Databricks-Generative-AI-Engineer-Associate시험패스가능한공부, Databricks-Generative-AI-Engineer-Associate퍼펙트덤프데모다운로드

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참고: Fast2test에서 Google Drive로 공유하는 무료, 최신 Databricks-Generative-AI-Engineer-Associate 시험 문제집이 있습니다: https://drive.google.com/open?id=1r0aR9-WhxEyZ8EH2JZhMS2r-GuuHbHLO

우리Fast2test 에서는 여러분들한테 아주 편리하고 시간 절약함과 바꿀 수 있는 좋은 대책을 마련하였습니다. Fast2test에서는Databricks Databricks-Generative-AI-Engineer-Associate인증시험관련가이드로 효과적으로Databricks Databricks-Generative-AI-Engineer-Associate시험을 패스하도록 도와드리겠습니다.만약 여러분이 다른 사이트에서도 관련덤프자료를 보셨을 경우 페이지 아래를 보시면 자료출처는 당연히 Fast2test 일 것입니다. Fast2test의 자료만의 제일 전면적이고 또 최신 업데이트일것입니다.

관심있는 인증시험과목Databricks Databricks-Generative-AI-Engineer-Associate덤프의 무료샘플을 원하신다면 덤프구매사이트의 PDF Version Demo 버튼을 클릭하고 메일주소를 입력하시면 바로 다운받아Databricks Databricks-Generative-AI-Engineer-Associate덤프의 일부분 문제를 체험해 보실수 있습니다. PDF버전외에 온라인버전과 테스트엔버전 Demo도 다운받아 보실수 있습니다.

>> Databricks-Generative-AI-Engineer-Associate시험패스 가능한 공부 <<

Databricks-Generative-AI-Engineer-Associate:Databricks Certified Generative AI Engineer Associate 시험덤프 Databricks-Generative-AI-Engineer-Associate응시자료

Fast2test의Databricks Databricks-Generative-AI-Engineer-Associate덤프로Databricks Databricks-Generative-AI-Engineer-Associate시험공부를 하여 시험에서 떨어지는 경우 덤프비용전액을 환불해드릴만큼 저희 덤프는 높은 적중율을 자랑하고 있습니다. 주문번호와 불합격성적표를 메일로 보내오시면 바로 환불가능합니다. 환불해드린후에는 무료업데이트 서비스가 종료됩니다. Databricks Databricks-Generative-AI-Engineer-Associate 시험을 우려없이 패스하고 싶은 분은 저희 사이트를 찾아주세요.

최신 Generative AI Engineer Databricks-Generative-AI-Engineer-Associate 무료샘플문제 (Q56-Q61):

질문 # 56
A Generative Al Engineer is building an LLM-based application that has an important transcription (speech-to-text) task. Speed is essential for the success of the application Which open Generative Al models should be used?

정답：B

설명：
The task requires an open generative AI model for a transcription (speech-to-text) task where speed is essential. Let's assess the options based on their suitability for transcription and performance characteristics, referencing Databricks' approach to model selection.
Option A: Llama-2-70b-chat-hf
Llama-2 is a text-based LLM optimized for chat and text generation, not speech-to-text. It lacks transcription capabilities.
Databricks Reference: "Llama models are designed for natural language generation, not audio processing" ("Databricks Model Catalog").
Option B: MPT-30B-Instruct
MPT-30B is another text-based LLM focused on instruction-following and text generation, not transcription. It's irrelevant for speech-to-text tasks.
Databricks Reference: No specific mention, but MPT is categorized under text LLMs in Databricks' ecosystem, not audio models.
Option C: DBRX
DBRX, developed by Databricks, is a powerful text-based LLM for general-purpose generation. It doesn't natively support speech-to-text and isn't optimized for transcription.
Databricks Reference: "DBRX excels at text generation and reasoning tasks" ("Introducing DBRX," 2023)-no mention of audio capabilities.
Option D: whisper-large-v3 (1.6B)
Whisper, developed by OpenAI, is an open-source model specifically designed for speech-to-text transcription. The "large-v3" variant (1.6 billion parameters) balances accuracy and efficiency, with optimizations for speed via quantization or deployment on GPUs-key for the application's requirements.
Databricks Reference: "For audio transcription, models like Whisper are recommended for their speed and accuracy" ("Generative AI Cookbook," 2023). Databricks supports Whisper integration in its MLflow or Lakehouse workflows.
Conclusion: Only D. whisper-large-v3 is a speech-to-text model, making it the sole suitable choice. Its design prioritizes transcription, and its efficiency (e.g., via optimized inference) meets the speed requirement, aligning with Databricks' model deployment best practices.


질문 # 57
A Generative AI Engineer is testing a simple prompt template in LangChain using the code below, but is getting an error:
Python
from langchain.chains import LLMChain
from langchain_community.llms import OpenAI
from langchain_core.prompts import PromptTemplate
prompt_template = "Tell me a {adjective} joke"
prompt = PromptTemplate(input_variables=["adjective"], template=prompt_template)
# ... (Error-prone section)
Assuming the API key was properly defined, what change does the Generative AI Engineer need to make to fix their chain?

정답：D

설명：
The error in the original snippet usually stems from the improper instantiation of the LLMChain or the incorrect call to the .generate() method. In LangChain, an LLMChain requires two primary components: an LLM (the engine) and a Prompt (the template). Option C provides the correct syntax: first, the PromptTemplate is defined with the correct input_variables. Second, the OpenAI model is instantiated. Third, the LLMChain binds the model and the prompt together. Finally, the .generate() method expects a list of dictionaries, where each dictionary represents a set of inputs for the prompt variables. Options A, B, and D in the original image contain syntax errors such as passing the variable directly into the chain initialization or missing the dictionary list format required by the standard LangChain API for batch-like generation.


질문 # 58
A Generative Al Engineer is building a RAG application that answers questions about internal documents for the company SnoPen AI.
The source documents may contain a significant amount of irrelevant content, such as advertisements, sports news, or entertainment news, or content about other companies.
Which approach is advisable when building a RAG application to achieve this goal of filtering irrelevant information?

정답：C

설명：
In a Retrieval-Augmented Generation (RAG) application built to answer questions about internal documents, especially when the dataset contains irrelevant content, it's crucial to guide the system to focus on the right information. The best way to achieve this is by including a clear instruction in the system prompt (option C).
System Prompt as Guidance:
The system prompt is an effective way to instruct the LLM to limit its focus to SnoPen AI-related content. By clearly specifying that the model should avoid answering questions unrelated to SnoPen AI, you add an additional layer of control that helps the model stay on-topic, even if irrelevant content is present in the dataset.
Why This Approach Works:
The prompt acts as a guiding principle for the model, narrowing its focus to specific domains. This prevents the model from generating answers based on irrelevant content, such as advertisements or news unrelated to SnoPen AI.
Why Other Options Are Less Suitable:
A (Keep All Articles): Retaining all content, including irrelevant materials, without any filtering makes the system prone to generating answers based on unwanted data.
B (Include in the System Prompt about SnoPen AI): This option doesn't address irrelevant content directly, and without filtering, the model might still retrieve and use irrelevant data.
D (Consolidating Documents into a Single Chunk): Grouping documents into a single chunk makes the retrieval process less efficient and won't help filter out irrelevant content effectively.
Therefore, instructing the system in the prompt not to answer questions unrelated to SnoPen AI (option C) is the best approach to ensure the system filters out irrelevant information.


질문 # 59
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?

정답：B

설명：
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.


질문 # 60
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?

정답：D

설명：
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.


질문 # 61
......

Databricks인증 Databricks-Generative-AI-Engineer-Associate시험을 패스하기 위하여 잠을 설쳐가며 시험준비 공부를 하고 계신 분들은 이 글을 보는 즉시 공부방법이 틀렸구나 하는 생각이 들것입니다. Fast2test의Databricks인증 Databricks-Generative-AI-Engineer-Associate덤프는 실제시험을 대비하여 제작한 최신버전 공부자료로서 문항수도 적합하여 불필요한 공부는 하지 않으셔도 되게끔 만들어져 있습니다.가격도 착하고 시험패스율 높은Fast2test의Databricks인증 Databricks-Generative-AI-Engineer-Associate덤프를 애용해보세요. 놀라운 기적을 안겨드릴것입니다.

Databricks-Generative-AI-Engineer-Associate퍼펙트 덤프데모 다운로드: https://kr.fast2test.com/Databricks-Generative-AI-Engineer-Associate-premium-file.html

Databricks-Generative-AI-Engineer-Associate자격증을 많이 취득하여 더욱 멋진 삶에 도전해보세요, 시험탈락시 덤프구매일로부터 60일내에 환불신청하시면 덤프비용 전액환불을 약속해드리기에 안심하시고 구매하셔도 됩니다.신뢰가 생기지 않는다면 Fast2test Databricks-Generative-AI-Engineer-Associate 구매사이트에서 무료샘플을 다운받아보세요, Databricks Databricks-Generative-AI-Engineer-Associate시험패스 가능한 공부 IT 인증자격증 취득 의향이 있으시면 저희, 그중에서 대부분 분들이Fast2test Databricks-Generative-AI-Engineer-Associate퍼펙트 덤프데모 다운로드제품에 많은 관심과 사랑을 주고 계시는데 그 원인은 무엇일가요?바로Fast2test Databricks-Generative-AI-Engineer-Associate퍼펙트 덤프데모 다운로드에서 제공해드리는 덤프자료 품질이 제일 좋고 업데이트가 제일 빠르고 가격이 제일 저렴하고 구매후 서비스가 제일 훌륭하다는 점에 있습니다, Databricks-Generative-AI-Engineer-Associate 시험을 패스하여 자격증을 취득하고 싶은 분들은 저희 덤프를 저렴한 가격에 주문하여 알맞춤 시험대비를 해보세요.

높은 자리에 올라온 이상, 사람들의 아첨이 따르는 건 당연한 일이었다, 이봐, 강프로, Databricks-Generative-AI-Engineer-Associate자격증을 많이 취득하여 더욱 멋진 삶에 도전해보세요, 시험탈락시 덤프구매일로부터 60일내에 환불신청하시면 덤프비용 전액환불을 약속해드리기에 안심하시고 구매하셔도 됩니다.신뢰가 생기지 않는다면 Fast2test Databricks-Generative-AI-Engineer-Associate 구매사이트에서 무료샘플을 다운받아보세요.

Databricks 자격증 Databricks-Generative-AI-Engineer-Associate 시험문제와 답

IT 인증자격증 취득 의향이 있으시면 저희, 그중에서 대부분 분들이Fast2test제품에 많은 관심Databricks-Generative-AI-Engineer-Associate과 사랑을 주고 계시는데 그 원인은 무엇일가요?바로Fast2test에서 제공해드리는 덤프자료 품질이 제일 좋고 업데이트가 제일 빠르고 가격이 제일 저렴하고 구매후 서비스가 제일 훌륭하다는 점에 있습니다.

Databricks-Generative-AI-Engineer-Associate 시험을 패스하여 자격증을 취득하고 싶은 분들은 저희 덤프를 저렴한 가격에 주문하여 알맞춤 시험대비를 해보세요.

그리고 Fast2test Databricks-Generative-AI-Engineer-Associate 시험 문제집의 전체 버전을 클라우드 저장소에서 다운로드할 수 있습니다: https://drive.google.com/open?id=1r0aR9-WhxEyZ8EH2JZhMS2r-GuuHbHLO

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