Databricks Databricks-Generative-AI-Engineer-Associate受験練習参考書、Databricks-Generative-AI-Engineer-Associate試験情報

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

Certification Vendor:	Databricks
Exam Name:	Databricks Certified Generative AI Engineer Associate Exam
Exam Number:	Databricks-Generative-AI-Engineer-Associate
Passing Score:	700/1000 or 70%
Exam Duration:	90 minutes
Available Languages:	Korean, English, Japanese, Brazilian Portuguese
Exam Format:	Multiple choice
Real Exam Qty:	45
Exam Price:	USD 200
Certificate Validity Period:	2 years
Recommended Training:	Generative AI Engineering with Databricks
Exam Registration:	Databricks Certification Registration
Sample Questions:	Databricks Databricks-Generative-AI-Engineer-Associate Sample Questions
Exam Way:	Online proctored or in-person test center
Pre Condition:	No formal prerequisites; recommended 6+ months hands-on experience building generative AI solutions
Official Syllabus URL:	https://www.databricks.com/learn/certification/genai-engineer-associate

>> Databricks Databricks-Generative-AI-Engineer-Associate受験練習参考書 <<

Databricks-Generative-AI-Engineer-Associate試験情報、Databricks-Generative-AI-Engineer-Associate日本語復習赤本

Databricks-Generative-AI-Engineer-AssociateトレーニングガイドDatabricksでは、PDFバージョン、PCバージョン、APPオンラインバージョンを含む3つのバージョンを強化しています。 Databricks-Generative-AI-Engineer-Associateテストガイドは非常に効率的で、回答と質問の形式は同じです。バージョンが異なると、独自の機能と使用方法が強化され、クライアントは最も便利な方法を選択できます。たとえば、Databricks-Generative-AI-Engineer-AssociateガイドトレントのPDF形式は印刷可能で、ダウンロードへの即時アクセスを促進します。いつでも学習でき、1年の任意の日にDatabricks-Generative-AI-Engineer-Associate試験問題を自由に更新できます。

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	アプリケーションの組み立てとデプロイ: このトピックでは、Generative AI エンジニアは、pyfunc モードを使用してチェーンをコーディングする方法、langchain を使用してシンプルなチェーンをコーディングする方法、要件に従ってシンプルなチェーンをコーディングする方法を学びます。さらに、このトピックでは、RAG アプリケーションを作成するために必要な基本要素に焦点を当てています。最後に、このトピックでは、MLflow を使用してモデルを Unity Catalog に登録する方法に関するサブトピックを取り上げます。
トピック 2	アプリケーション開発: このトピックでは、Generative AI エンジニアは、データの抽出に必要なツール、Langchain 類似ツール、一般的な問題を特定するための応答の評価について学習します。さらに、このトピックには、LLM の応答の調整、LLM ガードレール、およびアプリケーションの属性に基づいた最適な LLM に関する質問が含まれています。
トピック 3	データ準備: Generative AI エンジニアは、特定のドキュメント構造とモデル制約のチャンキング戦略について説明します。このトピックでは、ソースドキュメント内の不要なコンテンツのフィルター処理にも重点を置いています。最後に、Generative AI エンジニアは、提供されたソースデータと形式からドキュメントコンテンツを抽出する方法についても学習します。
トピック 4	評価と監視: このトピックでは、LLM の選択と主要なメトリックについて説明します。さらに、Generative AI エンジニアはモデルのパフォーマンスの評価について学習します。最後に、このトピックには推論ログと Databricks 機能の使用に関するサブトピックが含まれています。

Databricks Certified Generative AI Engineer Associate 認定 Databricks-Generative-AI-Engineer-Associate 試験問題 (Q14-Q19):

質問 # 14
What is an effective method to preprocess prompts using custom code before sending them to an LLM?

A. Write a MLflow PyFunc model that has a separate function to process the prompts
B. Directly modify the LLM's internal architecture to include preprocessing steps
C. Rather than preprocessing prompts, it's more effective to postprocess the LLM outputs to align the outputs to desired outcomes
D. It is better not to introduce custom code to preprocess prompts as the LLM has not been trained with examples of the preprocessed prompts

正解：A

解説：
The most effective way to preprocess prompts using custom code is to write a custom model, such as an MLflow PyFunc model. Here's a breakdown of why this is the correct approach:
* MLflow PyFunc Models:MLflow is a widely used platform for managing the machine learning lifecycle, including experimentation, reproducibility, and deployment. APyFuncmodel is a generic Python function model that can implement custom logic, which includes preprocessing prompts.
* Preprocessing Prompts:Preprocessing could include various tasks like cleaning up the user input, formatting it according to specific rules, or augmenting it with additional context before passing it to the LLM. Writing this preprocessing as part of a PyFunc model allows the custom code to be managed, tested, and deployed easily.
* Modular and Reusable:By separating the preprocessing logic into a PyFunc model, the system becomes modular, making it easier to maintain and update without needing to modify the core LLM or retrain it.
* Why Other Options Are Less Suitable:
* A (Modify LLM's Internal Architecture): Directly modifying the LLM's architecture is highly impractical and can disrupt the model's performance. LLMs are typically treated as black-box models for tasks like prompt processing.
* B (Avoid Custom Code): While it's true that LLMs haven't been explicitly trained with preprocessed prompts, preprocessing can still improve clarity and alignment with desired input formats without confusing the model.
* C (Postprocessing Outputs): While postprocessing the output can be useful, it doesn't address the need for clean and well-formatted inputs, which directly affect the quality of the model's responses.
Thus, using an MLflow PyFunc model allows for flexible and controlled preprocessing of prompts in a scalable way, making it the most effective method.

質問 # 15
A Generative AI Engineer is building a Generative AI system that suggests the best matched employee team member to newly scoped projects. The team member is selected from a very large team. Thematch should be based upon project date availability and how well their employee profile matches the project scope. Both the employee profile and project scope are unstructured text.
How should the Generative Al Engineer architect their system?

A. Create a tool for finding available team members given project dates. Embed all project scopes into a vector store, perform a retrieval using team member profiles to find the best team member.
B. Create a tool for finding team member availability given project dates, and another tool that uses an LLM to extract keywords from project scopes. Iterate through available team members' profiles and perform keyword matching to find the best available team member.
C. Create a tool to find available team members given project dates. Create a second tool that can calculate a similarity score for a combination of team member profile and the project scope. Iterate through the team members and rank by best score to select a team member.
D. Create a tool for finding available team members given project dates. Embed team profiles into a vector store and use the project scope and filtering to perform retrieval to find the available best matched team members.

正解：D

質問 # 16
Generative AI Engineer at an electronics company just deployed a RAG application for customers to ask questions about products that the company carries. However, they received feedback that the RAG response often returns information about an irrelevant product.
What can the engineer do to improve the relevance of the RAG's response?

A. Use a different semantic similarity search algorithm
B. Implement caching for frequently asked questions
C. Assess the quality of the retrieved context
D. Use a different LLM to improve the generated response

正解：C

解説：
In a Retrieval-Augmented Generation (RAG) system, the key to providing relevant responses lies in the quality of the retrieved context. Here's why option A is the most appropriate solution:
Context Relevance:
The RAG model generates answers based on retrieved documents or context. If the retrieved information is about an irrelevant product, it suggests that the retrieval step is failing to select the right context. The Generative AI Engineer must first assess the quality of what is being retrieved and ensure it is pertinent to the query.
Vector Search and Embedding Similarity:
RAG typically uses vector search for retrieval, where embeddings of the query are matched against embeddings of product descriptions. Assessing the semantic similarity search process ensures that the closest matches are actually relevant to the query.
Fine-tuning the Retrieval Process:
By improving the retrieval quality, such as tuning the embeddings or adjusting the retrieval strategy, the system can return more accurate and relevant product information.
Why Other Options Are Less Suitable:
B (Caching FAQs): Caching can speed up responses for frequently asked questions but won't improve the relevance of the retrieved content for less frequent or new queries.
C (Use a Different LLM): Changing the LLM only affects the generation step, not the retrieval process, which is the core issue here.
D (Different Semantic Search Algorithm): This could help, but the first step is to evaluate the current retrieval context before replacing the search algorithm.
Therefore, improving and assessing the quality of the retrieved context (option A) is the first step to fixing the issue of irrelevant product information.

質問 # 17
A Generative AI Engineer has deployed a customer-support agent in production that retrieves product documentation and generates responses. SMEs have been reviewing agent responses and providing feedback through a web interface that captures ratings of 1-5 stars and written comments. The engineer needs to systematically collect this feedback and use it to create an evaluation dataset that can be used to compare future agent versions against the current baseline performance.
Which approach should the engineer use to accomplish this task?

A. Export only the written SME comments to a text file and manually score them using a custom script, then use the script's output as the evaluation dataset for future agent comparisons.
B. Log the SME ratings and comments directly to a Delta table with the corresponding user queries and agent responses, then use MLflow to create an evaluation dataset from this table and register it for future agent evaluations.
C. Use Unity Catalog to create a view that filters only 5-star-rated interactions, then register this view as the evaluation dataset to benchmark all future agent versions.
D. Use the customer review app to collect SME feedback, then directly deploy the highest-rated responses as the new agent baseline without storing them as a formal evaluation dataset.

正解：B

解説：
Option B preserves the relationships between each user query, generated response, and expert assessment.
That context is necessary to turn production feedback into reusable evaluation cases. The engineer can curate representative examples from the Delta table, map them into MLflow's evaluation schema, and retain the dataset for consistent comparisons between agent versions. Where correctness scoring is required, experts should provide corrected answers or expected facts; a star rating alone is not a ground-truth answer. Keeping only written comments loses essential request context. Selecting only five-star interactions biases the dataset and excludes failure cases that future versions should improve. Deploying highly rated responses is not a substitute for evaluating an updated agent against a stable, representative benchmark. Databricks documentation

質問 # 18
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?

A. Lakeview
B. Inference Tables
C. Vector Search
D. DBSQL

正解：B

解説：
Problem Context: The goal is to monitor theserving endpointfor incoming requests and outgoing responses in aprovisioned throughput model serving endpointwithin aRetrieval-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 Tablesin Databricks are designed to store the results and metadata of inference runs. This allows the system to logincoming requests and outgoing responsesdirectly 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 Tablesare the optimal feature for monitoring request and response logs within the Databricks infrastructure for a model serving endpoint.

質問 # 19
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Databricks-Generative-AI-Engineer-Associate試験情報: https://www.goshiken.com/Databricks/Databricks-Generative-AI-Engineer-Associate-mondaishu.html