Pass Guaranteed Quiz 2025 Databricks-Generative-AI-Engineer-Associate - Databricks Certified Generative AI Engineer Associate Test Sample Questions

Blog Article

Tags: Databricks-Generative-AI-Engineer-Associate Test Sample Questions, Databricks-Generative-AI-Engineer-Associate Reliable Test Testking, Updated Databricks-Generative-AI-Engineer-Associate CBT, Detail Databricks-Generative-AI-Engineer-Associate Explanation, Latest Databricks-Generative-AI-Engineer-Associate Exam Topics

BONUS!!! Download part of 2Pass4sure Databricks-Generative-AI-Engineer-Associate dumps for free: https://drive.google.com/open?id=14CyVZdHBTjtdsCKIwzFEtS5H14Rxg6lc

Our Databricks Databricks-Generative-AI-Engineer-Associate Practice Exam software is compatible with Windows computers. If you run into any issues while using our Databricks Certified Generative AI Engineer Associate (Databricks-Generative-AI-Engineer-Associate) exam simulation software, our 24/7 product support team is here to help you. One of our Databricks-Generative-AI-Engineer-Associate desktop practice exam software's other feature is that it can be used even without an active internet connection. The Internet is only required for product license validation. This feature allows users to practice without an active internet connection.

Databricks Databricks-Generative-AI-Engineer-Associate Exam Syllabus Topics:

Topic	Details
Topic 1	Assembling and Deploying Applications: In this topic, Generative AI Engineers get knowledge about coding a chain using a pyfunc mode, coding a simple chain using langchain, and coding a simple chain according to requirements. Additionally, the topic focuses on basic elements needed to create a RAG application. Lastly, the topic addresses sub-topics about registering the model to Unity Catalog using MLflow.
Topic 2	Governance: Generative AI Engineers who take the exam get knowledge about masking techniques, guardrail techniques, and legal licensing requirements in this topic.
Topic 3	Application Development: In this topic, Generative AI Engineers learn about tools needed to extract data, Langchain similar tools, and assessing responses to identify common issues. Moreover, the topic includes questions about adjusting an LLM's response, LLM guardrails, and the best LLM based on the attributes of the application.
Topic 4	Evaluation and Monitoring: This topic is all about selecting an LLM choice and key metrics. Moreover, Generative AI Engineers learn about evaluating model performance. Lastly, the topic includes sub-topics about inference logging and usage of Databricks features.

>> Databricks-Generative-AI-Engineer-Associate Test Sample Questions <<

Databricks-Generative-AI-Engineer-Associate Reliable Test Testking, Updated Databricks-Generative-AI-Engineer-Associate CBT

It is common in modern society that many people who are more knowledgeable and capable than others finally lost some good opportunities for development because they didn’t obtain the Databricks-Generative-AI-Engineer-Associate Certification. The prerequisite for obtaining the Databricks-Generative-AI-Engineer-Associate certification is to pass the exam, but not everyone has the ability to pass it at one time. Because of not having appropriate review methods and review materials, or not grasping the rule of the questions, so many candidates eventually failed to pass even if they have devoted much effort.

Databricks Certified Generative AI Engineer Associate Sample Questions (Q54-Q59):

NEW QUESTION # 54
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?

A. Pick an embedding model trained on related domain knowledge
B. pick the embedding model ranked highest on the Massive Text Embedding Benchmark (MTEB) leaderboard hosted by HuggingFace
C. Pick an embedding model with multilingual support to support potential multilingual user questions
D. Pick the most recent and most performant open LLM released at the time

Answer: A

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 # 55
A Generative AI Engineer has been asked to design an LLM-based application that accomplishes the following business objective: answer employee HR questions using HR PDF documentation.
Which set of high level tasks should the Generative AI Engineer's system perform?

A. Use an LLM to summarize HR documentation. Provide summaries of documentation and user query into an LLM with a large context window to generate a response to the user.
B. Create an interaction matrix of historical employee questions and HR documentation. Use ALS to factorize the matrix and create embeddings. Calculate the embeddings of new queries and use them to find the best HR documentation. Use an LLM to generate a response to the employee question based upon the documentation retrieved.
C. Calculate averaged embeddings for each HR document, compare embeddings to user query to find the best document. Pass the best document with the user query into an LLM with a large context window to generate a response to the employee.
D. Split HR documentation into chunks and embed into a vector store. Use the employee question to retrieve best matched chunks of documentation, and use the LLM to generate a response to the employee based upon the documentation retrieved.

Answer: D

Explanation:
To design an LLM-based application that can answer employee HR questions using HR PDF documentation, the most effective approach is option D. Here's why:
* Chunking and Vector Store Embedding:HR documentation tends to be lengthy, so splitting it into smaller, manageable chunks helps optimize retrieval. These chunks are then embedded into avector store(a database that stores vector representations of text). Each chunk of text is transformed into an embeddingusing a transformer-based model, which allows for efficient similarity-based retrieval.
* Using Vector Search for Retrieval:When an employee asks a question, the system converts their query into an embedding as well. This embedding is then compared with the embeddings of the document chunks in the vector store. The most semantically similar chunks are retrieved, which ensures that the answer is based on the most relevant parts of the documentation.
* LLM to Generate a Response:Once the relevant chunks are retrieved, these chunks are passed into the LLM, which uses them as context to generate a coherent and accurate response to the employee's question.
* Why Other Options Are Less Suitable:
* A (Calculate Averaged Embeddings): Averaging embeddings might dilute important information. It doesn't provide enough granularity to focus on specific sections of documents.
* B (Summarize HR Documentation): Summarization loses the detail necessary for HR-related queries, which are often specific. It would likely miss the mark for more detailed inquiries.
* C (Interaction Matrix and ALS): This approach is better suited for recommendation systems and not for HR queries, as it's focused on collaborative filtering rather than text-based retrieval.
Thus, option D is the most effective solution for providing precise and contextual answers based on HR documentation.

NEW QUESTION # 56
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?

A. RECALL metric
B. ROUGE metric
C. NDCG metric
D. BLEU metric

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 # 57
A Generative Al Engineer is using an LLM to classify species of edible mushrooms based on text descriptions of certain features. The model is returning accurate responses in testing and the Generative Al Engineer is confident they have the correct list of possible labels, but the output frequently contains additional reasoning in the answer when the Generative Al Engineer only wants to return the label with no additional text.
Which action should they take to elicit the desired behavior from this LLM?

A. Use zero shot prompting to instruct the model on expected output format
B. Use few snot prompting to instruct the model on expected output format
C. Use zero shot chain-of-thought prompting to prevent a verbose output format
D. Use a system prompt to instruct the model to be succinct in its answer

Answer: D

Explanation:
The LLM classifies mushroom species accurately but includes unwanted reasoning text, and the engineer wants only the label. Let's assess how to control output format effectively.
* Option A: Use few shot prompting to instruct the model on expected output format
* Few-shot prompting provides examples (e.g., input: description, output: label). It can work but requires crafting multiple examples, which is effort-intensive and less direct than a clear instruction.
* Databricks Reference:"Few-shot prompting guides LLMs via examples, effective for format control but requires careful design"("Generative AI Cookbook").
* Option B: Use zero shot prompting to instruct the model on expected output format
* Zero-shot prompting relies on a single instruction (e.g., "Return only the label") without examples. It's simpler than few-shot but may not consistently enforce succinctness if the LLM's default behavior is verbose.
* Databricks Reference:"Zero-shot prompting can specify output but may lack precision without examples"("Building LLM Applications with Databricks").
* Option C: Use zero shot chain-of-thought prompting to prevent a verbose output format
* Chain-of-Thought (CoT) encourages step-by-step reasoning, which increases verbosity-opposite to the desired outcome. This contradicts the goal of label-only output.
* Databricks Reference:"CoT prompting enhances reasoning but often results in detailed responses"("Databricks Generative AI Engineer Guide").
* Option D: Use a system prompt to instruct the model to be succinct in its answer
* A system prompt (e.g., "Respond with only the species label, no additional text") sets a global instruction for the LLM's behavior. It's direct, reusable, and effective for controlling output style across queries.
* Databricks Reference:"System prompts define LLM behavior consistently, ideal for enforcing concise outputs"("Generative AI Cookbook," 2023).
Conclusion: Option D is the most effective and straightforward action, using a system prompt to enforce succinct, label-only responses, aligning with Databricks' best practices for output control.

NEW QUESTION # 58
A Generative AI Engineer is developing a chatbot designed to assist users with insurance-related queries. The chatbot is built on a large language model (LLM) and is conversational. However, to maintain the chatbot's focus and to comply with company policy, it must not provide responses to questions about politics. Instead, when presented with political inquiries, the chatbot should respond with a standard message:
"Sorry, I cannot answer that. I am a chatbot that can only answer questions around insurance." Which framework type should be implemented to solve this?

A. Compliance Guardrail
B. Safety Guardrail
C. Contextual Guardrail
D. Security Guardrail

Answer: B

Explanation:
In this scenario, the chatbot must avoid answering political questions and instead provide a standard message for such inquiries. Implementing aSafety Guardrailis the appropriate solution for this:
* What is a Safety Guardrail?Safety guardrails are mechanisms implemented in Generative AI systems to ensure the model behaves within specific bounds. In this case, it ensures the chatbot does not answer politically sensitive or irrelevant questions, which aligns with the business rules.
* Preventing Responses to Political Questions:The Safety Guardrail is programmed to detect specific types of inquiries (like political questions) and prevent the model from generating responses outside its intended domain. When such queries are detected, the guardrail intervenes and provides a pre-defined response: "Sorry, I cannot answer that. I am a chatbot that can only answer questions around insurance."
* How It Works in Practice:The LLM system can include aclassification layeror trigger rules based on specific keywords related to politics. When such terms are detected, the Safety Guardrail blocks the normal generation flow and responds with the fixed message.
* Why Other Options Are Less Suitable:
* B (Security Guardrail): This is more focused on protecting the system from security vulnerabilities or data breaches, not controlling the conversational focus.
* C (Contextual Guardrail): While context guardrails can limit responses based on context, safety guardrails are specifically about ensuring the chatbot stays within a safe conversational scope.
* D (Compliance Guardrail): Compliance guardrails are often related to legal and regulatory adherence, which is not directly relevant here.
Therefore, aSafety Guardrailis the right framework to ensure the chatbot only answers insurance-related queries and avoids political discussions.

NEW QUESTION # 59
......

It is worth mentioning that, the simulation test is available in our software version. With the simulation test, all of our customers will get accustomed to the Databricks-Generative-AI-Engineer-Associate exam easily, and get rid of bad habits, which may influence your performance in the real Databricks-Generative-AI-Engineer-Associate exam. In addition, the mode of Databricks-Generative-AI-Engineer-Associate learning guide questions and answers is the most effective for you to remember the key points. During your practice process, the Databricks-Generative-AI-Engineer-Associate Test Questions would be absorbed, which is time-saving and high-efficient. Considerate 24/7 service shows our attitudes, we always consider our candidates’ benefits and we guarantee that our Databricks-Generative-AI-Engineer-Associate test questions are the most excellent path for you to pass the exam.

Databricks-Generative-AI-Engineer-Associate Reliable Test Testking: https://www.2pass4sure.com/Generative-AI-Engineer/Databricks-Generative-AI-Engineer-Associate-actual-exam-braindumps.html

2025 Latest 2Pass4sure Databricks-Generative-AI-Engineer-Associate PDF Dumps and Databricks-Generative-AI-Engineer-Associate Exam Engine Free Share: https://drive.google.com/open?id=14CyVZdHBTjtdsCKIwzFEtS5H14Rxg6lc

Report this page

PASS GUARANTEED QUIZ 2025 DATABRICKS-GENERATIVE-AI-ENGINEER-ASSOCIATE - DATABRICKS CERTIFIED GENERATIVE AI ENGINEER ASSOCIATE TEST SAMPLE QUESTIONS

Pass Guaranteed Quiz 2025 Databricks-Generative-AI-Engineer-Associate - Databricks Certified Generative AI Engineer Associate Test Sample Questions