answer

Decrease the model training iterations configuration setting to prevent overfitting on handwriting.

Answer

Switch from a Custom Neural model to a Custom Template model.

Question 4

Your custom extraction model is failing to identify fields on a new version of a document. You have labeled 50 documents. What is the most likely solution?

Accepted Answer

Add more training documents with the new layout. When a custom extraction model fails on a new document layout, the most likely fix is to add more training documents that represent the new layout so the model can learn the additional variations. Custom models (especially neural) improve with more diverse, labeled examples covering the layouts they must handle; 50 documents of the old layout are insufficient for the new version. Retraining with representative samples is the standard remedy.

Answer

Change the API version to v2.1

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Switch to a template model

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Increase the confidence threshold

Question 5

Your company's security policy requires that all Azure AI Services accounts disable public network access completely. However, internal applications hosted inside a specific Azure Virtual Network must still communicate with the AI services. How should you configure network access?

Accepted Answer

Disable public network access and configure a Private Endpoint for the resource.. To disable public network access while allowing internal applications in a specific Azure Virtual Network to communicate with Azure AI Services, you should disable public network access and configure a Private Endpoint. This creates a private IP address in the VNet for the service, enabling secure private connectivity.

Answer

Configure an inbound Network Security Group (NSG) rule on the AI resource subnet.

Answer

Keep public network access enabled and rely on API keys for security.

Answer

Deploy a VPN gateway inside the Azure AI resource group.

Question 6

Your organization has multiple Azure AI services deployed across different resource groups. Your security officer requests a centralized dashboard to monitor security recommendations, vulnerability assessments, and compliance posture for all AI resources. Which Azure service should you open?

Accepted Answer

Microsoft Defender for Cloud. Microsoft Defender for Cloud provides a centralized security posture management dashboard that aggregates security recommendations, vulnerability assessments, regulatory compliance, and threat protection across Azure resources, including Azure AI services. It is the correct service to open when a security officer requests a unified view of security recommendations and compliance posture across multiple resource groups.

Answer

Azure Advisor

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Microsoft Entra ID protection portal

Answer

Azure Policy compliance blade

Question 7

You are designing a multi-tenant SaaS application on Azure. Each tenant requires isolation of their Azure OpenAI token usage and cost tracking. What is the recommended provisioning strategy to achieve strict cost and quota isolation per tenant?

Accepted Answer

Provision a separate Azure OpenAI resource for each tenant.. Provisioning a separate Azure OpenAI resource per tenant provides independent quota limits, separate keys, and distinct billing meters, which together deliver strict cost and quota isolation. This is the recommended multi-tenant pattern when isolation is a hard requirement.

Answer

Use a single Azure OpenAI resource for all tenants and track usage using custom HTTP headers.

Answer

Deploy all tenants in the F0 free pricing tier.

Answer

Rely on Azure Cost Management resource group tags alone without resource separation.

Question 8

You are analyzing product reviews using Azure AI Language. You receive sentiment analysis output where a sentence has a positive confidence score of 0.85, a neutral score of 0.10, and a negative score of 0.05. What is the sentiment label assigned to this sentence?

Accepted Answer

positive. Azure AI Language sentiment analysis assigns the label corresponding to the highest confidence score among positive, neutral, and negative. Here positive is 0.85, which is the maximum, so the sentence is labeled positive. The scores are a probability distribution that sums to approximately 1.0.

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neutral

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mixed

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negative

Question 9

You are building a custom named entity recognition model in Language Studio. You want to extract product serial numbers that follow a specific pattern (two uppercase letters followed by four digits, a hyphen, and three digits). Which entity type definition method should you use?

Accepted Answer

Regex entity type. A regex entity type is designed to extract entities that match a specific regular expression pattern, such as two uppercase letters followed by four digits, a hyphen, and three digits (e.g., '[A-Z]{2}\d{4}-\d{3}'). In Language Studio, you define the regex pattern and optionally provide examples, making it ideal for structured identifiers like serial numbers.

Answer

List entity type

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Learned entity type

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Prebuilt entity type

Question 10

You are deploying an Azure AI multi-service account in the West US region. The finance department requires that all resource deployment costs be tracked by department code using Azure Resource Manager tags. Which Azure feature should you use to enforce the mandatory application of the department tag across all future Azure AI resource deployments?

Accepted Answer

Azure Policy. Azure Policy is the governance service that enforces organizational standards by evaluating resources against rules and taking effects such as deny, audit, or append. A policy with an 'append' or 'deny' effect on the tag requirement enforces mandatory department tags across all future deployments.

Answer

Azure Cost Management budgets

Answer

Azure role-based access control (RBAC)

Answer

Azure Blueprints

Question 11

You are implementing prompt engineering for a customer support agent. To minimize hallucinations when the model lacks information, which Prompt Flow technique is most effective?

Accepted Answer

Instructional grounding to only answer from context. Instructional grounding — explicitly instructing the model to answer only from the provided context and to say it doesn't know otherwise — is the most direct and effective prompt-engineering technique to minimize hallucinations when the model lacks information. It constrains the model's generation to the retrieved context rather than its parametric memory.

Answer

Chain-of-thought prompting

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Increasing the Top_P parameter

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Few-shot prompting with varied examples

Question 12

You are using Azure AI Document Intelligence to extract information from identity documents, such as passports and driver licenses. Which prebuilt model should you select?

Accepted Answer

prebuilt-idDocument. The prebuilt-idDocument model is specifically designed to extract key information from identity documents such as passports, driver licenses, and U.S. state IDs. It returns structured fields like name, date of birth, document number, and expiration date. This is the only prebuilt model among the options that targets identity documents.

Answer

prebuilt-tax.us.w2

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prebuilt-W2

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prebuilt-businessCard

Question 13

You are developing a RAG solution using Azure AI Search. Users are searching using domain-specific acronyms that are not well-represented in standard embedding models, resulting in poor retrieval scores. What feature should you enable in Azure AI Search to improve keyword matching for exact acronyms?

Accepted Answer

Hybrid search combining vector search and full-text BM25 search. Hybrid search in Azure AI Search runs vector (k-NN) search and full-text BM25 keyword search in parallel, then fuses the results using Reciprocal Rank Fusion (RRF). BM25 excels at exact term matching, so domain-specific acronyms that embedding models underrepresent will still be matched lexically, dramatically improving recall for those queries. This is the standard remedy when semantic/vector retrieval alone misses rare tokens.

Answer

Increasing the embedding dimension size to 3072

Answer

Enabling Azure AI Content Safety filters

Answer

Setting the semantic ranker threshold to zero

Question 14

Which file format is NOT natively supported for training custom models in Azure AI Document Intelligence?

Accepted Answer

DOCX. Azure AI Document Intelligence custom model training accepts PDF, JPEG, PNG, BMP, TIFF, and HEIF image formats, but DOCX (Word) files are not natively supported as training inputs. To train on Word content, you must first convert DOCX to PDF or render pages as images before uploading to the labeled training set. This is why DOCX is the correct answer to 'which is NOT natively supported.'

Answer

PDF

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JPEG

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BMP

Question 15

You are provisioning an Azure AI Language resource and want to ensure that all telemetry and activity logs are retained for compliance auditing over a 365-day period. Where should you configure this retention policy?

Accepted Answer

In the resource's Diagnostic settings by specifying a retention period on a storage account destination.. Diagnostic settings in Azure allow you to configure the collection and retention of telemetry and activity logs. By specifying a retention period (e.g., 365 days) on a storage account destination, you ensure logs are retained for compliance auditing. This is the correct place to set retention policies for Azure AI Language resource logs.

Answer

In the Microsoft Defender for Cloud security policy blade.

Answer

In the Azure AI Language service Studio configuration panel.

Answer

In the Azure Cost Management budget alert settings.

Question 16

You are designing an agentic workflow in Azure AI Foundry that requires the agent to call an external REST API. Which component should you use to define the tool interface?

Accepted Answer

Tool definition using a Python function. In Azure AI Foundry agentic workflows, tools are defined as callable functions with a name, description, and parameter schema; a Python function definition (often decorated or registered as a tool) is the standard way to expose an external REST API to the agent. The model then emits structured arguments that invoke that function. This is the correct component for defining the tool interface.

Answer

Azure AI Content Safety

Answer

LLM output parser

Answer

Flow input schema

Question 17

You are troubleshooting an Azure AI Language asynchronous summarization job. You submitted a batch of documents using the `/analyze` endpoint with long-running operations (LRO). How should your application check the status and retrieve the results of the job?

Accepted Answer

Poll the URL provided in the 'operation-location' response header using a GET request until the status is 'succeeded'.. Azure AI Language asynchronous (LRO) jobs return an 'operation-location' header in the initial 202 Accepted response. The application must poll that URL with GET requests until the status field reads 'succeeded', then retrieve results from the same endpoint. This is the standard Azure long-running operation pattern.

Answer

Listen for incoming webhook notifications on an Azure Event Grid subscription attached to the text analytics resource.

Answer

Query the Azure Monitor metrics API for job completion status.

Answer

Repeatedly send synchronous POST requests to the same document endpoint until a 200 OK is returned.

Question 18

You have an Azure AI application that is hitting rate limits (429 errors). You need to determine which specific client application is causing the issue. How do you identify this?

Accepted Answer

Enable diagnostic logging to Log Analytics and run a KQL query.. Diagnostic logging to Log Analytics captures per-request telemetry including the caller identity, subscription, resource, operation name, and HTTP status code, so a KQL query filtering on ResponseCode == 429 and summarizing by CallerIpAddress or identity reveals which client is exhausting the quota. The Activity Log only records control-plane operations (resource create/update/delete), not data-plane inference calls, so it cannot show which app is making the requests.

Answer

Check the 'Activity Log'.

Answer

Increase the resource quota in the 'Quotas' blade.

Answer

Restart the AI service.

Question 19

Which THREE of the following are components of a standard Prompt Flow DAG (Directed Acyclic Graph)?

Accepted Answer

Inputs. In a standard Prompt Flow DAG, Inputs (A) are the declared entry points that supply values (for example, a question string or a dataset column) to the flow, making them a required component. Outputs (B) are also required because they define the flow's returned values (such as the model's answer or a score) that downstream consumers or evaluations read. Tools (D) are the executable nodes of the DAG — LLM, Python, Prompt, and built-in tools — and the edges connecting them define the directed acyclic execution order. Model Training Jobs (C) are not part of a Prompt Flow DAG; training is handled by separate Azure Machine Learning job constructs, not flow nodes. Data Labeling Projects (E) are likewise a distinct Azure Machine Learning feature for annotating data and are not components of a Prompt Flow DAG.

Answer

Model Training Jobs

Answer

Data Labeling Projects

Question 20

You are evaluating an agentic workflow in Azure AI Foundry that performs multi-step reasoning. The agent occasionally enters an infinite loop, repeatedly calling the same tool with identical arguments. Which architectural pattern or configuration should you apply to prevent this behavior?

Accepted Answer

Configure a maximum iteration step limit in the agent orchestration loop. Agentic workflows in Azure AI Foundry (and similar frameworks like Semantic Kernel or AutoGen) require a bounded orchestration loop; configuring a maximum iteration or step limit causes the orchestrator to terminate or escalate when the agent exceeds the threshold, preventing infinite tool-calling loops. This is the standard architectural safeguard for multi-step reasoning agents. It directly addresses the symptom of repeated identical tool calls.

Answer

Increase the vector store chunk overlap size

Answer

Enable Azure API Management caching for the tool endpoint

Answer

Set the model frequency penalty to 2.0