[Nov 26, 2024] 1z0-1127-24 PDF Recently Updated Questions Dumps to Improve Exam Score

1z0-1127-24 Dumps Full Questions with Free PDF Questions to Pass

NEW QUESTION # 25
Which technique involves prompting the Large Language Model (LLM) to emit intermediate reasoning steps as part of its response?

• A. Least to most Prompting
• B. Step-Bock Prompting
• C. In context Learning
• D. Chain-of-Through

Answer: D

NEW QUESTION # 26
Which is a distinguishing feature of "Parameter-Efficient Fine-tuning (PEFT)" as opposed to classic Tine- tuning" in Large Language Model training?

• A. PEFT modifies all parameters and uses unlabeled, task-agnostic data.
• B. PEFT does not modify any parameters but uses soft prompting with unlabeled data. PEFT modifies
• C. PEFT involves only a few or new parameters and uses labeled, task-specific data.
• D. PEFT parameters and b typically used when no training data exists.

Answer: C

NEW QUESTION # 27
Which role docs a "model end point" serve in the inference workflow of the OCI Generative AI service?

• A. Evaluates the performance metrics of the custom model
• B. Serves as a designated point for user requests and model responses
• C. Updates the weights of the base model during the fine-tuning process
• D. Hosts the training data for fine-tuning custom model

Answer: D

NEW QUESTION # 28
Which component of Retrieval-Augmented Generation (RAG) evaluates and prioritizes the information retrieved by the retrieval system?

• A. Encoder-decoder
• B. Ranker
• C. Generator
• D. Retriever

Answer: B

NEW QUESTION # 29
What is the primary purpose of LangSmith Tracing?

• A. To generate test cases for language models
• B. To debug issues in language model outputs
• C. To monitor the performance of language models
• D. To analyze the reasoning process of language

Answer: D

NEW QUESTION # 30
When should you use the T-Few fine-tuning method for training a model?

• A. For data sets with a few thousand samples or less
• B. For data sets with hundreds of thousands to millions of samples
• C. For complicated semantical undemanding improvement
• D. For models that require their own hosting dedicated Al duster

Answer: B

NEW QUESTION # 31
How does the utilization of T-Few transformer layers contribute to the efficiency of the fine-tuning process?

• A. By excluding transformer layers from the fine-tuning process entirely
• B. By allowing updates across all layers of the model
• C. By restricting updates to only a specific croup of transformer Layers
• D. By incorporating additional layers to the base model

Answer: C

NEW QUESTION # 32
Given the following prompts used with a Large Language Model, classify each as employing the Chain-of- Thought, Least-to-most, or Step-Back prompting technique.
L Calculate the total number of wheels needed for 3 cars. Cars have 4 wheels each. Then, use the total number of wheels to determine how many sets of wheels we can buy with $200 if one set (4 wheels) costs $50.
2. Solve a complex math problem by first identifying the formula needed, and then solve a simpler version of the problem before tackling the full question.
3. To understand the impact of greenhouse gases on climate change, let's start by defining what greenhouse gases are. Next, well explore how they trap heat in the Earths atmosphere.

• A. 1:Step-Back, 2:Chain-of-Thought, 3:Least-to-most
• B. 1:Chain-of-throught, 2: Least-to-most, 3:Step-Back
• C. 1:Chain-of-Thought ,2:Step-Back, 3:Least-to most
• D. 1:Least-to-most, 2 Chain-of-Thought, 3:Step-Back

Answer: A

NEW QUESTION # 33
Which is the main characteristic of greedy decoding in the context of language model word prediction?

• A. It chooses words randomly from the set of less probable candidates.
• B. It picks the most likely word email at each step of decoding.
• C. It requires a large temperature setting to ensure diverse word selection.
• D. It selects words bated on a flattened distribution over the vocabulary.

Answer: B

NEW QUESTION # 34
Given a block of code:
qa = Conversational Retrieval Chain, from 11m (11m, retriever-retv, memory-memory) when does a chain typically interact with memory during execution?

• A. Before user input and after chain execution
• B. Continuously throughout the entire chain execution process
• C. Only after the output has been generated
• D. After user input but before chain execution, and again after core logic but before output

Answer: C

NEW QUESTION # 35
What does a higher number assigned to a token signify in the "Show Likelihoods" feature of the language model token generation?

• A. The token is unrelated to the current token and will not be used.
• B. The token is less likely to follow the current token.
• C. The token is more likely to follow the current token.
• D. The token will be the only one considered in the next generation step.

Answer: C

NEW QUESTION # 36
ow do Dot Product and Cosine Distance differ in their application to comparing text embeddings in natural language?

• A. Dot Product measures the magnitude and direction vectors, whereas Cosine Distance focuses on the orientation regardless of magnitude.
• B. Dot Product calculates the literal overlap of words, whereas Cosine Distance evaluates the stylistic similarity.
• C. Dot Product assesses the overall similarity in content, whereas Cosine Distance measures topical relevance.
• D. Dot Product is used for semantic analysis, whereas Cosine Distance is used for syntactic comparisons.

Answer: A

NEW QUESTION # 37
What does "Loss" measure in the evaluation of OCI Generative AI fine-tuned models?
The difference between the accuracy of the model at the beginning of training and the accuracy of the deployed model

• A. The difference between the accuracy of the model at the beginning of training and the accuracy of the deployed model
• B. The level of incorrectness in the models predictions, with lower values indicating better performance
• C. The improvement in accuracy achieved by the model during training on the user-uploaded data set
• D. The percentage of incorrect predictions made by the model compared with the total number of predictions in the evaluation

Answer: B

NEW QUESTION # 38
What issue might arise from using small data sets with the Vanilla fine-tuning method in the OCI Generative AI service?

• A. Model Drift
• B. Overfilling
• C. Underfitting
• D. Data Leakage

Answer: B

NEW QUESTION # 39
Which is NOT a typical use case for LangSmith Evaluators?

• A. Evaluating factual accuracy of outputs
• B. Aliening code readability
• C. Measuring coherence of generated text
• D. Detecting bias or toxicity

Answer: B

NEW QUESTION # 40
What distinguishes the Cohere Embed v3 model from its predecessor in the OCI Generative AI service?

• A. Capacity to translate text in over u languages
• B. Emphasis on syntactic clustering of word embedding's
• C. Improved retrievals for Retrieval Augmented Generation (RAG) systems
• D. Support for tokenizing longer sentences

Answer: C

NEW QUESTION # 41
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