DATABRICKS-GENERATIVE-AI-ENGINEER-ASSOCIATE UPGRADE DUMPS, DATABRICKS-GENERATIVE-AI-ENGINEER-ASSOCIATE HOTTEST CERTIFICATION

Databricks-Generative-AI-Engineer-Associate Upgrade Dumps, Databricks-Generative-AI-Engineer-Associate Hottest Certification

Databricks-Generative-AI-Engineer-Associate Upgrade Dumps, Databricks-Generative-AI-Engineer-Associate Hottest Certification

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Databricks Databricks-Generative-AI-Engineer-Associate Exam Syllabus Topics:

TopicDetails
Topic 1
  • Assembling and Deploying Applications: In this topic, Generative AI Engineers get knowledge about coding a chain using a pyfunc mode, coding a simple chain using langchain, and coding a simple chain according to requirements. Additionally, the topic focuses on basic elements needed to create a RAG application. Lastly, the topic addresses sub-topics about registering the model to Unity Catalog using MLflow.
Topic 2
  • Design Applications: The topic focuses on designing a prompt that elicits a specifically formatted response. It also focuses on selecting model tasks to accomplish a given business requirement. Lastly, the topic covers chain components for a desired model input and output.
Topic 3
  • Governance: Generative AI Engineers who take the exam get knowledge about masking techniques, guardrail techniques, and legal
  • licensing requirements in this topic.

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Databricks-Generative-AI-Engineer-Associate Hottest Certification | Databricks-Generative-AI-Engineer-Associate Pass Exam

The Databricks Databricks-Generative-AI-Engineer-Associate practice questions come with three easy-to-use and install formats. The certification for the Databricks Databricks-Generative-AI-Engineer-Associate exam is a valuable, well-recognized professional credential. You can develop your skills and become a recognized specialist with the Databricks Certified Generative AI Engineer Associate Databricks-Generative-AI-Engineer-Associate Certification in addition to learning about new technology requirements.

Databricks Certified Generative AI Engineer Associate Sample Questions (Q40-Q45):

NEW QUESTION # 40
A Generative AI Engineer just deployed an LLM application at a digital marketing company that assists with answering customer service inquiries.
Which metric should they monitor for their customer service LLM application in production?

  • A. Final perplexity scores for the training of the model
  • B. Energy usage per query
  • C. Number of customer inquiries processed per unit of time
  • D. HuggingFace Leaderboard values for the base LLM

Answer: C

Explanation:
When deploying an LLM application for customer service inquiries, the primary focus is on measuring the operational efficiency and quality of the responses. Here's whyAis the correct metric:
* Number of customer inquiries processed per unit of time: This metric tracks the throughput of the customer service system, reflecting how many customer inquiries the LLM application can handle in a given time period (e.g., per minute or hour). High throughput is crucial in customer service applications where quick response times are essential to user satisfaction and business efficiency.
* Real-time performance monitoring: Monitoring the number of queries processed is an important part of ensuring that the model is performing well under load, especially during peak traffic times. It also helps ensure the system scales properly to meet demand.
Why other options are not ideal:
* B. Energy usage per query: While energy efficiency is a consideration, it is not the primary concern for a customer-facing application where user experience (i.e., fast and accurate responses) is critical.
* C. Final perplexity scores for the training of the model: Perplexity is a metric for model training, but it doesn't reflect the real-time operational performance of an LLM in production.
* D. HuggingFace Leaderboard values for the base LLM: The HuggingFace Leaderboard is more relevant during model selection and benchmarking. However, it is not a direct measure of the model's performance in a specific customer service application in production.
Focusing on throughput (inquiries processed per unit time) ensures that the LLM application is meeting business needs for fast and efficient customer service responses.


NEW QUESTION # 41
A Generative Al Engineer is building a system which will answer questions on latest stock news articles.
Which will NOT help with ensuring the outputs are relevant to financial news?

  • A. Increase the compute to improve processing speed of questions to allow greater relevancy analysis C Implement a profanity filter to screen out offensive language
  • B. Implement a comprehensive guardrail framework that includes policies for content filters tailored to the finance sector.
  • C. Incorporate manual reviews to correct any problematic outputs prior to sending to the users

Answer: A

Explanation:
In the context of ensuring that outputs are relevant to financial news, increasing compute power (option B) does not directly improve therelevanceof the LLM-generated outputs. Here's why:
* Compute Power and Relevancy:Increasing compute power can help the model process inputs faster, but it does not inherentlyimprove therelevanceof the answers. Relevancy depends on the data sources, the retrieval method, and the filtering mechanisms in place, not on how quickly the model processes the query.
* What Actually Helps with Relevance:Other methods, like content filtering, guardrails, or manual review, can directly impact the relevance of the model's responses by ensuring the model focuses on pertinent financial content. These methods help tailor the LLM's responses to the financial domain and avoid irrelevant or harmful outputs.
* Why Other Options Are More Relevant:
* A (Comprehensive Guardrail Framework): This will ensure that the model avoids generating content that is irrelevant or inappropriate in the finance sector.
* C (Profanity Filter): While not directly related to financial relevancy, ensuring the output is clean and professional is still important in maintaining the quality of responses.
* D (Manual Review): Incorporating human oversight to catch and correct issues with the LLM's output ensures the final answers are aligned with financial content expectations.
Thus, increasing compute power does not help with ensuring the outputs are more relevant to financial news, making option B the correct answer.


NEW QUESTION # 42
A Generative AI Engineer has created a RAG application which can help employees retrieve answers from an internal knowledge base, such as Confluence pages or Google Drive. The prototype application is now working with some positive feedback from internal company testers. Now the Generative Al Engineer wants to formally evaluate the system's performance and understand where to focus their efforts to further improve the system.
How should the Generative AI Engineer evaluate the system?

  • A. Curate a dataset that can test the retrieval and generation components of the system separately. Use MLflow's built in evaluation metrics to perform the evaluation on the retrieval and generation components.
  • B. Use an LLM-as-a-judge to evaluate the quality of the final answers generated.
  • C. Use cosine similarity score to comprehensively evaluate the quality of the final generated answers.
  • D. Benchmark multiple LLMs with the same data and pick the best LLM for the job.

Answer: A

Explanation:
* Problem Context: After receiving positive feedback for the RAG application prototype, the next step is to formally evaluate the system to pinpoint areas for improvement.
* Explanation of Options:
* Option A: While cosine similarity scores are useful, they primarily measure similarity rather than the overall performance of an RAG system.
* Option B: This option provides a systematic approach to evaluation by testing both retrieval and generation components separately. This allows for targeted improvements and a clear understanding of each component's performance, using MLflow's metrics for a structured and standardized assessment.
* Option C: Benchmarking multiple LLMs does not focus on evaluating the existing system's components but rather on comparing different models.
* Option D: Using an LLM as a judge is subjective and less reliable for systematic performance evaluation.
OptionBis the most comprehensive and structured approach, facilitating precise evaluations and improvements on specific components of the RAG system.


NEW QUESTION # 43
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?

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

Answer: 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 whyDis 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.


NEW QUESTION # 44
A Generative AI Engineer is creating an LLM-powered application that will need access to up-to-date news articles and stock prices.
The design requires the use of stock prices which are stored in Delta tables and finding the latest relevant news articles by searching the internet.
How should the Generative AI Engineer architect their LLM system?

  • A. Create an agent with tools for SQL querying of Delta tables and web searching, provide retrieved values to an LLM for generation of response.
  • B. Use an LLM to summarize the latest news articles and lookup stock tickers from the summaries to find stock prices.
  • C. Query the Delta table for volatile stock prices and use an LLM to generate a search query to investigate potential causes of the stock volatility.
  • D. Download and store news articles and stock price information in a vector store. Use a RAG architecture to retrieve and generate at runtime.

Answer: A

Explanation:
To build an LLM-powered system that accesses up-to-date news articles and stock prices, the best approach is tocreate an agentthat has access to specific tools (option D).
* Agent with SQL and Web Search Capabilities:By using an agent-based architecture, the LLM can interact with external tools. The agent can query Delta tables (for up-to-date stock prices) via SQL and perform web searches to retrieve the latest news articles. This modular approach ensures the system can access both structured (stock prices) and unstructured (news) data sources dynamically.
* Why This Approach Works:
* SQL Queries for Stock Prices: Delta tables store stock prices, which the agent can query directly for the latest data.
* Web Search for News: For news articles, the agent can generate search queries and retrieve the most relevant and recent articles, then pass them to the LLM for processing.
* Why Other Options Are Less Suitable:
* A (Summarizing News for Stock Prices): This convoluted approach would not ensure accuracy when retrieving stock prices, which are already structured and stored in Delta tables.
* B (Stock Price Volatility Queries): While this could retrieve relevant information, it doesn't address how to obtain the most up-to-date news articles.
* C (Vector Store): Storing news articles and stock prices in a vector store might not capture the real-time nature of stock data and news updates, as it relies on pre-existing data rather than dynamic querying.
Thus, using an agent with access to both SQL for querying stock prices and web search for retrieving news articles is the best approach for ensuring up-to-date and accurate responses.


NEW QUESTION # 45
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