AI-300 Generative AI Optimization Practice Test 2 — 15 Questions

Question 1

An application is hitting rate limits on the Azure OpenAI service. Which action is the most standard approach for handling this in production?

Accepted Answer

Implement exponential backoff in the client application.. The standard production approach for handling rate limits (HTTP 429) from Azure OpenAI is to implement retry logic with exponential backoff in the client application. Azure OpenAI returns a 429 status code with a Retry-After header when the tokens-per-minute (TPM) or requests-per-minute (RPM) quota is exceeded, and the client should wait and retry with progressively longer delays. This is the documented Microsoft-recommended pattern and is also built into the Azure OpenAI SDKs via retry policies.

Answer

Delete the deployment and recreate it.

Answer

Disable the rate limit in the portal.

Answer

Increase the temperature of the model.

Question 2

You are optimizing a model for a specific domain language. Which fine-tuning approach minimizes cost while maximizing domain adaptation?

Accepted Answer

Using LoRA (Low-Rank Adaptation).. LoRA (Low-Rank Adaptation) freezes the pretrained model weights and injects small trainable low-rank matrices into attention layers, so only a tiny fraction of parameters are updated. This dramatically reduces GPU memory, compute, and storage costs compared to full fine-tuning while still adapting the model to domain-specific language. It is the standard cost-efficient fine-tuning technique for domain adaptation.

Answer

Training the model from scratch.

Answer

Prompt engineering alone.

Answer

Full parameter fine-tuning.

Question 3

You notice your model is outputting redundant information. Which parameter specifically targets the penalty for repeating tokens?

Accepted Answer

frequency_penalty. The frequency_penalty parameter applies a penalty proportional to how many times a token has already appeared in the generated text, directly discouraging repeated tokens and reducing redundancy. Its value ranges from -2.0 to 2.0, with positive values increasing the penalty for frequent tokens.

Answer

max_tokens

Answer

presence_penalty

Answer

top_p

Question 4

You have a large set of documents for a RAG system. How should you optimize retrieval speed?

Accepted Answer

Store embeddings in a dedicated vector database with indexing.. Storing embeddings in a dedicated vector database with an ANN index (HNSW, IVF) enables sub-linear nearest-neighbor search instead of scanning every document. This is the standard architecture for fast retrieval over large corpora in RAG systems. The index precomputes a graph or cluster structure so queries touch only a small fraction of vectors.

Answer

Process all documents in the prompt.

Answer

Increase the model's max_tokens.

Answer

Perform a brute-force search on all documents.

Question 5

A user wants to restrict the model's output to valid JSON format. What is the most effective way to ensure this?

Accepted Answer

Enable JSON mode/Response Format in the API configuration.. Enabling JSON mode (Response Format) in the API configuration is the most effective way to constrain model output to valid JSON because the model is instructed and, in supported models, constrained to emit syntactically valid JSON. This is a first-class API feature (response_format: { type: 'json_object' }) that enforces structure at generation time. Prompt instructions alone are probabilistic, and post-processing only fixes output after the fact.

Answer

Increase the temperature.

Answer

Add 'output must be JSON' to the prompt.

Answer

Post-process the output with a regex script.

Question 6

Which THREE factors contribute to increased latency in an LLM application?

Accepted Answer

The size/complexity of the chosen model.. Model size, input/output token count, and network transit time are the main drivers of latency.

Answer

The number of system messages used.

Answer

The use of JSON output mode.

Question 7

Which TWO techniques should you use to improve the accuracy of a RAG pipeline?

Accepted Answer

Upgrading to a more advanced embedding model.. Option B is correct because upgrading to a more advanced embedding model improves the semantic quality of the vectors used for retrieval, so semantically similar passages are matched more accurately and relevant context is more likely to be retrieved. Option D is correct because optimizing the document chunking strategy (for example, choosing appropriate chunk sizes and overlaps that preserve coherent context) directly improves retrieval precision and ensures the retrieved passages contain the information needed to ground the generated answer. Option A is not correct because simply reducing the number of documents in the vector store shrinks the knowledge base and can remove relevant content rather than improve retrieval accuracy. Option C is not correct because increasing the temperature to 2.0 makes the model's output more random and less faithful, which harms answer accuracy rather than improving it. Option E is not correct because using one single long chunk for all documents dilutes the embedding with irrelevant content and reduces retrieval precision, so it degrades RAG accuracy.

Answer

Reducing the number of documents in the vector store.

Answer

Increasing the temperature to 2.0.

Answer

Using only one single long chunk for all documents.

Question 8

Which THREE steps are necessary to successfully fine-tune an Azure OpenAI model?

Accepted Answer

Prepare and upload a training dataset in JSONL format.. Option B is correct because Azure OpenAI fine-tuning requires a training dataset uploaded as a JSONL file with chat-completion formatted examples (messages with role/content pairs), which the service uses to adapt the model. Option D is correct because you must choose a base model that supports fine-tuning (for example gpt-35-turbo, gpt-4o-mini, or babbage-002/davinci-002) and that is available in the region where you create the fine-tune job. Option E is correct because after submitting the fine-tuning job you track its status and metrics (training loss, token usage, and completion state) via the Azure OpenAI Studio or the REST API, and you only deploy the resulting model once the job succeeds. Option A is not needed because Azure OpenAI manages the weight updates internally; you never edit model weights in code. Option C is not required because content filtering is a separate safety configuration and disabling the input filter is neither necessary nor recommended for fine-tuning.

Answer

Manually update the model weights in the code.

Answer

Disable the input filter in the portal.

Question 9

Which TWO of the following are effective ways to reduce hallucination?

Accepted Answer

Explicitly instructing the model to say 'I don't know' if the context is insufficient.. Option D is correct because explicitly instructing the model to say 'I don't know' when the context is insufficient gives it a permissible fallback behavior, reducing the tendency to fabricate plausible-sounding but unsupported answers. Option E is correct because supplying ground-truth reference context in the prompt anchors the model's response in verified source material, a technique commonly called retrieval-augmented or grounded generation, which measurably lowers hallucination rates. Option A is incorrect because raising the temperature to 0.7 increases sampling randomness, which typically increases rather than reduces hallucination. Option B is incorrect because removing the system prompt strips away behavioral constraints and role guidance, generally making the model less reliable and more prone to unsupported output. Option C is incorrect because requesting the longest possible response length encourages verbosity and elaboration beyond the available evidence, which tends to increase hallucinated content rather than reduce it.

Answer

Increasing the temperature to 0.7.

Answer

Removing the system prompt.

Answer

Using the longest possible response length.

Question 10

Which TWO of the following strategies are commonly used to optimize for cost in Generative AI?

Accepted Answer

Selecting the smallest capable model for the task.. Option A is correct because choosing the smallest capable model for a task directly reduces cost, since inference pricing scales with model size and capability—smaller models consume fewer compute resources per token. Option B is correct because caching frequent prompts or responses avoids redundant model invocations, so repeated queries are served from cache instead of incurring new token-generation charges. Option C is not a cost optimization: expanding the context window to 128k increases the number of input tokens processed per request, which typically raises cost. Option D is not a cost optimization: verbose logging increases storage and observability overhead without reducing inference spend. Option E is not a cost optimization: using the highest possible temperature affects output randomness, not token pricing, and can even increase retries or output length.

Answer

Increasing the context window size to 128k.

Answer

Enabling verbose logging.

Answer

Using the highest possible temperature.

Question 11

Which THREE metrics are critical for monitoring a production Generative AI system?

Accepted Answer

Latency (Time-to-first-token).. Option A (Latency, specifically Time-to-first-token) is critical because in a production Generative AI system, TTFT directly measures the perceived responsiveness of streaming responses, and high TTFT degrades user experience and can indicate model, network, or inference bottlenecks. Option D (Token usage per request) is critical because tokens drive both cost and throughput in LLM serving; tracking prompt and completion tokens per request enables accurate chargeback, quota enforcement, and capacity planning against rate limits such as tokens-per-minute. Option E (Error rates, HTTP 4xx/5xx) is critical because 4xx codes reveal client-side issues like authentication failures, throttling (429), or malformed prompts, while 5xx codes expose server-side faults such as model timeouts or backend unavailability, making them essential for reliability and SLO monitoring. Option B (hardware temperature of the Azure server) is not a critical application-level metric for a Generative AI system because Azure abstracts physical hardware and exposes health through platform metrics rather than requiring per-server temperature tracking. Option C (model training loss) is not appropriate here because it is an offline training-time metric and does not reflect the runtime health, cost, or reliability of a deployed production inference system.

Answer

The hardware temperature of the Azure server.

Answer

Model training loss.

Question 12

Which TWO methods are best for debugging an LLM pipeline that fails on complex queries?

Accepted Answer

Inspecting raw input and output tokens via logs.. Option A is correct because inspecting raw input and output tokens via logs reveals exactly what prompt text and context the model received and what it generated, exposing truncation, malformed prompt templates, or unexpected tokenization that cause failures on complex queries. Option B is correct because tracing the request flow through the RAG pipeline shows each stage — query rewriting, embedding, vector retrieval, reranking, and final generation — so you can pinpoint which component drops or corrupts information for complex queries. Option C is wrong because adding API users only increases load and does not diagnose pipeline failures. Option D is wrong because disabling security controls introduces risk without providing any debugging insight. Option E is wrong because deleting and rebuilding the deployment is a destructive, non-diagnostic action that may erase logs and state needed for root-cause analysis.

Answer

Increasing the number of API users.

Answer

Disabling all security controls.

Answer

Deleting the deployment and rebuilding it.

Question 13

You are building a generative AI assistant for a financial advisory firm using Azure OpenAI. The assistant must answer questions based on a large repository of internal policy documents that are updated frequently. You need to reduce hallucination and ensure the model cites the most current policy versions. Which approach should you implement?

Accepted Answer

Use Retrieval-Augmented Generation (RAG) with Azure AI Search to retrieve relevant document chunks and include them in the prompt.. Retrieval-Augmented Generation with Azure AI Search retrieves up-to-date document chunks and injects them into the prompt, grounding the model's responses in current policies and reducing hallucination. This dynamic approach is ideal for frequently updated repositories, as the search index can be refreshed independently of model retraining. Other options either fail to provide current information or increase randomness.

Answer

Increase the temperature setting to encourage more creative and comprehensive answers.

Answer

Fine-tune the base model on the entire document repository weekly.

Answer

Deploy a larger model with a higher token limit to accommodate more context.

Question 14

You are optimizing a generative AI application that uses Azure OpenAI's GPT-4 model for summarizing long legal contracts. The summaries are often incomplete because the model truncates important details from the middle of the contract. You need to improve the completeness of the summaries without exceeding the model's context window. What should you do?

Accepted Answer

Use a chunking strategy with overlapping segments and summarize each chunk, then combine the summaries.. Chunking with overlapping segments and hierarchical summarization allows the model to process the entire contract in parts, preserving context at boundaries and combining partial summaries into a complete one. This approach works within the model's context window and avoids truncation. Increasing output tokens or lowering temperature does not solve input truncation, and switching to a larger model may still be insufficient and costly.

Answer

Switch to a model with a larger context window, such as GPT-4-32k, and pass the entire contract.

Answer

Increase the max_tokens parameter to allow longer summaries.

Answer

Lower the temperature to 0 to make the model more deterministic.

Question 15

You are monitoring an Azure OpenAI deployment used for a customer support chatbot. You notice that the model sometimes generates responses that include fabricated product features. You want to implement a technique that encourages the model to say 'I don't know' when it lacks information, rather than guessing. Which approach should you use?

Accepted Answer

Use a system message that instructs the model to only answer if it is confident and to otherwise respond with 'I don't know'.. A system message that instructs the model to only answer when confident and to otherwise respond with 'I don't know' directly leverages the model's instruction-following capability to reduce fabrication. This prompt engineering approach is effective for encouraging honesty and is easy to implement. Other options adjust randomness or filter harmful content but do not address the core issue of hallucination.

Answer

Enable content filtering to block harmful or inappropriate responses.

Answer

Increase the frequency_penalty parameter to discourage repetitive tokens.

Answer

Decrease the top_p parameter to 0.1 to make the output more focused.