Certified Generative AI Engineer Associate Exam Questions in PDF and Online

QUESTION: 6

A Generative AI Engineer has a provisioned throughput model serving endpoint as part of a RAG

application and would like to monitor the serving endpoint's incoming requests and outgoing responses. The current approach is to include a micro-service in between the endpoint and the user interface to write logs to a remote server.

Which Databricks feature should they use instead which will perform the same task?

Vector Search
Lakeview
DBSQL
Inference Tables

Answer(s): D

Explanation:

Problem Context: The goal is to monitor the serving endpoint for incoming requests and outgoing responses in a provisioned throughput model serving endpoint within a Retrieval-Augmented Generation (RAG) application. The current approach involves using a microservice to log requests and responses to a remote server, but the Generative AI Engineer is looking for a more streamlined solution within Databricks.

Explanation of Options:

Option A: Vector Search: This feature is used to perform similarity searches within vector databases. It doesn't provide functionality for logging or monitoring requests and responses in a serving endpoint, so it's not applicable here.

Option B: Lakeview: Lakeview is not a feature relevant to monitoring or logging request-response cycles for serving endpoints. It might be more related to viewing data in Databricks Lakehouse but doesn't fulfill the specific monitoring requirement.

Option C: DBSQL: Databricks SQL (DBSQL) is used for running SQL queries on data stored in Databricks, primarily for analytics purposes. It doesn't provide the direct functionality needed to monitor requests and responses in real-time for an inference endpoint.

Option D: Inference Tables: This is the correct answer. Inference Tables in Databricks are designed to store the results and metadata of inference runs. This allows the system to log incoming requests and outgoing responses directly within Databricks, making it an ideal choice for monitoring the behavior of a provisioned serving endpoint. Inference Tables can be queried and analyzed, enabling easier monitoring and debugging compared to a custom microservice.

Thus, Inference Tables are the optimal feature for monitoring request and response logs within the Databricks infrastructure for a model serving endpoint.

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QUESTION: 7

A Generative Al Engineer is tasked with improving the RAG quality by addressing its inflammatory outputs.

Which action would be most effective in mitigating the problem of offensive text outputs?

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

Answer(s): D

Explanation:

Addressing offensive or inflammatory outputs in a Retrieval-Augmented Generation (RAG) system is critical for improving user experience and ensuring ethical AI deployment. Here's why D is the most effective approach:

Manual data curation: The root cause of offensive outputs often comes from the underlying data used to train the model or populate the retrieval system. By manually curating the upstream data and conducting thorough reviews before the data is fed into the RAG system, the engineer can filter out harmful, offensive, or inappropriate content.

Improving data quality: Curating data ensures the system retrieves and generates responses from a high-quality, well-vetted dataset. This directly impacts the relevance and appropriateness of the outputs from the RAG system, preventing inflammatory content from being included in responses.

Effectiveness: This strategy directly tackles the problem at its source (the data) rather than just mitigating the consequences (such as informing users or restricting access). It ensures that the system consistently provides non-offensive, relevant information.

Other options, such as increasing the frequency of data updates or informing users about behavior expectations, may not directly mitigate the generation of inflammatory outputs.

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QUESTION: 8

A Generative Al Engineer is creating an LLM-based application. The documents for its retriever have been chunked to a maximum of 512 tokens each. The Generative Al Engineer knows that cost and latency are more important than quality for this application. They have several context length levels to choose from.

Which will fulfill their need?

context length 514; smallest model is 0.44GB and embedding dimension 768
context length 2048: smallest model is 11GB and embedding dimension 2560
context length 32768: smallest model is 14GB and embedding dimension 4096
context length 512: smallest model is 0.13GB and embedding dimension 384

Answer(s): D

Explanation:

When prioritizing cost and latency over quality in a Large Language Model (LLM)-based application, it is crucial to select a configuration that minimizes both computational resources and latency while still providing reasonable performance. Here's why D is the best choice:

Context length: The context length of 512 tokens aligns with the chunk size used for the documents

(maximum of 512 tokens per chunk). This is sufficient for capturing the needed information and generating responses without unnecessary overhead.

Smallest model size: The model with a size of 0.13GB is significantly smaller than the other options. This small footprint ensures faster inference times and lower memory usage, which directly reduces both latency and cost.

Embedding dimension: While the embedding dimension of 384 is smaller than the other options, it is still adequate for tasks where cost and speed are more important than precision and depth of understanding.

This setup achieves the desired balance between cost-efficiency and reasonable performance in a latency-sensitive, cost-conscious application.

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QUESTION: 9

A small and cost-conscious startup in the cancer research field wants to build a RAG application using Foundation Model APIs.

Which strategy would allow the startup to build a good-quality RAG application while being cost- conscious and able to cater to customer needs?

Limit the number of relevant documents available for the RAG application to retrieve from
Pick a smaller LLM that is domain-specific
Limit the number of queries a customer can send per day
Use the largest LLM possible because that gives the best performance for any general queries

Answer(s): B

Explanation:

For a small, cost-conscious startup in the cancer research field, choosing a domain-specific and smaller LLM is the most effective strategy. Here's why B is the best choice:

Domain-specific performance: A smaller LLM that has been fine-tuned for the domain of cancer research will outperform a general-purpose LLM for specialized queries. This ensures high-quality responses without needing to rely on a large, expensive LLM.

Cost-efficiency: Smaller models are cheaper to run, both in terms of compute resources and API usage costs. A domain-specific smaller LLM can deliver good quality responses without the need for the extensive computational power required by larger models.

Focused knowledge: In a specialized field like cancer research, having an LLM tailored to the subject matter provides better relevance and accuracy for queries, while keeping costs low. Large, general- purpose LLMs may provide irrelevant information, leading to inefficiency and higher costs.

This approach allows the startup to balance quality, cost, and customer satisfaction effectively, making it the most suitable strategy.

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QUESTION: 10

A Generative Al Engineer is responsible for developing a chatbot to enable their company's internal HelpDesk Call Center team to more quickly find related tickets and provide resolution.
While creating the GenAI application work breakdown tasks for this project, they realize they need to start planning which data sources (either Unity Catalog volume or Delta table) they could choose for this application. They have collected several candidate data sources for consideration:

call_rep_history: a Delta table with primary keys representative_id, call_id. This table is maintained to calculate representatives' call resolution from fields call_duration and call start_time.

transcript Volume: a Unity Catalog Volume of all recordings as a *.wav files, but also a text transcript as *.txt files.

call_cust_history: a Delta table with primary keys customer_id, cal1_id. This table is maintained to calculate how much internal customers use the HelpDesk to make sure that the charge back model is consistent with actual service use.

call_detail: a Delta table that includes a snapshot of all call details updated hourly. It includes root_cause and resolution fields, but those fields may be empty for calls that are still active.

maintenance_schedule a Delta table that includes a listing of both HelpDesk application outages as well as planned upcoming maintenance downtimes.

They need sources that could add context to best identify ticket root cause and resolution.

Which TWO sources do that? (Choose two.)

call_cust_history
maintenance_schedule
call_rep_history
call_detail
transcript Volume

Answer(s): D,E

Explanation:

In the context of developing a chatbot for a company's internal HelpDesk Call Center, the key is to select data sources that provide the most contextual and detailed information about the issues being addressed. This includes identifying the root cause and suggesting resolutions. The two most appropriate sources from the list are:

Call Detail (Option D):

Contents: This Delta table includes a snapshot of all call details updated hourly, featuring essential fields like root_cause and resolution.

Relevance: The inclusion of root_cause and resolution fields makes this source particularly valuable, as it directly contains the information necessary to understand and resolve the issues discussed in the calls. Even if some records are incomplete, the data provided is crucial for a chatbot aimed at speeding up resolution identification.

Transcript Volume (Option E):

Contents: This Unity Catalog Volume contains recordings in .wav format and text transcripts in .txt files.

Relevance: The text transcripts of call recordings can provide in-depth context that the chatbot can analyze to understand the nuances of each issue. The chatbot can use natural language processing techniques to extract themes, identify problems, and suggest resolutions based on previous similar interactions documented in the transcripts.

Why Other Options Are Less Suitable:

A (Call Cust History): While it provides insights into customer interactions with the HelpDesk, it focuses more on the usage metrics rather than the content of the calls or the issues discussed.

B (Maintenance Schedule): This data is useful for understanding when services may not be available but does not contribute directly to resolving user issues or identifying root causes.

C (Call Rep History): Though it offers data on call durations and start times, which could help in assessing performance, it lacks direct information on the issues being resolved.

Therefore, Call Detail and Transcript Volume are the most relevant data sources for a chatbot designed to assist with identifying and resolving issues in a HelpDesk Call Center setting, as they provide direct and contextual information related to customer issues.

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Databricks Certified Generative AI Engineer Associate Certified Generative AI Engineer Associate Exam Questions in PDF

Free Databricks Certified Generative AI Engineer Associate Dumps Questions (page: 2)

QUESTION: 6

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QUESTION: 7

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QUESTION: 8

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QUESTION: 9

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QUESTION: 10

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