AI-103 Implement Generative AI And Agentic Solutions Practice Test 5 — 15 Questions

Question 1

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 2

You are building an advanced agentic solution in Azure AI Foundry that integrates with external line-of-business systems. Which THREE architectural patterns or features should you incorporate to ensure robustness and safety? (Choose THREE)

Accepted Answer

Human-in-the-loop review for high-impact tool actions. Option A (Human-in-the-loop review for high-impact tool actions) is correct because agentic solutions that call external line-of-business systems can perform destructive or financially significant operations, so gating high-impact tool invocations behind human approval provides a safety control that prevents irreversible mistakes. Option D (Exponential backoff and jitter retry logic for handling API rate limits (HTTP 429)) is correct because external LOB APIs commonly throttle callers and return HTTP 429, and retrying with exponentially increasing delays plus randomized jitter avoids thundering-herd retries and improves robustness against transient throttling. Option E (Structured Outputs with strict JSON Schema validation for tool payloads) is correct because enforcing a strict JSON Schema on tool inputs and outputs guarantees that the agent emits well-formed, type-safe arguments that the downstream LOB system can parse, reducing malformed calls and injection-style errors. Option B is wrong because executing arbitrary bash commands on production servers without validation is a severe security and reliability risk, not a robustness pattern. Option C is wrong because disabling logging and monitoring removes observability, auditability, and the ability to detect failures or misuse, which directly undermines safety and robustness.

Answer

Allowing the agent to execute any bash command directly on production servers without validation

Answer

Disabling all logging and monitoring to maximize execution speed

Question 3

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 4

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 5

When configuring vector embeddings and search indexes in Azure AI Search for RAG, which THREE factors directly impact vector search performance and recall accuracy? (Choose THREE)

Accepted Answer

Embedding model dimension size and consistency with source generation. Option A is correct because the embedding model's dimension size determines the vector field's dimensionality in the Azure AI Search index, and it must match the dimensions produced by the model used to generate the query embeddings; a mismatch causes search failures or degraded recall, and higher dimensions generally increase accuracy at the cost of memory and latency. Option C is correct because HNSW parameters directly govern the approximate nearest neighbor graph: 'm' controls the number of bidirectional links per node (affecting index size and recall), while 'efSearch' (efConstruction/efSearch) tunes the breadth of the search during query time, trading latency for recall. Option D is correct because the similarity metric (Cosine, Dot Product, or Euclidean) must align with how the embedding model was trained; using the wrong metric distorts distance comparisons and reduces recall accuracy. Option B is incorrect because Azure App Service CPU architecture is unrelated to Azure AI Search vector indexing or querying, which runs on the search service's own infrastructure. Option E is incorrect because Azure Front Door DNS CNAME records only affect traffic routing and hostname resolution, not vector search performance or recall.

Answer

Azure App Service CPU architecture (x64 vs ARM)

Answer

Configuring Azure Front Door DNS CNAME records

Question 6

You are troubleshooting a multi-agent workflow in Azure AI Foundry where an agent fails to invoke an external API tool correctly. Which THREE debugging steps should you perform? (Choose THREE)

Accepted Answer

Test network connectivity and authentication credentials between the agent service and the external API endpoint. Option A is correct because when an agent fails to invoke an external API tool, you must verify that the agent service can actually reach the endpoint and that its authentication credentials (API keys, OAuth tokens, managed identity, etc.) are valid, since connectivity or auth failures are common root causes of tool invocation errors. Option C is correct because inspecting the model's generated tool call arguments against the expected JSON Schema reveals whether the model is producing malformed, missing, or incorrectly typed parameters that the external API would reject. Option D is correct because the tool definition schema given to the model—including accurate descriptions, parameter names, types, and required fields—directly determines whether the model can generate a valid call; vague or incorrect schemas cause the model to hallucinate or omit arguments. Option B does not belong because restarting the client browser session only affects the local UI and has no bearing on server-side agent-to-API tool invocation logic. Option E does not belong because increasing Azure DNS TTL affects caching duration for domain resolution and would not fix an agent's failure to correctly construct or authenticate an external API tool call.

Answer

Restart the client web browser session

Answer

Increase the Azure DNS TTL value for domain resolution

Question 7

You are monitoring an Azure OpenAI model deployed in Azure AI Foundry. Which TWO Azure diagnostic metrics or logs should you analyze to track operational health and performance? (Choose TWO)

Accepted Answer

Request duration (latency) in milliseconds. Option A, request duration (latency) in milliseconds, is correct because Azure OpenAI in Azure AI Foundry emits latency metrics that directly reflect the responsiveness and operational health of the deployed model endpoint, making it a primary signal for performance monitoring. Option C, prompt tokens and completion tokens consumed per request, is correct because token consumption metrics are emitted by Azure OpenAI and are essential for tracking throughput, capacity, quota usage, and cost/performance behavior of the model deployment. Options B, D, and E are incorrect because ExpressRoute circuit bandwidth saturation, Azure SQL Database deadlock frequency, and VM disk IOPS consumption are metrics for networking, database, and compute infrastructure respectively, not for an Azure OpenAI model deployment's operational health and performance.

Answer

Azure ExpressRoute circuit bandwidth saturation

Answer

Azure SQL Database deadlock frequency

Answer

Virtual machine disk IOPS consumption

Question 8

When designing an Azure AI Foundry project architecture, which TWO resources are typically linked to the Azure AI Hub to enable full model deployment and search capabilities? (Choose TWO)

Accepted Answer

Azure AI Search service. Option A (Azure AI Search service) is correct because an Azure AI Search resource is a standard linked dependency of an Azure AI Hub/project, providing the vector and keyword indexing backend needed for search, retrieval, and RAG-style grounding scenarios. Option C (Azure OpenAI service) is correct because linking an Azure OpenAI resource to the hub supplies the deployed GPT and embedding models that the project uses for model deployment and inference. Option B (Azure SQL Database) is not a typical hub-linked resource; relational data can be connected separately but it is not required for model deployment or search. Option D (Azure Stream Analytics) is a real-time stream processing service unrelated to Foundry hub model/search enablement. Option E (Power BI embedded) is a visualization/analytics service and plays no role in linking model deployment or search capabilities to an AI Hub.

Answer

Azure SQL Database relational server

Answer

Azure Stream Analytics job cluster

Answer

Power BI embedded analytics workspace

Question 9

You are implementing indirect prompt injection defenses in a RAG application where documents are ingested from public web pages. Which THREE mitigation strategies should you deploy? (Choose THREE)

Accepted Answer

Use strict structural delimiters (like XML tags) to isolate retrieved context from system instructions. Option A is correct because strict structural delimiters such as XML tags (e.g., ...) create a clear boundary between untrusted retrieved document text and trusted system instructions, making it harder for injected instructions inside web content to be interpreted as authoritative commands. Option D is correct because output validation filters inspect model completions for unauthorized actions, data exfiltration patterns, or policy violations, providing a defense-in-depth control that catches injection effects even if the input-side isolation fails. Option E is correct because Azure AI Content Safety's Prompt Shields feature is specifically designed to detect both direct (user) and indirect (document) prompt injection attacks, including spotlighting techniques used in RAG ingestion pipelines. Option B is incorrect because trusting all incoming web page text unconditionally is the exact vulnerability that enables indirect prompt injection, since public pages can contain adversarial instructions. Option C is incorrect because disabling content moderation filters removes a key safety layer and increases the risk that malicious or harmful injected content is processed and acted upon.

Answer

Trust all incoming web page text unconditionally without inspection

Answer

Disable all content moderation filters to maximize processing speed

Question 10

When designing an evaluation pipeline in Azure AI Foundry for generative AI applications, which TWO metrics are commonly used to assess safety and risk? (Choose TWO)

Accepted Answer

Sexual content and violence safety metrics. Option C is correct because Azure AI Foundry's safety evaluation metrics specifically include sexual content and violence severity scores, which quantify harmful content categories in generated outputs and are part of the built-in risk and safety evaluators. Option E is correct because hate speech and unfairness detection are also standard safety metrics in Azure AI Foundry, covering hateful or discriminatory language and bias/unfairness assessments of model responses. These metrics align with the Azure AI Content Safety categories (hate, violence, sexual, self-harm) plus fairness evaluations used to assess generative AI risk. Options A and B are incorrect because database transaction log growth rate and storage account geo-replication latency are infrastructure/storage performance measures, not safety or risk metrics for generative AI. Option D is incorrect because network bandwidth throughput in megabits per second is a networking performance metric, unrelated to evaluating content safety or fairness risks.

Answer

Database transaction log growth rate

Answer

Storage account geo-replication latency

Answer

Network bandwidth throughput in megabits per second

Question 11

You are optimizing an enterprise RAG application in Azure AI Foundry for high accuracy and minimal latency. Which THREE advanced retrieval techniques should you consider implementing? (Choose THREE)

Accepted Answer

Semantic ranking to re-rank top search results using deep learning models. Option A (Semantic ranking) is correct because Azure AI Search's semantic ranker applies deep-learning language models to re-rank the top BM25/vector results, promoting the most contextually relevant passages and directly improving RAG answer accuracy. Option C (Hybrid search combining full-text BM25 and vector embeddings) is correct because fusing keyword and dense-vector retrieval via Reciprocal Rank Fusion captures both exact term matches and semantic similarity, which raises recall and precision over either method alone. Option E (Query expansion and re-writing) is correct because enriching the user prompt with synonyms and related terms broadens the candidate set and aligns the query with the indexed content, improving retrieval quality for ambiguous or short queries. Option B does not belong because random sentence selection without indexing provides no relevance signal or deterministic retrieval, guaranteeing poor accuracy. Option D does not belong because disabling vector search and relying on random string matching discards semantic retrieval entirely, which destroys both accuracy and the ability to handle natural-language queries.

Answer

Using random sentence selection without indexing

Answer

Disabling vector search entirely to rely solely on random string matching

Question 12

When configuring role-based access control (RBAC) in Azure AI Foundry and Azure OpenAI, which TWO built-in Azure roles are commonly assigned to developers and services? (Choose TWO)

Accepted Answer

Cognitive Services OpenAI User. Option B, Cognitive Services OpenAI User, is correct because it is the built-in role that grants data-plane access to Azure OpenAI resources, allowing developers and services to call inference endpoints such as chat completions and embeddings without granting management permissions. Option E, Azure AI Developer, is correct because it is the built-in role designed for building and managing Azure AI Foundry projects and hubs, giving developers the permissions needed to create projects, connections, and deploy models while scoping access appropriately. Option A, Storage Blob Data Owner, is not appropriate here since it governs blob storage data access, not Azure OpenAI or AI Foundry RBAC. Option C, Virtual Machine Contributor, manages VMs and is unrelated to AI service access. Option D, Network Contributor, manages networking resources and does not grant the AI/OpenAI data-plane permissions required for this scenario.

Answer

Storage Blob Data Owner

Answer

Virtual Machine Contributor

Answer

Network Contributor

Question 13

You are configuring a Microsoft Foundry project to deploy a multimodal model. You need to ensure that the model deployment scales automatically based on request volume while minimizing management overhead. Which deployment type should you select?

Accepted Answer

Standard deployment. A Standard deployment in Microsoft Foundry (Azure AI Foundry) for a multimodal model provides automatic scaling based on request volume while minimizing management overhead. It is a pay-as-you-go deployment type that dynamically allocates compute resources to handle traffic, making it ideal for variable workloads without the need for manual provisioning or capacity planning.

Answer

Local container deployment

Answer

Provisioned Throughput deployment

Answer

Batch deployment

Question 14

You are implementing Retrieval-Augmented Generation (RAG) using Azure AI Search as the retriever and Azure OpenAI as the generator. Users report that the generated responses frequently miss critical context located in the middle of long retrieved documents. Which grounding optimization technique should you apply?

Accepted Answer

Implement semantic ranking and reorder retrieved documents so the highest-scored chunks appear at the very beginning and end of the prompt context.. LLMs exhibit a 'lost in the middle' bias where content at the beginning and end of the context window is attended to more strongly than content in the middle. Applying semantic ranking to surface the most relevant chunks and then reordering them so the highest-scored chunks sit at the start and end of the prompt mitigates this positional bias and improves grounding.

Answer

Increase the temperature parameter of the Azure OpenAI model to 1.2 to encourage creative synthesis of the middle content.

Answer

Switch the Azure AI Search retriever from hybrid search to pure keyword BM25 search.

Answer

Reduce the chunk size to 64 tokens to force the retriever to return hundreds of tiny fragments.

Question 15

You are building an agentic workflow in Azure AI Foundry using the Semantic Kernel framework. The agent needs to call external APIs dynamically based on user intent. Which component should you register with the kernel to enable this capability?

Accepted Answer

A plugin containing native or semantic functions. In Semantic Kernel, plugins encapsulate native functions (code) or semantic functions (prompts) that the kernel can invoke. Registering a plugin with the kernel enables the agent to call external APIs dynamically based on user intent, as the kernel orchestrates function calls.

Answer

A system prompt template

Answer

A sequential planner instance

Answer

A custom memory store