ちなみに、GoShiken Databricks-Generative-AI-Engineer-Associateの一部をクラウドストレージからダウンロードできます：https://drive.google.com/open?id=15Z5gskPsNHMXIHPoV8jGI453Ttvz1Qna
私たちDatabricksのDatabricks-Generative-AI-Engineer-Associate学習クイズは、仕上げの体系的な分析の分野での近年の試験状況のさまざまな専門家から作られ、学生の要求をできるだけ満たし、同時にチェックとレビューを行う専門スタッフがいますDatabricks-Generative-AI-Engineer-Associate実践教材、学生の学習に高品質の情報を楽しんでもらいました。 試験の多様性により、GoShikenのDatabricks-Generative-AI-Engineer-Associate学習教材もさまざまな種類の学習教材にまとめられているため、学生は必要なDatabricks-Generative-AI-Engineer-Associateガイド急流の情報をすばやく見つけることができます。
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>> Databricks-Generative-AI-Engineer-Associate認定資格試験 <<
効果的な勤勉さが結果に正比例することは誰もが知っているので、長年の勤勉な作業によって、私たちの専門家は頻繁にテストされた知識をあなたの参考のためにDatabricks Certified Generative AI Engineer Associate実践資料に集めました。 ですから、Databricks Certified Generative AI Engineer Associateトレーニング資料は彼らの努力の成果です。 Databricks Certified Generative AI Engineer Associateの実践教材に頼ることで、以前に想像していた以上の成果を絶対に得ることができます。 Databricks Certified Generative AI Engineer Associateの実際のDatabricks-Generative-AI-Engineer-Associateテストを選択した顧客から収集された明確なデータがあり、合格率は98〜100％です。 したがって、成功を収めるチャンスは、当社の資料によって大幅に向上します。
質問 # 73
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LLM-generated analyses for any users who would like to have live summaries, rather than reading a series of potentially outdated news articles.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?
正解：A
解説：
* 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.
質問 # 74
A Generative Al Engineer is ready to deploy an LLM application written using Foundation Model APIs. They want to follow security best practices for production scenarios Which authentication method should they choose?
正解：A
解説：
The task is to deploy an LLM application using Foundation Model APIs in a production environment while adhering to security best practices. Authentication is critical for securing access to Databricks resources, such as the Foundation Model API. Let's evaluate the options based on Databricks' security guidelines for production scenarios.
* Option A: Use an access token belonging to service principals
* Service principals are non-human identities designed for automated workflows and applications in Databricks. Using an access token tied to a service principal ensures that the authentication is scoped to the application, follows least-privilege principles (via role-based access control), and avoids reliance on individual user credentials. This is a security best practice for production deployments.
* Databricks Reference:"For production applications, use service principals with access tokens to authenticate securely, avoiding user-specific credentials"("Databricks Security Best Practices,"
2023). Additionally, the "Foundation Model API Documentation" states:"Service principal tokens are recommended for programmatic access to Foundation Model APIs."
* Option B: Use a frequently rotated access token belonging to either a workspace user or a service principal
* Frequent rotation enhances security by limiting token exposure, but tying the token to a workspace user introduces risks (e.g., user account changes, broader permissions). Including both user and service principal options dilutes the focus on application-specific security, making this less ideal than a service-principal-only approach. It also adds operational overhead without clear benefits over Option A.
* Databricks Reference:"While token rotation is a good practice, service principals are preferred over user accounts for application authentication"("Managing Tokens in Databricks," 2023).
* Option C: Use OAuth machine-to-machine authentication
* OAuth M2M (e.g., client credentials flow) is a secure method for application-to-service communication, often using service principals under the hood. However, Databricks' Foundation Model API primarily supports personal access tokens (PATs) or service principal tokens over full OAuth flows for simplicity in production setups. OAuth M2M adds complexity (e.g., managing refresh tokens) without a clear advantage in this context.
* Databricks Reference:"OAuth is supported in Databricks, but service principal tokens are simpler and sufficient for most API-based workloads"("Databricks Authentication Guide," 2023).
* Option D: Use an access token belonging to any workspace user
* Using a user's access token ties the application to an individual's identity, violating security best practices. It risks exposure if the user leaves, changes roles, or has overly broad permissions, and it's not scalable or auditable for production.
* Databricks Reference:"Avoid using personal user tokens for production applications due to security and governance concerns"("Databricks Security Best Practices," 2023).
Conclusion: Option A is the best choice, as it uses a service principal's access token, aligning with Databricks' security best practices for production LLM applications. It ensures secure, application-specific authentication with minimal complexity, as explicitly recommended for Foundation Model API deployments.
質問 # 75
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?
正解：B
解説：
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 whyDis 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.
質問 # 76
A Generative AI Engineer is building an LLM to generate article summaries in the form of a type of poem, such as a haiku, given the article content. However, the initial output from the LLM does not match the desired tone or style.
Which approach will NOT improve the LLM's response to achieve the desired response?
正解：B
解説：
The task at hand is to improve the LLM's ability to generate poem-like article summaries with the desired tone and style. Using a neutralizer to normalize the tone and style of the underlying documents (option B) will not help improve the LLM's ability to generate the desired poetic style. Here's why:
Neutralizing Underlying Documents:
A neutralizer aims to reduce or standardize the tone of input data. However, this contradicts the goal, which is to generate text with a specific tone and style (like haikus). Neutralizing the source documents will strip away the richness of the content, making it harder for the LLM to generate creative, stylistic outputs like poems.
Why Other Options Improve Results:
A (Explicit Instructions in the Prompt): Directly instructing the LLM to generate text in a specific tone and style helps align the output with the desired format (e.g., haikus). This is a common and effective technique in prompt engineering.
C (Few-shot Examples): Providing examples of the desired output format helps the LLM understand the expected tone and structure, making it easier to generate similar outputs.
D (Fine-tuning the LLM): Fine-tuning the model on a dataset that contains examples of the desired tone and style is a powerful way to improve the model's ability to generate outputs that match the target format.
Therefore, using a neutralizer (option B) is not an effective method for achieving the goal of generating stylized poetic summaries.
質問 # 77
A Generative Al Engineer is helping a cinema extend its website ' s chat bot to be able to respond to questions about specific showtimes for movies currently playing at their local theater. They already have the location of the user provided by location services to their agent, and a Delta table which is continually updated with the latest showtime information by location. They want to implement this new capability In their RAG application.
Which option will do this with the least effort and in the most performant way?
正解：B
解説：
The task is to extend a cinema chatbot to provide movie showtime information using a RAG application, leveraging user location and a continuously updated Delta table, with minimal effort and high performance.
Let's evaluate the options.
* Option A: Create a Feature Serving Endpoint from a FeatureSpec that references an online store synced from the Delta table. Query the Feature Serving Endpoint as part of the agent logic / tool implementation
* Databricks Feature Serving provides low-latency access to real-time data from Delta tables via an online store. Syncing the Delta table to a Feature Serving Endpoint allows the chatbot to query showtimes efficiently, integrating seamlessly into the RAG agent's tool logic. This leverages Databricks' native infrastructure, minimizing effort and ensuring performance.
* Databricks Reference : " Feature Serving Endpoints provide real-time access to Delta table data with low latency, ideal for production systems " ( " Databricks Feature Engineering Guide, "
2023).
* Option B: Query the Delta table directly via a SQL query constructed from the user ' s input using a text-to-SQL LLM in the agent logic / tool
* Using a text-to-SQL LLM to generate queries adds complexity (e.g., ensuring accurate SQL generation) and latency (LLM inference + SQL execution). While feasible, it's less performant and requires more effort than a pre-built serving solution.
* Databricks Reference : " Direct SQL queries are flexible but may introduce overhead in real- time applications " ( " Building LLM Applications with Databricks " ).
* Option C: Write the Delta table contents to a text column, then embed those texts using an embedding model and store these in the vector index. Look up the information based on the embedding as part of the agent logic / tool implementation
* Converting structured Delta table data (e.g., showtimes) into text, embedding it, and using vector search is inefficient for structured lookups. It's effort-intensive (preprocessing, embedding) and less precise than direct queries, undermining performance.
* Databricks Reference : " Vector search excels for unstructured data, not structured tabular lookups " ( " Databricks Vector Search Documentation " ).
* Option D: Set up a task in Databricks Workflows to write the information in the Delta table periodically to an external database such as MySQL and query the information from there as part of the agent logic / tool implementation
* Exporting to an external database (e.g., MySQL) adds setup effort (workflow, external DB management) and latency (periodic updates vs. real-time). It's less performant and more complex than using Databricks' native tools.
* Databricks Reference : " Avoid external systems when Delta tables provide real-time data natively " ( " Databricks Workflows Guide " ).
Conclusion : Option A minimizes effort by using Databricks Feature Serving for real-time, low-latency access to the Delta table, ensuring high performance in a production-ready RAG chatbot.
質問 # 78
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Databricks-Generative-AI-Engineer-Associate学習ガイドの高品質と高効率は、同じ業界の製品で際立っています。私たちの教材は常にユーザーのために考慮されています。 Databricks-Generative-AI-Engineer-Associate試験問題を選択すると、より良い自己になります。 Databricks-Generative-AI-Engineer-Associate実際の試験では、輝かしい未来に貢献したいと考えています。私たちの教材は常に改善されています。良いアイデアがあれば、私たちの教材は喜んで受け入れます。 Databricks-Generative-AI-Engineer-Associate試験資料は、このファミリーに参加するパートナーを増やすことを楽しみにしています。私たちは一緒に進歩し、より良くなります。
Databricks-Generative-AI-Engineer-Associate勉強方法: https://www.goshiken.com/Databricks/Databricks-Generative-AI-Engineer-Associate-mondaishu.html
P.S. GoShiken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=15Z5gskPsNHMXIHPoV8jGI453Ttvz1Qna