Databricks-GenAI-Assoc Design Applications Practice Test 3 — 15 Questions

Question 1

What is the primary function of the 'Prompt' object in a Databricks RAG architecture?

Accepted Answer

To format instructions and retrieved context for LLM consumption.. The prompt acts as the interface between the data retrieved from the vector index and the LLM's generative capability. It organizes the context, instructions, and user request into a coherent message structure. Proper prompt design is essential to focus the LLM's attention, reduce extraneous output, and ensure that the generative output remains grounded in the provided evidence.

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To store the user's chat history for session persistence.

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To index documents for faster similarity search.

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To calculate the latency of the RAG pipeline.

Question 2

Which TWO factors should be considered when choosing an embedding model for a RAG application?

Accepted Answer

The model's native dimensionality and its effect on vector index storage size.. The choice of embedding model determines the quality of semantic retrieval and the computational requirements of the system. By aligning the model's language support, dimensionality, and performance with the application's needs, you ensure a balanced design. It is also vital to consider the model's licensing and whether it needs to be fine-tuned to capture domain-specific terminology that general models might misinterpret.

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The model's ability to generate creative, hallucinatory content.

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The maximum number of characters allowed in a single prompt.

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The color profile of the documents being ingested.

Question 3

Refer to the exhibit. What is the cause of this error when logging a RAG chain to MLflow?

Accepted Answer

The model was logged without a signature defining input and output types.. This error occurs because the model signature is missing or incorrectly defined, which is required for MLflow to perform input validation and inference. When serving a RAG chain, MLflow needs to know exactly what the input schema looks like so it can properly route requests and manage data types. Providing a valid signature ensures the model is deployable and functional.

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The vector index is offline.

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The user has insufficient Unity Catalog permissions.

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The LLM endpoint is overloaded.

Question 4

In a RAG application, which architectural component ensures that the system handles changes in the source data effectively?

Accepted Answer

The Delta Lake Change Data Feed (CDF).. The Delta Lake Change Data Feed (CDF) or the automatic synchronization of Vector Search indexes is designed specifically to track and apply changes (inserts, updates, deletes) to downstream systems. This ensures that the vector representation of the data is always in sync with the source of truth, avoiding the need for expensive full re-indexes and ensuring that retrieved information is always up-to-date.

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The LLM's internal knowledge base.

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The prompt engineering template.

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The user session cache.

Question 5

A GenAI engineer is building a Databricks RAG application where the retrieval step returns the top-5 chunks for each user question. The engineer wants to add a second LLM call that evaluates whether each retrieved chunk contains enough information to answer the question, and then filters out chunks that fail this evaluation before passing the remaining chunks to the final answer-generation prompt. Which design pattern is the engineer implementing?

Accepted Answer

Retrieval-augmented generation with relevance grading. The defining behavior in the scenario is a second LLM call that grades each retrieved chunk for answerability and drops the ones that fail. That is relevance grading layered on top of retrieval-augmented generation, which raises precision by keeping only context that can actually support the answer. The other techniques alter retrieval ranking, prompt reasoning, or embeddings but do not insert an evaluative filtering step between retrieval and generation.

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Chain-of-thought prompting

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Fine-tuning the embedding model on domain data

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Hybrid search combining vector similarity with BM25 keyword matching

Question 6

A GenAI engineer is building a Databricks RAG application that answers questions over a Delta table containing 40 million support tickets. Users report that simple keyword lookups return irrelevant results because the tickets use inconsistent terminology. The engineer needs semantic retrieval that stays synchronized as new tickets stream in every few minutes. Which Databricks component should be used to serve this retrieval layer?

Accepted Answer

A Databricks Vector Search index with a Delta Sync index over the tickets table. Semantic retrieval over frequently changing data requires both embedding-based similarity and automatic synchronization with the source table. A Databricks Vector Search Delta Sync index provides exactly this: it computes and maintains embeddings as the Delta table changes and exposes a low-latency endpoint for similarity queries, so terminology variations are handled by vector proximity rather than literal string matching.

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A Feature Store table that materializes ticket embeddings refreshed by a nightly job

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A Delta Live Tables pipeline that computes embeddings and writes them to a Parquet directory

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A Databricks SQL warehouse running a LIKE query over the ticket text column

Question 7

A generative AI engineer is building a RAG chain on Databricks using LangChain. The chain must call an external LLM provider via a Databricks Model Serving endpoint that proxies the provider, and the engineer wants to avoid hardcoding credentials in notebooks. Which approach should the engineer use to authenticate calls from the chain to the serving endpoint?

Accepted Answer

Configure the serving endpoint with the provider credential stored as a secret in the endpoint's environment, and call the endpoint using the Databricks SDK or LangChain's Databricks LLM class with workspace authentication.. The serving endpoint is the correct place to hold the external provider credential because it centralizes secret storage, rotation, and auditing. Clients then authenticate to the endpoint with workspace identity rather than the provider key. LangChain's Databricks LLM integration and the Databricks SDK both support this pattern, keeping credentials out of notebooks and enabling Unity Catalog governance over the endpoint.

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Pass the provider API key as a plaintext argument in the LangChain ChatOpenAI constructor and rely on notebook access controls to protect it.

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Use a personal access token generated for a service principal and embed it in the chain's prompt template so the LLM can forward it to the provider.

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Store the provider API key in a Databricks secret scope and reference it with dbutils.secrets.get inside the notebook before instantiating the LLM client.

Question 8

A GenAI engineer is building a customer-support assistant on Databricks. The assistant must invoke an external LLM endpoint that requires an API token, and the token must not appear in notebook source or logs. The engineer wants to store the token once and let notebooks and Databricks Jobs reference it by name. Which Databricks capability should the engineer use to satisfy this requirement?

Accepted Answer

Databricks secrets backed by a secret scope. Databricks secrets provide a governed, auditable store for sensitive values and expose them through dbutils.secrets.get or secret references, so the external LLM token is never embedded in code or logs. Other storage locations either expose plaintext to readers or serve a different purpose, such as authenticating to the workspace rather than to an external provider.

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A Unity Catalog volume mounted at /Volumes/secure/token.txt

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A workspace environment variable set in the cluster Spark configuration

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A personal access token stored in the Databricks CLI configuration file

Question 9

A team is designing a Databricks RAG application that must return answers with citations to source documents. They plan to use Databricks Vector Search and want the LLM to reference specific chunks. Which two design choices are required to produce reliable citations? (Choose two.)

Accepted Answer

Store a stable document identifier and chunk metadata alongside each embedding in the Vector Search index.. Reliable citations require two things: the retriever must return source identifiers, and those identifiers must reach the LLM in the prompt. Storing document IDs and chunk metadata in the Vector Search index enables the first, and including them in the prompt context enables the second. Similarity metrics, temperature, and inference tables do not provide the linkage needed for citations.

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Increase the LLM temperature to encourage the model to generate more detailed citations.

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Enable inference tables on the Vector Search endpoint to capture citation metadata.

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Set the Vector Search index to use a cosine similarity metric instead of dot product.

Question 10

An engineer has a Databricks Vector Search index that stores chunk embeddings for a technical manual. Users report that queries containing exact part numbers return irrelevant chunks because the embedding model blurs numeric tokens. The engineer wants retrieval to consider both dense vector similarity and exact keyword matching on the same Delta table without building a separate search system. Which Databricks Vector Search feature should the engineer enable?

Accepted Answer

Hybrid search on the index. Hybrid search augments dense vector similarity with keyword matching, which restores recall for exact tokens like part numbers while preserving semantic matching for natural-language queries. The other options affect index freshness, capacity, or result volume, but none of them introduce the exact-match signal that the scenario requires.

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A larger embedding model dimension

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A higher number of returned results per query

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Delta table change data feed on the source table

Question 11

A GenAI engineer is building a RAG chatbot backed by a Databricks Vector Search index that is synced from a Delta table with Change Data Feed enabled. Source documents are frequently updated, and some are deleted. The engineer wants the index to reflect deletions and updates automatically with minimal operational overhead. Which configuration should be used?

Accepted Answer

Create a Delta Sync index with a pipeline_type of CONTINUOUS, pointing at the source Delta table, and let Databricks manage synchronization.. Automatic reflection of inserts, updates, and deletes from a Delta source into a Vector Search index is achieved with a Delta Sync index running in CONTINUOUS pipeline mode, which consumes the source table's Change Data Feed. Triggered refreshes, Direct Vector Access, and index-side Change Data Feed all either add manual work, introduce staleness, or rely on unsupported behavior.

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Create a Direct Vector Access index and write a Structured Streaming job that upserts embeddings into it whenever the source table changes.

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Create a Delta Sync index and enable Change Data Feed on the index itself so that deletions flow back into the source Delta table.

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Create a Delta Sync index with a pipeline_type of TRIGGERED and schedule a Databricks Job to run a full refresh every 15 minutes.

Question 12

An engineer is designing a RAG application where the LLM must answer questions using only the retrieved context and must refuse to answer when the context is insufficient. Which prompt design approach best enforces this behavior?

Accepted Answer

Use a system prompt that instructs the model to answer only from the provided context and to respond with a fixed refusal phrase when the context does not contain the answer.. The most reliable way to constrain a model to retrieved context is a system prompt that explicitly limits answers to that context and defines a refusal response when the information is missing. This gives the model a clear rule and a fallback, reducing hallucinations. Retrieval volume, temperature, and question repetition do not establish grounding boundaries.

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Increase the number of retrieved chunks to ten so the model always has enough context to answer.

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Set the model's temperature to zero and rely on deterministic decoding to prevent unsupported answers.

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Append the user's question twice in the prompt to reinforce the instruction to stay grounded.

Question 13

A GenAI team at a retail company has built a RAG chatbot on Databricks that answers customer questions from a product catalog stored in Delta Lake. The catalog is updated nightly, and the team wants the chatbot to reflect those updates without manual intervention. They are deciding how to keep the Vector Search index synchronized. Which approach should they use?

Accepted Answer

Configure a Vector Search Delta Sync index on the catalog table so the index is refreshed automatically when the source table changes.. A Delta Sync index is the Databricks Vector Search construct that keeps an index aligned with a Delta table, including incremental changes. Because the catalog is a Delta table that changes nightly, configuring Delta Sync lets the chatbot retrieve current product information without a custom rebuild job. The other choices either fail to update the index or introduce unnecessary full recomputation.

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Use a Databricks SQL scheduled query to copy the catalog table into a Parquet file and point the retriever at that file each night.

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Create a standard Vector Search index and schedule a daily notebook that deletes the index and recreates it from the catalog table.

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Enable Change Data Feed on the catalog table and rely on it to update the Vector Search index automatically.

Question 14

A team is deploying a RAG chatbot that answers questions from internal policy documents. The documents change frequently, and the team wants retrieval to reflect updates within minutes without re-running a full embedding job. The source Delta table already has change data feed enabled. Which approach should the team use to keep the Databricks Vector Search index current?

Accepted Answer

Configure the index to sync from the Delta table so changes are propagated incrementally. A Delta-synced vector search index consumes change data feed events to apply inserts, updates, and deletes incrementally, keeping embeddings aligned with the source table. Rebuilding the index or tuning the endpoint addresses cost and throughput rather than freshness, and metadata filters only narrow an already-indexed set.

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Increase the cluster size of the vector search endpoint

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Drop and recreate the vector search index on a schedule

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Add a metadata filter for the document modification timestamp

Question 15

A team is designing a GenAI application that must call an external LLM provider from a Databricks notebook and from a Model Serving endpoint. The security team requires that the provider API key never appear in notebook source code, Git history, or the model artifact, and that the same governed credential be reused by both the notebook and the serving endpoint. Which design satisfies these requirements?

Accepted Answer

Store the API key in a Unity Catalog connection that supports external services, and reference that connection from both the notebook and the Model Serving endpoint.. Centralizing the external provider credential in a Unity Catalog connection keeps plaintext out of source code, Git history, and artifacts while letting both notebooks and Model Serving endpoints reference the same governed object. Secret scopes with manual injection, widget defaults, and cluster Spark configurations either leak the value or fail to reach the serving runtime.

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Hard-code the API key as a widget default in the notebook and mirror it into the serving endpoint's environment variables so both components share one value.

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Store the API key in a Databricks secret scope and reference it with dbutils.secrets.get in the notebook, then pass the resolved value into the serving endpoint's environment variables.

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Place the API key in a cluster-scoped Spark configuration on the interactive cluster, and have the serving endpoint read the same configuration at request time.