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| Section | Objectives |
|---|---|
| Topic 1: Retrieval-Augmented Generation (RAG) | - Vector databases and embeddings
|
| Topic 2: Responsible AI and Governance | - Model governance
|
| Topic 3: Prompt Engineering | - Prompt evaluation
|
| Topic 4: Foundations of Generative AI | - Large Language Models (LLMs)
|
| Topic 5: Databricks Mosaic AI Platform | - Model development and serving
|
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NEW QUESTION # 37
All of the following are Python APIs used to query Databricks foundation models. When running in an interactive notebook, which of the following libraries does not automatically use the current session credentials?
Answer: A
Explanation:
When working within a Databricks notebook, several high-level SDKs are "Databricks-aware." The MLflow Deployments SDK (C) and the Databricks Python SDK (D) are designed to automatically look for the DATABRICKS_HOST and DATABRICKS_TOKEN environment variables provided by the notebook context. The OpenAI client (A), when configured for Databricks via Mosaic AI Gateway, also typically handles authentication via workspace integration in recent versions. However, the REST API via the requests library (B) is a generic Python HTTP client. It has no intrinsic knowledge of the Databricks environment. To use it, an engineer must manually extract the token (e.g., via dbutils.notebook.entry_point...) and explicitly pass it in the Authorization: Bearer <token> header of the request. Without this manual step, the requests library will fail with a 401 Unauthorized error.
NEW QUESTION # 38
A Generative AI Engineer developed an LLM application using the provisioned throughput Foundation Model API. Now that the application is ready to be deployed, they realize their volume of requests are not sufficiently high enough to create their own provisioned throughput endpoint. They want to choose a strategy that ensures the best cost-effectiveness for their application.
What strategy should the Generative AI Engineer use?
Answer: C
Explanation:
* Problem Context: The engineer needs a cost-effective deployment strategy for an LLM application with relatively low request volume.
* Explanation of Options:
* Option A: Switching to external models may not provide the required control or integration necessary for specific application needs.
* Option B: Using a pay-per-token model is cost-effective, especially for applications with variable or low request volumes, as it aligns costs directly with usage.
* Option C: Changing to a model with fewer parameters could reduce costs, but might also impact the performance and capabilities of the application.
* Option D: Manually throttling requests is a less efficient and potentially error-prone strategy for managing costs.
OptionBis ideal, offering flexibility and cost control, aligning expenses directly with the application's usage patterns.
NEW QUESTION # 39
A team uses Mosaic AI Vector Search to retrieve documents for their Retrieval-Augmented Generation (RAG) pipeline. The search query returns five relevant documents, and the first three are added to the prompt as context. Performance evaluation with Agent Evaluation shows that some lower-ranked retrieved documents have higher context relevancy scores than higher-ranked documents. Which option should the team consider to optimize this workflow?
Answer: C
Explanation:
The scenario describes a common "retrieval gap" where the initial bi-encoder (embedding model) used for vector search identifies relevant documents but does not rank them perfectly. This happens because embedding models represent entire documents as a single vector, which can lose nuance. The standard engineering solution is to implement a Reranker (Cross-Encoder). Unlike embedding models, a reranker processes the query and a candidate document simultaneously, allowing it to capture deep semantic interactions between the two. In a Mosaic AI workflow, after the vector search retrieves the top $k$ documents, the reranker evaluates those specific $k$ documents to produce a more accurate relevance score. This ensures that the most contextually relevant documents are placed at the top of the list (and thus the top of the LLM prompt), which is crucial because LLMs are sensitive to document order and often prioritize information found at the beginning of the context.
NEW QUESTION # 40
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?
Answer: C
Explanation:
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.
NEW QUESTION # 41
A Generative Al Engineer has built an LLM-based system that will automatically translate user text between two languages. They now want to benchmark multiple LLM's on this task and pick the best one. They have an evaluation set with known high quality translation examples. They want to evaluate each LLM using the evaluation set with a performant metric.
Which metric should they choose for this evaluation?
Answer: D
Explanation:
The task is to benchmark LLMs for text translation using an evaluation set with known high-quality examples, requiring a performant metric. Let's evaluate the options.
Option A: ROUGE metric
ROUGE (Recall-Oriented Understudy for Gisting Evaluation) measures overlap between generated and reference texts, primarily for summarization. It's less suited for translation, where precision and word order matter more.
Databricks Reference: "ROUGE is commonly used for summarization, not translation evaluation" ("Generative AI Cookbook," 2023).
Option B: BLEU metric
BLEU (Bilingual Evaluation Understudy) evaluates translation quality by comparing n-gram overlap with reference translations, accounting for precision and brevity. It's widely used, performant, and appropriate for this task.
Databricks Reference: "BLEU is a standard metric for evaluating machine translation, balancing accuracy and efficiency" ("Building LLM Applications with Databricks").
Option C: NDCG metric
NDCG (Normalized Discounted Cumulative Gain) assesses ranking quality, not text generation. It's irrelevant for translation evaluation.
Databricks Reference: "NDCG is suited for ranking tasks, not generative output scoring" ("Databricks Generative AI Engineer Guide").
Option D: RECALL metric
Recall measures retrieved relevant items but doesn't evaluate translation quality (e.g., fluency, correctness). It's incomplete for this use case.
Databricks Reference: No specific extract, but recall alone lacks the granularity of BLEU for text generation tasks.
Conclusion: Option B (BLEU) is the best metric for translation evaluation, offering a performant and standard approach, as endorsed by Databricks' guidance on generative tasks.
NEW QUESTION # 42
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