AI-102 Implement agentic AI solutions Practice Question
This AI-102 practice question tests your understanding of implement agentic ai solutions. Match the stated requirement to the specific cloud service, access model, or configuration option — many options are valid in isolation but not for this scenario. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
A company is building an agent that uses Azure OpenAI to answer questions from a large document library. The agent must use a Retrieval Augmented Generation (RAG) pattern. Which TWO actions should the team take to implement RAG effectively?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Index the documents into a vector database like Azure Cognitive Search.
Option C is correct because indexing documents into a vector database like Azure Cognitive Search enables efficient similarity search over embeddings, which is the retrieval foundation of RAG. This allows the system to quickly find the most relevant document chunks based on semantic meaning, rather than relying on the model to memorize or be fine-tuned on the entire library.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
✗
Ensure the model is large enough to memorize the entire document library.
Why it's wrong here
Memorization is not feasible for large libraries.
✗
Fine-tune the Azure OpenAI model on the document library.
Why it's wrong here
Fine-tuning is not required for RAG.
✓
Index the documents into a vector database like Azure Cognitive Search.
Why this is correct
Indexing enables efficient retrieval of relevant content.
Related concept
Read the scenario before looking for a memorised answer.
✗
Train a custom language model from scratch.
Why it's wrong here
RAG uses a pretrained model.
✓
Use a retrieval step to fetch relevant document chunks before generating a response.
Why this is correct
Retrieval is the core of RAG.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often confuse fine-tuning (which adapts model behavior) with RAG (which augments prompts with retrieved data), leading them to select Option B instead of understanding that RAG requires an external retrieval step and vector index.
Detailed technical explanation
How to think about this question
In a RAG implementation, documents are chunked and each chunk is converted into a vector embedding using a model like text-embedding-ada-002. Azure Cognitive Search with a vector index performs Approximate Nearest Neighbor (ANN) search using algorithms like HNSW, returning the top-k chunks whose embeddings are closest in cosine distance to the query embedding. These chunks are then injected into the prompt context window of the Azure OpenAI model (e.g., GPT-4) to ground the generation in retrieved facts, reducing hallucination.
KKey Concepts to Remember
Read the scenario before looking for a memorised answer.
Find the constraint that changes the correct option.
Eliminate answers that are true in general but not in this case.
TExam Day Tips
→Watch for words such as best, first, most likely and least administrative effort.
→Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
What to study next
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Implement agentic AI solutions — This question tests Implement agentic AI solutions — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Index the documents into a vector database like Azure Cognitive Search. — Option C is correct because indexing documents into a vector database like Azure Cognitive Search enables efficient similarity search over embeddings, which is the retrieval foundation of RAG. This allows the system to quickly find the most relevant document chunks based on semantic meaning, rather than relying on the model to memorize or be fine-tuned on the entire library.
What should I do if I get this AI-102 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Question Discussion
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