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| Certification Vendor: | Databricks |
|---|---|
| Exam Name: | Databricks Certified Generative AI Engineer Associate |
| Exam Number: | Databricks-Generative-AI-Engineer-Associate |
| Certificate Validity Period: | 2 years |
| Related Certifications: | Databricks Certified Data Engineer Associate Databricks Certified Machine Learning Associate |
| Exam Price: | USD 200 |
| Exam Format: | Multiple choice, Multiple select |
| Exam Duration: | 90 minutes |
| Available Languages: | English |
| Real Exam Qty: | 45-60 |
| Recommended Training: | Mosaic AI Documentation Databricks Academy - Generative AI Courses |
| Exam Registration: | Databricks Certification Portal |
| Sample Questions: | Databricks Databricks-Generative-AI-Engineer-Associate Sample Questions |
| Exam Way: | Online proctored exam |
| Pre Condition: | No strict prerequisites, but experience with Python, machine learning fundamentals, and Databricks platform is recommended. |
| Official Syllabus URL: | https://www.databricks.com/learn/certification |
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NEW QUESTION # 69
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?
Answer: B
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 # 70
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?
Answer: C
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.
NEW QUESTION # 71
A Generative AI Engineer is designing an LLM-powered live sports commentary platform. The platform provides real-time updates and LLM-generated analyses for any users who would like to have live summaries, rather than reading a series of potentially outdated news articles.
Which tool below will give the platform access to real-time data for generating game analyses based on the latest game scores?
Answer: D
Explanation:
* Problem Context: The engineer is developing an LLM-powered live sports commentary platform that needs to provide real-time updates and analyses based on the latest game scores. The critical requirement here is the capability to access and integrate real-time data efficiently with the platform for immediate analysis and reporting.
* Explanation of Options:
* Option A: DatabricksIQ: While DatabricksIQ offers integration and data processing capabilities, it is more aligned with data analytics rather than real-time feature serving, which is crucial for immediate updates necessary in a live sports commentary context.
* Option B: Foundation Model APIs: These APIs facilitate interactions with pre-trained models and could be part of the solution, but on their own, they do not provide mechanisms to access real- time game scores.
* Option C: Feature Serving: This is the correct answer as feature serving specifically refers to the real-time provision of data (features) to models for prediction. This would be essential for an LLM that generates analyses based on live game data, ensuring that the commentary is current and based on the latest events in the sport.
* Option D: AutoML: This tool automates the process of applying machine learning models to real-world problems, but it does not directly provide real-time data access, which is a critical requirement for the platform.
Thus,Option C(Feature Serving) is the most suitable tool for the platform as it directly supports the real-time data needs of an LLM-powered sports commentary system, ensuring that the analyses and updates are based on the latest available information.
NEW QUESTION # 72
A Generative Al Engineer is developing a RAG application and would like to experiment with different embedding models to improve the application performance.
Which strategy for picking an embedding model should they choose?
Answer: C
Explanation:
The task involves improving a Retrieval-Augmented Generation (RAG) application's performance by experimenting with embedding models. The choice of embedding model impacts retrieval accuracy, which is critical for RAG systems. Let's evaluate the options based on Databricks Generative AI Engineer best practices.
Option A: Pick an embedding model trained on related domain knowledge
Embedding models trained on domain-specific data (e.g., industry-specific corpora) produce vectors that better capture the semantics of the application's context, improving retrieval relevance. For RAG, this is a key strategy to enhance performance.
Databricks Reference: "For optimal retrieval in RAG systems, select embedding models aligned with the domain of your data" ("Building LLM Applications with Databricks," 2023).
Option B: Pick the most recent and most performant open LLM released at the time LLMs are not embedding models; they generate text, not embeddings for retrieval. While recent LLMs may be performant for generation, this doesn't address the embedding step in RAG. This option misunderstands the component being selected.
Databricks Reference: Embedding models and LLMs are distinct in RAG workflows: "Embedding models convert text to vectors, while LLMs generate responses" ("Generative AI Cookbook").
Option C: Pick the embedding model ranked highest on the Massive Text Embedding Benchmark (MTEB) leaderboard hosted by HuggingFace The MTEB leaderboard ranks models across general tasks, but high overall performance doesn't guarantee suitability for a specific domain. A top-ranked model might excel in generic contexts but underperform on the engineer's unique data.
Databricks Reference: General performance is less critical than domain fit: "Benchmark rankings provide a starting point, but domain-specific evaluation is recommended" ("Databricks Generative AI Engineer Guide").
Option D: Pick an embedding model with multilingual support to support potential multilingual user questions Multilingual support is useful only if the application explicitly requires it. Without evidence of multilingual needs, this adds complexity without guaranteed performance gains for the current use case.
Databricks Reference: "Choose features like multilingual support based on application requirements" ("Building LLM-Powered Applications").
Conclusion: Option A is the best strategy because it prioritizes domain relevance, directly improving retrieval accuracy in a RAG system-aligning with Databricks' emphasis on tailoring models to specific use cases.
NEW QUESTION # 73
A Generative AI Engineer has been reviewing issues with their company's LLM-based question-answering assistant and has determined that a technique called prompt chaining could help alleviate some performance concerns. However, to suggest this to their team, they have to clearly explain how it works and how it can benefit their question-answering assistant. Which explanation do they communicate to the team?
Answer: A
Explanation:
Prompt chaining is a fundamental design pattern in LLM application development used to handle complexity. Instead of sending a single, massive, and highly complex prompt to an LLM-which often results in reasoning errors or hallucinations-chaining breaks the logic into a sequence of smaller, targeted steps. For example, a legal assistant might first chain a step to "identify the legal jurisdiction," followed by a step to "extract relevant statutes," and finally a step to "summarize the findings." This modularity improves reliability because each prompt has a narrower focus, making it easier for the model to follow instructions accurately. While it may actually increase latency (contradicting B) and cost (contradicting D) due to multiple API calls, the primary engineering benefit is the significant boost in the quality and robustness of the output. It also allows for intermediate validation and error handling between steps, which is impossible in a single-call architecture.
NEW QUESTION # 74
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