Free CERTIFIED-GENERATIVE-AI-ENGINEER-ASSOCIATE – Databricks CERTIFIED-GENERATIVE-AI-ENGINEER-ASSOCIATE Practice Test Question

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• ○ Increase the frequency of upstream data updates
• ○ Inform the user of the expected RAG behavior
• ○ Restrict access to the data sources to a limited number of users
• ○ Curate upstream data properly that includes manual review before it is fed into the RAG system

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• ○ Split the data into train and test set, create a unique identifier for each document, then save to a Delta table
• ○ Flatten the dataframe to one chunk per row, create a unique identifier for each row, and save to a Delta table
• ○ First create a unique identifier for each document, then save to a Delta table
• ○ Store each chunk as an independent JSON file in Unity Catalog Volume. For each JSON file, the key is the document section name and the value is the array of text chunks for that section

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• ○ The chatbot should only look at previous event information
• ○ There should be two different chatbots handling different types of user queries.
• ○ The chatbot should be implemented as a multi-step LLM workflow. First, identify the type of question asked, then route the question to the appropriate model. If it’s an upcoming event question, send the query to a text-to-SQL model. If it’s about ticket purchasing, the customer should be redirected to a payment platform.
• ○ The chatbot should only process payments

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• ○ Compare the cosine similarities of the embeddings of returned results against those of a representative sample of test inputs
• ○ Compare the Bilingual Evaluation Understudy (BLEU) scores of returned results for a representative sample of test inputs
• ○ Compare the Recall-Oriented-Understudy for Gisting Evaluation (ROUGE) scores of returned results for a representative sample of test inputs
• ○ Compare the Levenshtein distances of returned results against a representative sample of test inputs

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• ○ Create a Feature Serving Endpoint from a FeatureSpec that references an online store synced from the Delta table. Query the Feature Serving Endpoint as part of the agent logic / tool implementation.
• ○ Query the Delta table directly via a SQL query constructed from the user’s input using a text-to-SQL LLM in the agent logic / tool implementation.
• ○ Set up a task in Databricks Workflows to write the information in the Delta table periodically to an external database such as MySQL and query the information from there as part of the agent logic / tool implementation.
• ○ Write the Delta table contents to a text column, then embed those texts using an embedding model and store these in the vector index. Look up the information based on the embedding as part of the agent logic / tool implementation.

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• ○ Include in the system prompt that the application is not supposed to answer any questions unrelated to technology.
• ○ Filter out irrelevant news articles in the retrieval process.
• ○ Keep all news articles because the RAG application needs to understand non-technological content to avoid answering questions about them.
• ○ Filter out irrelevant news articles in the upstream document database.

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• ○ Parse the electrical equipment PDF manuals into a table of question and response pairs. That way, the same chatbot can query tables easily to answer questions about both historical failure rates and equipment troubleshooting.
• ○ The chatbot should be implemented as a multi-step LLM workflow. First, identify the type of question asked, then route the question to the appropriate model. If it’s a historical failure rate question, send the query to a text-to-SQL model. If it’s a troubleshooting question, then send the query to another model that summarizes the equipment-specific document and generates the response.
• ○ There should be two different chatbots handling different types of user queries.
• ○ The table with electrical part failures should be converted into a text document first. That way, the same chatbot can use the same document retrieval process to generate answers regardless of question types.

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• ○ It allows you to break down complex tasks into multiple independent subtasks. This enables the assistant to generate more comprehensive and accurate responses.
• ○ It allows you to reduce the latency of your applications. By having multiple chains participating in the response as a chain, you increase the rate at which the response is generated.
• ○ It allows you to decrease the effort involved in crafting a prompt. Chains make it possible to reuse prompt text across multiple different use cases.
• ○ It reduces the average cost of a typical request. Chains make more efficient use of the tokens produced to generate higher quality responses with fewer tokens.

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• ○ Big Code Models Leaderboard
• ○ Number of model parameters
• ○ MTEB Leaderboard
• ○ Chatbot Arena Leaderboard
• ○ Number of model downloads last month

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• ○ Operational metrics like request volume, latency, and errors
• ○ Quality metrics like correctness and guideline adherence
• ○ Both operational and quality metrics
• ○ No metrics are automatically captured