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| Certification Vendor: | Databricks |
|---|---|
| Exam Name: | Databricks Certified Generative AI Engineer Associate Exam |
| Exam Number: | Databricks-Generative-AI-Engineer-Associate |
| Exam Price: | USD 200 |
| Exam Format: | Multiple choice |
| Exam Duration: | 90 minutes |
| Real Exam Qty: | 45 |
| Available Languages: | Japanese, English, Korean, Brazilian Portuguese |
| Certificate Validity Period: | 2 years |
| Passing Score: | 700/1000 or 70% |
| 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 |
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NEW QUESTION # 92
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 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.
NEW QUESTION # 93
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: B
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 # 94
A Generative AI Engineer is tasked with deploying an application that takes advantage of a custom MLflow Pyfunc model to return some interim results.
How should they configure the endpoint to pass the secrets and credentials?
Answer: A
Explanation:
Context: Deploying an application that uses an MLflow Pyfunc model involves managing sensitive information such as secrets and credentials securely.
Explanation of Options:
* Option A: Use spark.conf.set(): While this method can pass configurations within Spark jobs, using it for secrets is not recommended because it may expose them in logs or Spark UI.
* Option B: Pass variables using the Databricks Feature Store API: The Feature Store API is designed for managing features for machine learning, not for handling secrets or credentials.
* Option C: Add credentials using environment variables: This is a common practice for managing credentials in a secure manner, as environment variables can be accessed securely by applications without exposing them in the codebase.
* Option D: Pass the secrets in plain text: This is highly insecure and not recommended, as it exposes sensitive information directly in the code.
Therefore,Option Cis the best method for securely passing secrets and credentials to an application, protecting them from exposure.
NEW QUESTION # 95
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?
Answer: A
Explanation:
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
NEW QUESTION # 96
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?
Answer: A
Explanation:
* Problem Context: The goal is to monitor the serving endpoint for incoming requests and outgoing responses in a provisioned throughput model serving endpoint within a Retrieval-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 Tables in Databricks are designed to store the results and metadata of inference runs. This allows the system to log incoming requests and outgoing responses directly 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 Tables are the optimal feature for monitoring request and response logs within the Databricks infrastructure for a model serving endpoint.
NEW QUESTION # 97
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