AI-103 Practice Test 24 — 15 Questions

Question 1

Your enterprise requires strict security and governance over your Azure AI Vision resources. Which THREE security configurations should you implement? (Choose three.)

Accepted Answer

Configure Azure Private Endpoints to restrict access to your Virtual Network.. Option C is correct because Azure Private Endpoints create a private IP address for the Azure AI Vision resource inside your Virtual Network, eliminating exposure to the public internet and enforcing network-level isolation. Option D is correct because enabling diagnostic logging and streaming audit records to Log Analytics provides the governance and auditability required for strict security monitoring, retention, and compliance reporting. Option E is correct because Microsoft Entra ID RBAC provides fine-grained, identity-based authorization with conditional access and managed identities, which is more secure and governable than shared API keys alone. Option A is wrong because storing API keys in plain text on public GitHub repositories exposes credentials to anyone and violates every security best practice. Option B is wrong because disabling TLS removes encryption in transit, exposing data to interception and violating compliance requirements; latency gains do not justify this risk.

Answer

Store API keys in plain text configuration files on public GitHub repositories.

Answer

Disable TLS encryption to improve API response latency.

Question 2

You are optimizing an Azure OpenAI deployment in Azure AI Foundry for high-throughput batch processing. Which TWO strategies or configurations should you consider? (Choose TWO)

Accepted Answer

Azure OpenAI Batch API for asynchronous high-volume jobs at reduced costs. Option A is correct because the Azure OpenAI Batch API is purpose-built for asynchronous, high-volume workloads, processing requests in the background with a separate quota and offering roughly 50% lower cost than synchronous calls, which directly suits high-throughput batch processing. Option C is correct because Provisioned Throughput Units (PTUs) reserve dedicated model capacity for a deployment, guaranteeing consistent throughput and predictable latency rather than being throttled by shared pay-as-you-go quota, which is essential for sustained high-volume jobs. Option B is wrong because disabling SSL certificate validation weakens transport security and is never a valid optimization strategy. Option D is wrong because setting max_tokens to 1 would truncate responses to a single token, breaking output quality rather than improving throughput. Option E is wrong because routing production traffic through public developer workstations is insecure, unreliable, and not a supported architecture for scaling Azure OpenAI workloads.

Answer

Disabling SSL certificate validation in the client SDK

Answer

Setting model max_tokens to 1

Answer

Routing all traffic through public developer local workstations

Question 3

You are deploying Custom Vision containers on-premises using Docker. Which THREE prerequisites and configuration steps are required? (Choose three.)

Accepted Answer

Ensure internet connectivity or periodic connection to Azure for billing metering.. Option A is correct because Custom Vision containers must periodically connect to Azure (or maintain internet connectivity) to send usage/billing metering data; without this metering connection the container will not run. Option B is correct because the container requires the Billing Endpoint URI (e.g., https://<region>.api.cognitive.microsoft.com) and the associated API Key, supplied as environment variables such as Billing and ApiKey, so Azure can validate and meter the resource. Option C is correct because the container image requires explicit acceptance of the End-User License Agreement via the EULA=accept environment variable before it will start. Option D is not required because the Azure CLI is not installed inside the prediction container; billing is handled through the endpoint and key. Option E is not required and is not a supported configuration; no root access to the Azure billing portal from inside the container is needed.

Answer

Install the full Azure CLI inside every prediction container.

Answer

Grant root access to the Azure subscription billing portal from inside the container.

Question 4

You are designing an agentic workflow in Prompt Flow. Which TWO components are required to define a tool-based agent? (Choose two)

Accepted Answer

A LLM to act as the agent brain. Option A is correct because a tool-based agent in Prompt Flow requires an LLM to serve as the agent's reasoning brain, deciding which tools to invoke and how to interpret their outputs. Option B is correct because the agent must have tool definitions—provided as an OpenAPI specification or a Python function—so the LLM knows the available actions, their parameters, and how to call them. Option C is incorrect because a SQL Database is merely a possible backend a tool might query; it is not a required component of the agent definition. Option D is incorrect because a Power Automate cloud flow is an optional integration, not a mandatory element for defining a tool-based agent. Option E is incorrect because Azure Container Registry is only relevant for storing container images used in custom environments, not for defining the agent itself.

Answer

A SQL Database

Answer

A Power Automate cloud flow

Answer

An Azure Container Registry

Question 5

You are optimizing the cost of your Azure AI infrastructure. You notice several resources are underutilized or incorrectly provisioned. Which THREE actions should you take to reduce unnecessary spending? Each correct answer presents part of the solution.

Accepted Answer

Downgrade development and test resources from high-tier production SKUs to free or lower standard tiers where appropriate.. Option A is correct because non-production dev/test workloads rarely need high-tier production SKUs, so moving them to free or lower standard tiers (e.g., Azure AI Search Basic/Standard, Cognitive Services standard pricing tiers) directly cuts recurring compute and feature charges without affecting production SLAs. Option C is correct because abandoned or unused Azure AI resource accounts (e.g., orphaned Cognitive Services, Azure OpenAI, or Azure AI Search resources) still incur charges for provisioned capacity and stored data, so identifying and deleting them eliminates pure waste. Option D is correct because Azure Cost Management budgets and cost anomaly alerts provide proactive visibility and governance, letting you detect and remediate unexpected spend before it accumulates. Option B is not appropriate because Provisioned Throughput Units (PTUs) reserve dedicated Azure OpenAI capacity at a fixed hourly rate and are cost-effective only for steady, high-volume production workloads, not idle development sandboxes. Option E is not appropriate because permanently scaling Azure AI Search partitions and replicas to maximum capacity increases cost and is only justified by sustained indexing/query load or high-availability requirements, not cost reduction.

Answer

Provision dedicated Azure OpenAI PTUs for idle development sandboxes.

Answer

Scale out search service partitions and replicas to maximum capacity permanently.

Question 6

You are troubleshooting a scenario where client applications are unable to connect to an Azure AI Language resource secured by a Private Endpoint. Which TWO troubleshooting steps should you perform? Each correct answer presents part of the solution.

Accepted Answer

Check that the client subnet can route to the private IP address assigned to the Private Endpoint.. Option A is correct because a Private Endpoint exposes the Azure AI Language resource through a private IP address in the client's virtual network, so the client subnet must have valid routing (for example, via the default VNet routing or a configured route table) to reach that private IP; if routing is broken, connections fail even if DNS resolves correctly. Option D is correct because name resolution must map the Azure AI Language endpoint FQDN to the Private Endpoint's private IP, which requires the corresponding Private DNS zone (for example, privatelink.cognitiveservices.azure.com) to be linked to the client virtual network; without that link, clients resolve the public IP and cannot connect privately. Option B is not relevant because Storage Blob Data Contributor is an Azure Storage RBAC role and has no bearing on connecting to an Azure AI Language resource. Option C is not relevant because retraining Azure OpenAI model weights is unrelated to Private Endpoint connectivity for Azure AI Language. Option E is incorrect because enabling unrestricted public network access would bypass the private-only access model rather than troubleshoot the Private Endpoint connection, and it may violate the intended security configuration.

Answer

Verify that the client application has been assigned the Storage Blob Data Contributor role.

Answer

Ensure that the Azure OpenAI model weights have been retrained.

Answer

Check that public network access is set to enabled and unrestricted.

Question 7

When analyzing documents using Azure AI Document Intelligence, which THREE properties can be found within the returned JSON result object for an extracted table cell? (Choose THREE)

Accepted Answer

columnIndex. In the Azure AI Document Intelligence analyze-result JSON, each table cell is represented with its grid position and text: option A (columnIndex) is correct because the zero-based column position of the cell within the table is returned as the columnIndex property, and option E (rowIndex) is correct because the zero-based row position is returned as the rowIndex property. Option D (content) is also correct because the cell's recognized text is returned in the content field (with spans and boundingRegions tying it to the source). Options B (audioTranscript) and C (sentimentScore) do not belong: audioTranscript relates to speech-to-text transcription and sentimentScore to language sentiment analysis, neither of which is part of the Document Intelligence table-cell schema.

Answer

audioTranscript

Answer

sentimentScore

Question 8

You are configuring an agent in Azure AI Agent Service that needs to utilize tools. Which TWO types of tools are natively supported by Azure AI Agent Service? (Choose TWO)

Accepted Answer

Function calling (Custom tools/APIs). Azure AI Agent Service natively supports Function calling (option C), which lets the agent invoke custom tools and APIs by defining function schemas that the model can call to retrieve data or perform actions. It also natively supports Code Interpreter (option E), a sandboxed Python execution environment used for data analysis, math, and file generation. Options A, B, and D are not native tool types in Azure AI Agent Service: a 'stored procedure compiler,' a 'VM provisioning extension,' and a 'Stream Analytics tumbling window operator' are unrelated Azure capabilities and cannot be attached as agent tools.

Answer

Azure SQL Database stored procedure compiler

Answer

Azure Virtual Machine provisioning extension

Answer

Azure Stream Analytics tumbling window operator

Question 9

You are configuring an Azure AI Language resource. Which TWO pricing tiers are available for provisioning the resource in the Azure portal? (Choose two.)

Accepted Answer

Standard (S). The Standard (S) tier is correct because Azure AI Language resources are provisioned under the Standard (S) pricing tier, which provides the full production-level throughput and SLA-backed service for text analytics, question answering, and conversational language understanding features. The Free (F0) tier is also correct because Azure AI Language offers a Free (F0) tier for evaluation and low-volume testing, subject to limited transactions, document size, and rate limits per subscription. The Unlimited (U1) option is not a valid Azure AI Language pricing tier; no such tier exists in the Azure portal provisioning flow. Developer (DEV) is not an Azure AI Language tier; developer-oriented access is handled through keys and endpoints on S or F0, not a separate DEV SKU. Enterprise (E1) is likewise not an Azure AI Language pricing tier; enterprise capabilities are delivered through the Standard tier rather than a distinct E1 SKU.

Answer

Unlimited (U1)

Answer

Developer (DEV)

Answer

Enterprise (E1)

Question 10

Which THREE settings in the Azure AI Foundry playground can directly affect the 'creativity' of an agent's response?

Accepted Answer

Temperature.. Temperature (A) is correct because it scales the randomness of the model's token probability distribution—higher values like 1.0+ flatten the distribution and produce more varied, 'creative' outputs, while lower values sharpen it toward deterministic responses. Top-P (C), or nucleus sampling, is correct because it restricts sampling to the smallest set of tokens whose cumulative probability exceeds P; raising P (e.g., 0.9–1.0) admits more candidate tokens and increases output diversity. Frequency Penalty (D) is correct because it penalizes tokens proportionally to how often they have already appeared, discouraging repetition and thereby pushing the model toward more varied, novel phrasing. The search index threshold (B) governs retrieval grounding—how many or how similar documents must be before they are surfaced—so it affects factual grounding, not generative creativity. The system prompt length (E) is not a creativity parameter; prompt content and length shape behavior and context, but they do not directly tune the sampling distribution the way temperature, top-P, and frequency penalty do.

Answer

The search index threshold.

Answer

The system prompt length.

Question 11

When implementing grounding in a RAG application using Azure AI Foundry, which TWO practices help minimize hallucinations in model outputs? (Choose TWO)

Accepted Answer

Adding explicit system prompt instructions telling the model to refuse answers not supported by context. Option A is correct because explicit system prompt instructions that tell the model to refuse or say it doesn't know when the retrieved context does not support an answer constrain the model to grounded responses and reduce fabricated content. Option B is correct because including source document chunk citations alongside the retrieved text in the prompt context gives the model verifiable references and encourages it to anchor its output to the supplied evidence rather than inventing facts. Option C is incorrect because disabling content safety filters removes guardrails and does nothing to improve grounding; it can actually increase harmful or unsupported output. Option D is incorrect because setting temperature to 2.0 maximizes randomness and creativity, which increases hallucination risk rather than reducing it. Option E is incorrect because removing all source context documents eliminates the grounding data entirely, forcing the model to rely on parametric memory and making hallucinations far more likely.

Answer

Disabling content safety filters to allow unrestricted generation

Answer

Setting the model temperature parameter to 2.0 to maximize creativity

Answer

Removing all source context documents from the prompt entirely

Question 12

You are preparing a dataset for fine-tuning an Azure OpenAI model in Azure AI Foundry. Which TWO data formatting and preparation requirements must you follow? (Choose TWO)

Accepted Answer

Format training data as JSONL (JSON Lines) where each line is a chat completion example. Option C is correct because Azure OpenAI fine-tuning requires the training file to be in JSONL (JSON Lines) format, where each line is a self-contained JSON object representing one training example, typically a chat completion example with a messages array. Option D is correct because for chat models the messages must use the expected role structure — system, user, and assistant — so the model learns the conversational mapping between prompts and desired responses; the assistant message provides the target completion. Option A is wrong because including unencrypted passwords or PII is a security and compliance violation and does nothing to improve model quality. Option B is wrong because raw unstructured PDFs cannot be uploaded directly as a fine-tuning dataset; the data must first be parsed and converted into the required JSONL chat format. Option E is wrong because .wav audio files are for speech/audio models, not for fine-tuning a text-based Azure OpenAI chat model.

Answer

Include unencrypted passwords and PII in plain text to improve model empathy

Answer

Upload raw unstructured PDF files directly as the fine-tuning training dataset

Answer

Encode all training examples in binary audio wave files (.wav)

Question 13

You are designing a generative AI application in Azure AI Foundry that supports multi-turn conversations with memory. Which THREE components are required to properly maintain conversational state and context? (Choose THREE)

Accepted Answer

Thread or session state storage to persist message history across turns. Option A is correct because maintaining conversational state requires a thread or session store that persists the message history across turns, allowing the model to reference earlier exchanges in a multi-turn dialogue. Option D is correct because even with stored history, the application must append prior messages into the prompt payload sent to the model so the context is actually available during inference. Option E is correct because as history grows it will exceed the model's context window, so a strategy such as summarization, truncation, or sliding-window management is needed to prevent token overflow and keep requests valid. Option B is incorrect because deleting all messages after every turn destroys the memory that multi-turn conversations depend on. Option C is incorrect because temperature controls randomness of sampling, not memory, and setting it to 0.0 does not erase or manage conversational state.

Answer

Permanently deleting all messages after every single API turn

Answer

Setting temperature to 0.0 to erase all memory between calls

Question 14

You are building a customer service agent in Azure AI Foundry. The agent needs to call an external inventory management REST API when customers ask about stock levels. Which feature of Azure AI Agent Service should you implement to allow the agent to interact with this external API?

Accepted Answer

Function calling (Tools). Function calling (Tools) in Azure AI Agent Service allows the agent to invoke external APIs by defining tool schemas that describe the API's parameters and expected responses. When a customer asks about stock levels, the agent emits a structured function call that the runtime executes against the inventory REST API, then feeds the result back into the model to formulate a natural-language answer. This is the purpose-built mechanism for connecting agents to external systems.

Answer

Prompt shields

Answer

Azure AI Content Safety filters

Answer

Azure Cognitive Search index

Question 15

You are deploying a custom text classification model using Azure AI Language. The model undergoes frequent retraining as new business data arrives. You need to establish a model versioning strategy that allows zero-downtime client updates while ensuring seamless rollback capability if regression occurs. What is the recommended architectural approach?

Accepted Answer

Deploy each model version with a unique deployment name and update the application client configuration or routing rules to shift traffic progressively.. Azure AI Language custom models are consumed through deployments, and each deployment has a unique name that maps to a specific model version. By deploying each new version under a unique deployment name and updating client configuration or routing rules, you can shift traffic progressively and roll back instantly by pointing clients back to the previous deployment. This achieves zero-downtime updates and seamless rollback.

Answer

Delete the original resource before deploying the updated model version.

Answer

Overwrite the existing model artifact in place with the new training run using the exact same name.

Answer

Create a completely new Azure AI resource in a different subscription for every single model iteration.