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| Certification Vendor: | Databricks |
|---|---|
| Exam Name: | Databricks Certified Generative AI Engineer Associate |
| Exam Number: | Generative AI Engineer Associate |
| Related Certifications: | Databricks Certified Machine Learning Associate Databricks Certified Data Engineer Associate |
| Passing Score: | Scaled Score (approx. 70%) |
| Exam Format: | Multiple Select, Multiple Choice |
| Certificate Validity Period: | 2 years |
| Exam Price: | $200 USD |
| Exam Duration: | 90 minutes |
| Available Languages: | Korean, Japanese, Portuguese (Brazil), English |
| Real Exam Qty: | 45 |
| Sample Questions: | Databricks Databricks-Generative-AI-Engineer-Associate Sample Questions |
| Exam Way: | Online (Proctored) or Test Center |
| Pre Condition: | None (Recommended: 6+ months hands-on experience) |
| Official Syllabus URL: | https://www.databricks.com/learn/certification/genai-engineer-associate |
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Achieving the Databricks Certified Generative AI Engineer Associate (Databricks-Generative-AI-Engineer-Associate) certification can significantly impact your career progression and earning potential. This certification showcases your expertise and knowledge to employers, making you a valuable asset in the Databricks Databricks-Generative-AI-Engineer-Associate industry. With the rapidly evolving nature of the Databricks world, staying up-to-date with the latest technologies and trends is crucial. The Databricks-Generative-AI-Engineer-Associate Certification Exam enables you to learn these changes and ensures you remain current in your field.
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NEW QUESTION # 78
A Generative Al Engineer is tasked with developing a RAG application that will help a small internal group of experts at their company answer specific questions, augmented by an internal knowledge base. They want the best possible quality in the answers, and neither latency nor throughput is a huge concern given that the user group is small and they're willing to wait for the best answer. The topics are sensitive in nature and the data is highly confidential and so, due to regulatory requirements, none of the information is allowed to be transmitted to third parties.
Which model meets all the Generative Al Engineer's needs in this situation?
Answer: D
Explanation:
* Problem Context: The Generative AI Engineer needs a model for a Retrieval-Augmented Generation (RAG) application that provides high-quality answers, where latency and throughput are not major concerns. The key factors are confidentiality and sensitivity of the data, as well as the requirement for all processing to be confined to internal resources without external data transmission.
* Explanation of Options:
Option A: Dolly 1.5B: This model does not typically support RAG applications as it's more focused on image generation tasks.
Option B: OpenAI GPT-4: While GPT-4 is powerful for generating responses, its standard deployment involves cloud-based processing, which could violate the confidentiality requirements due to external data transmission.
Option C: BGE-large: The BGE (Big Green Engine) large model is a suitable choice if it is configured to operate on-premises or within a secure internal environment that meets regulatory requirements. Assuming this setup, BGE-large can provide high-quality answers while ensuring that data is not transmitted to third parties, thus aligning with the project's sensitivity and confidentiality needs.
Option D: Llama2-70B: Similar to GPT-4, unless specifically set up for on-premises use, it generally relies on cloud-based services, which might risk confidential data exposure.
Given the sensitivity and confidentiality concerns, BGE-large is assumed to be configurable for secure internal use, making it the optimal choice for this scenario.
NEW QUESTION # 79
A Generative Al Engineer has already trained an LLM on Databricks and it is now ready to be deployed.
Which of the following steps correctly outlines the easiest process for deploying a model on Databricks?
Answer: A
Explanation:
* Problem Context: The goal is to deploy a trained LLM on Databricks in the simplest and most integrated manner.
* Explanation of Options:
* Option A: This method involves unnecessary steps like logging the model as a pickle object, which is not the most efficient path in a Databricks environment.
* Option B: Logging the model with MLflow during training and then using MLflow's API to register and start serving the model is straightforward and leverages Databricks' built-in functionalities for seamless model deployment.
* Option C: Building and running a Docker container is a complex and less integrated approach within the Databricks ecosystem.
* Option D: Using Flask and Gunicorn is a more manual approach and less integrated compared to the native capabilities of Databricks and MLflow.
OptionBprovides the most straightforward and efficient process, utilizing Databricks' ecosystem to its full advantage for deploying models.
NEW QUESTION # 80
A Generative Al Engineer is deciding between using LSH (Locality Sensitive Hashing) and HNSW (Hierarchical Navigable Small World) for indexing their vector database Their top priority is semantic accuracy Which approach should the Generative Al Engineer use to evaluate these two techniques?
Answer: D
Explanation:
The task is to choose between LSH and HNSW for a vector database index, prioritizing semantic accuracy.
The evaluation must assess how well each method retrieves semantically relevant results. Let's evaluate the options.
* Option A: Compare the cosine similarities of the embeddings of returned results against those of a representative sample of test inputs
* Cosine similarity measures semantic closeness between vectors, directly assessing retrieval accuracy in a vector database. Comparing returned results' embeddings to test inputs' embeddings evaluates how well LSH or HNSW preserves semantic relationships, aligning with the priority.
* Databricks Reference:"Cosine similarity is a standard metric for evaluating vector search accuracy"("Databricks Vector Search Documentation," 2023).
* Option B: Compare the Bilingual Evaluation Understudy (BLEU) scores of returned results for a representative sample of test inputs
* BLEU evaluates text generation (e.g., translations), not vector retrieval accuracy. It's irrelevant for indexing performance.
* Databricks Reference:"BLEU applies to generative tasks, not retrieval"("Generative AI Cookbook").
* Option C: Compare the Recall-Oriented-Understudy for Gisting Evaluation (ROUGE) scores of returned results for a representative sample of test inputs
* ROUGE is for summarization evaluation, not vector search. It doesn't measure semantic accuracy in retrieval.
* Databricks Reference:"ROUGE is unsuited for vector database evaluation"("Building LLM Applications with Databricks").
* Option D: Compare the Levenshtein distances of returned results against a representative sample of test inputs
* Levenshtein distance measures string edit distance, not semantic similarity in embeddings. It's inappropriate for vector-based retrieval.
* Databricks Reference: No specific support for Levenshtein in vector search contexts.
Conclusion: Option A (cosine similarity) is the correct approach, directly evaluating semantic accuracy in vector retrieval, as recommended by Databricks for Vector Search assessments.
NEW QUESTION # 81
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: D
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 # 82
When developing an LLM application, it's crucial to ensure that the data used for training the model complies with licensing requirements to avoid legal risks.
Which action is NOT appropriate to avoid legal risks?
Answer: A
Explanation:
* Problem Context : When using data to train a model, it's essential to ensure compliance with licensing to avoid legal risks. Legal issues can arise from using data without permission, especially when it comes from third-party sources.
* Explanation of Options :
* Option A : Reaching out to data curators before using the data is an appropriate action. This allows you to ensure you have permission or understand the licensing terms before starting to use the data in your model.
* Option B : Using original data that you personally created is always a safe option. Since you have full ownership over the data, there are no legal risks, as you control the licensing.
* Option C : Using data that is explicitly labeled with an open license and adhering to the license terms is a correct and recommended approach. This ensures compliance with legal requirements.
* Option D : Reaching out to the data curators after you have already started using the trained model is not appropriate . If you've already used the data without understanding its licensing terms, you may have already violated the terms of use, which could lead to legal complications.
It's essential to clarify the licensing terms before using the data, not after.
Thus, Option D is not appropriate because it could expose you to legal risks by using the data without first obtaining the proper licensing permissions.
NEW QUESTION # 83
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