Databricks-Generative-AI-Engineer-Associate資格認定 & Databricks-Generative-AI-Engineer-Associate前提条件

無料でクラウドストレージから最新のPass4Test Databricks-Generative-AI-Engineer-Associate PDFダンプをダウンロードする：https://drive.google.com/open?id=14lTwW2m9YC1ojksvHa6yoU0sASD5_sYj

Databricks-Generative-AI-Engineer-Associate試験の教材を使用すると、夢をより確実に保護できます。これは、教材の合格率が高いためです。 Databricks-Generative-AI-Engineer-Associate学習教材は、Databricks-Generative-AI-Engineer-Associate学習ガイドの品質が業界を確実にリードし、完璧なサービスシステムを確保するために最も専門的なチームを選択しました。 Databricks-Generative-AI-Engineer-Associate学習教材の焦点と真剣さは、99％の合格率を与えます。当社の製品を使用すると、最も重要な合格率など、必要なすべてを取得できます。私たちのDatabricks-Generative-AI-Engineer-Associateの実際の試験は、あなたの夢の道で本当に良いヘルパーです。

Databricks Databricks-Generative-AI-Engineer-Associate Exam Overview:

Certification Vendor:	Databricks
Exam Name:	Databricks Certified Generative AI Engineer Associate
Exam Number:	Databricks-Generative-AI-Engineer-Associate
Related Certifications:	Databricks Certified Data Engineer Associate Databricks Certified Machine Learning Associate
Exam Duration:	90 minutes
Exam Price:	USD 200
Available Languages:	English
Certificate Validity Period:	2 years
Exam Format:	Multiple select, Multiple choice
Real Exam Qty:	45-60
Recommended Training:	Databricks Academy - Generative AI Courses Mosaic AI Documentation
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

>> Databricks-Generative-AI-Engineer-Associate資格認定 <<

Databricks-Generative-AI-Engineer-Associate前提条件、Databricks-Generative-AI-Engineer-Associate関連合格問題

すべての人にDatabricks-Generative-AI-Engineer-Associate試験問題を試す機会を提供するために、当社の専門家がすべての人向けのDatabricks-Generative-AI-Engineer-Associate準備ガイドの試用版を設計しました。当社の製品を購入することをheする場合。 Databricks-Generative-AI-Engineer-Associateテストプラクティスファイルを購入する前に、当社の試用版を試すことができます。試用版はデモを提供します。さらに重要なことは、当社のデモはすべての人にとって無料です。無料デモで、当社のDatabricks-Generative-AI-Engineer-Associate準備資料を深く理解できます。

Databricks Databricks-Generative-AI-Engineer-Associate 認定試験の出題範囲：

トピック	出題範囲
トピック 1	Data Preparation: Generative AI Engineers covers a chunking strategy for a given document structure and model constraints. The topic also focuses on filter extraneous content in source documents. Lastly, Generative AI Engineers also learn about extracting document content from provided source data and format.
トピック 2	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.
トピック 3	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.
トピック 4	Governance: Generative AI Engineers who take the exam get knowledge about masking techniques, guardrail techniques, and legal licensing requirements in this topic.
トピック 5	Application Development: In this topic, Generative AI Engineers learn about tools needed to extract data, Langchain similar tools, and assessing responses to identify common issues. Moreover, the topic includes questions about adjusting an LLM's response, LLM guardrails, and the best LLM based on the attributes of the application.

Databricks Certified Generative AI Engineer Associate 認定 Databricks-Generative-AI-Engineer-Associate 試験問題 (Q43-Q48):

質問 # 43
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

正解：A

解説：
In the context of ensuring that outputs are relevant to financial news, increasing compute power (option B) does not directly improve the relevance of 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 inherently improve the relevance of 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.

質問 # 44
A Generative AI Engineer is designing a RAG application for answering user questions on technical regulations as they learn a new sport.
What are the steps needed to build this RAG application and deploy it?

A. Ingest documents from a source -> Index the documents and saves to Vector Search -> User submits queries against an LLM -> LLM retrieves relevant documents -> Evaluate model -> LLM generates a response -> Deploy it using Model Serving
B. User submits queries against an LLM -> Ingest documents from a source -> Index the documents and save to Vector Search -> LLM retrieves relevant documents -> LLM generates a response -> Evaluate model -> Deploy it using Model Serving
C. Ingest documents from a source -> Index the documents and save to Vector Search -> User submits queries against an LLM -> LLM retrieves relevant documents -> LLM generates a response -> Evaluate model -> Deploy it using Model Serving
D. Ingest documents from a source -> Index the documents and save to Vector Search -> Evaluate model -> Deploy it using Model Serving

正解：C

解説：
The Generative AI Engineer needs to follow a methodical pipeline to build and deploy a Retrieval-Augmented Generation (RAG) application. The steps outlined in option B accurately reflect this process:
Ingest documents from a source: This is the first step, where the engineer collects documents (e.g., technical regulations) that will be used for retrieval when the application answers user questions.
Index the documents and save to Vector Search: Once the documents are ingested, they need to be embedded using a technique like embeddings (e.g., with a pre-trained model like BERT) and stored in a vector database (such as Pinecone or FAISS). This enables fast retrieval based on user queries.
User submits queries against an LLM: Users interact with the application by submitting their queries. These queries will be passed to the LLM.
LLM retrieves relevant documents: The LLM works with the vector store to retrieve the most relevant documents based on their vector representations.
LLM generates a response: Using the retrieved documents, the LLM generates a response that is tailored to the user's question.
Evaluate model: After generating responses, the system must be evaluated to ensure the retrieved documents are relevant and the generated response is accurate. Metrics such as accuracy, relevance, and user satisfaction can be used for evaluation.
Deploy it using Model Serving: Once the RAG pipeline is ready and evaluated, it is deployed using a model-serving platform such as Databricks Model Serving. This enables real-time inference and response generation for users.
By following these steps, the Generative AI Engineer ensures that the RAG application is both efficient and effective for the task of answering technical regulation questions.

質問 # 45
After changing the response generating LLM in a RAG pipeline from GPT-4 to a model with a shorter context length that the company self-hosts, the Generative AI Engineer is getting the following error:

What TWO solutions should the Generative AI Engineer implement without changing the response generating model? (Choose two.)

A. Use a smaller embedding model to generate
B. Decrease the chunk size of embedded documents
C. Reduce the maximum output tokens of the new model
D. Reduce the number of records retrieved from the vector database
E. Retrain the response generating model using ALiBi

正解：B、D

解説：
* Problem Context: After switching to a model with a shorter context length, the error message indicating that the prompt token count has exceeded the limit suggests that the input to the model is too large.
* Explanation of Options:
* Option A: Use a smaller embedding model to generate- This wouldn't necessarily address the issue of prompt size exceeding the model's token limit.
* Option B: Reduce the maximum output tokens of the new model- This option affects the output length, not the size of the input being too large.
* Option C: Decrease the chunk size of embedded documents- This would help reduce the size of each document chunk fed into the model, ensuring that the input remains within the model's context length limitations.
* Option D: Reduce the number of records retrieved from the vector database- By retrieving fewer records, the total input size to the model can be managed more effectively, keeping it within the allowable token limits.
* Option E: Retrain the response generating model using ALiBi- Retraining the model is contrary to the stipulation not to change the response generating model.
OptionsCandDare the most effective solutions to manage the model's shorter context length without changing the model itself, by adjusting the input size both in terms of individual document size and total documents retrieved.

質問 # 46
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?

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

正解：B

解説：
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 whyBis 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.

質問 # 47
A Generative Al Engineer is using an LLM to classify species of edible mushrooms based on text descriptions of certain features. The model is returning accurate responses in testing and the Generative Al Engineer is confident they have the correct list of possible labels, but the output frequently contains additional reasoning in the answer when the Generative Al Engineer only wants to return the label with no additional text.
Which action should they take to elicit the desired behavior from this LLM?

A. Use zero shot chain-of-thought prompting to prevent a verbose output format
B. Use few snot prompting to instruct the model on expected output format
C. Use a system prompt to instruct the model to be succinct in its answer
D. Use zero shot prompting to instruct the model on expected output format

正解：C

解説：
The LLM classifies mushroom species accurately but includes unwanted reasoning text, and the engineer wants only the label. Let's assess how to control output format effectively.
Option A: Use few shot prompting to instruct the model on expected output format Few-shot prompting provides examples (e.g., input: description, output: label). It can work but requires crafting multiple examples, which is effort-intensive and less direct than a clear instruction.
Databricks Reference: "Few-shot prompting guides LLMs via examples, effective for format control but requires careful design" ("Generative AI Cookbook").
Option B: Use zero shot prompting to instruct the model on expected output format Zero-shot prompting relies on a single instruction (e.g., "Return only the label") without examples. It's simpler than few-shot but may not consistently enforce succinctness if the LLM's default behavior is verbose.
Databricks Reference: "Zero-shot prompting can specify output but may lack precision without examples" ("Building LLM Applications with Databricks").
Option C: Use zero shot chain-of-thought prompting to prevent a verbose output format Chain-of-Thought (CoT) encourages step-by-step reasoning, which increases verbosity-opposite to the desired outcome. This contradicts the goal of label-only output.
Databricks Reference: "CoT prompting enhances reasoning but often results in detailed responses" ("Databricks Generative AI Engineer Guide").
Option D: Use a system prompt to instruct the model to be succinct in its answer A system prompt (e.g., "Respond with only the species label, no additional text") sets a global instruction for the LLM's behavior. It's direct, reusable, and effective for controlling output style across queries.
Databricks Reference: "System prompts define LLM behavior consistently, ideal for enforcing concise outputs" ("Generative AI Cookbook," 2023).
Conclusion: Option D is the most effective and straightforward action, using a system prompt to enforce succinct, label-only responses, aligning with Databricks' best practices for output control.

質問 # 48
......

Databricks-Generative-AI-Engineer-Associate前提条件: https://www.pass4test.jp/Databricks-Generative-AI-Engineer-Associate.html

P.S. Pass4TestがGoogle Driveで共有している無料かつ新しいDatabricks-Generative-AI-Engineer-Associateダンプ：https://drive.google.com/open?id=14lTwW2m9YC1ojksvHa6yoU0sASD5_sYj