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Latest Oracle 1z0-1127-24 Practice Test Questions, Oracle Cloud Infrastructure 2024 Generative AI Professional Exam Dumps
NEW QUESTION # 12
How does the temperature setting in a decoding algorithm influence the probability distribution over the vocabulary?
- A. Temperature has no effect on probability distribution; it only changes the speed of decoding.
- B. Decreasing the temperature broadens the distribution, making less likely words more probable.
- C. Increasing the temperature flattens the distribution, allowing for more varied word choices.
- D. Increasing the temperature removes the impact of the most likely word.
Answer: C
Explanation:
Temperature is a parameter in LLM decoding algorithms that controls randomness in text generation.
Effects of Temperature on Text Generation:
Higher Temperature (>1.0):
Flattens the probability distribution, making lower-probability words more likely.
Increases randomness, resulting in more creative and diverse outputs.
Lower Temperature (<1.0):
Sharpening effect, making high-probability words more dominant.
Produces more predictable and deterministic responses.
Why Other Options Are Incorrect:
(B) is incorrect because temperature does not remove the impact of likely words; it reduces or increases randomness.
(C) is incorrect because temperature affects probability, not speed.
(D) is incorrect because decreasing the temperature narrows the distribution, making text more deterministic.
🔹 Oracle Generative AI Reference:
Oracle AI models allow dynamic temperature control to balance coherence and creativity in text generation.
NEW QUESTION # 13
Which statement is true about the "Top p" parameter of the OCI Generative AI Generation models?
- A. Top p determines the maximum number of tokens per response.
- B. Top p selects tokens from the "Top k' tokens sorted by probability.
- C. Top p limits token selection based on the sum of their probabilities.
- D. Top p assigns penalties to frequently occurring tokens.
Answer: C
NEW QUESTION # 14
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. Improved retrievals for Retrieval Augmented Generation (RAG) systems
- C. Support for tokenizing longer sentences
- D. Emphasis on syntactic clustering of word embedding's
Answer: B
NEW QUESTION # 15
Which is NOT a category of pertained foundational models available in the OCI Generative AI service?
- A. Embedding models
- B. Summarization models
- C. Generation models
- D. Translation models
Answer: D
NEW QUESTION # 16
Which component of Retrieval-Augmented Generation (RAG) evaluates and prioritizes the information retrieved by the retrieval system?
- A. Ranker
- B. Generator
- C. Encoder-decoder
- D. Retriever
Answer: A
NEW QUESTION # 17
What does a dedicated RDMA cluster network do during model fine-tuning and inference?
- A. It leads to higher latency in model inference.
- B. It enables the deployment of multiple fine-tuned models.
- C. It increases G PU memory requirements for model deployment.
- D. It limits the number of fine-tuned model deployable on the same GPU cluster.
Answer: B
Explanation:
A dedicated RDMA (Remote Direct Memory Access) cluster network is crucial during model fine-tuning and inference because it facilitates high-speed, low-latency communication between GPUs. This capability is essential for scaling up the deployment of multiple fine-tuned models across a GPU cluster.
RDMA allows data to be transferred directly between the memory of different computers without involving the CPU, leading to significantly reduced latency and higher throughput. This efficiency is particularly important in the context of fine-tuning and deploying large language models, where the speed and efficiency of data transfer can impact overall performance and scalability.
By enabling fast and efficient communication, a dedicated RDMA cluster network supports the deployment of multiple fine-tuned models on the same GPU cluster, enhancing both flexibility and scalability in handling various AI workloads.
Reference
Oracle Cloud Infrastructure (OCI) documentation on RDMA cluster networks Technical resources on the benefits of RDMA in high-performance computing environments
NEW QUESTION # 18
What does the RAG Sequence model do in the context of generating a response?
- A. It retrieves relevant documents only for the initial part of the query and ignores the rest.
- B. It retrieves a single relevant document for the entire input query and generates a response based on that alone.
- C. It modifies the input query before retrieving relevant documents to ensure a diverse response.
- D. For each input query, it retrieves a set of relevant documents and considers them together to generate a cohesive response.
Answer: D
Explanation:
RAG (Retrieval-Augmented Generation) Sequence models combine retrieval-based search with LLM-generated responses, ensuring factually grounded and contextually relevant outputs.
How the RAG Sequence Model Works:
Retrieves multiple documents for an input query.
Uses all retrieved documents collectively to generate a well-informed response.
Ensures the answer is contextually aware and factually accurate.
Why Other Options Are Incorrect:
(A) is incorrect because RAG does not ignore part of the query.
(B) is incorrect because it does not rely on a single document.
(C) is incorrect because RAG does not modify the input query but focuses on retrieval and generation.
🔹 Oracle Generative AI Reference:
Oracle AI implements RAG-based architectures to enhance LLM-generated responses by retrieving and grounding responses in factual data.
NEW QUESTION # 19
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 percentage of incorrect predictions made by the model compared with the total number of predictions in the evaluation
- B. The level of incorrectness in the models predictions, with lower values indicating better performance
- C. The difference between the accuracy of the model at the beginning of training and the accuracy of the deployed model
- D. The improvement in accuracy achieved by the model during training on the user-uploaded data set
Answer: B
Explanation:
In the evaluation of OCI Generative AI fine-tuned models, "Loss" measures the level of incorrectness in the model's predictions. It quantifies how far the model's predictions are from the actual values. Lower loss values indicate better performance, as they reflect a smaller discrepancy between the predicted and true values. The goal during training is to minimize the loss, thereby improving the model's accuracy and reliability.
Reference
Articles on loss functions in machine learning
OCI Generative AI service documentation on model evaluation metrics
NEW QUESTION # 20
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 will be the only one considered in the next generation step.
- C. The token is more likely to follow the current token.
- D. The token is less likely to follow the current token.
Answer: C
NEW QUESTION # 21
What issue might arise from using small data sets with the Vanilla fine-tuning method in the OCI Generative AI service?
- A. Underfitting
- B. Overfilling
- C. Data Leakage
- D. Model Drift
Answer: B
NEW QUESTION # 22
Which statement describes the difference between Top V and Top p" in selecting the next token in the OCI Generative AI Generation models?
- A. Top K considers the sum of probabilities of the top tokens, whereas Top" selects from the Top k" tokens sorted by probability.
- B. Top k and Top p" both select from the same set of tokens but use different methods to prioritize them based on frequency.
- C. Top k and "Top p" are identical in their approach to token selection but differ in their application of penalties to tokens.
- D. Top k selects the next token based on its position in the list of probable tokens, whereas "Top p" selects based on the cumulative probability of the Top token.
Answer: A
NEW QUESTION # 23
What does "k-shot prompting* refer to when using Large Language Models for task-specific applications?
- A. Providing the exact k words in the prompt to guide the model's response
- B. Explicitly providing k examples of the intended task in the prompt to guide the models output
- C. Limiting the model to only k possible outcomes or answers for a given task
- D. The process of training the model on k different tasks simultaneously to improve its versatility
Answer: B
Explanation:
K-shot prompting refers to providing the language model with k examples of the task at hand within the prompt. These examples help guide the model's understanding and output by demonstrating the desired format and approach. This technique is used to improve the model's performance on specific tasks by showing it how to handle similar situations.
Reference
Research papers on few-shot learning and prompting techniques
Technical documentation on using examples in prompts for large language models
NEW QUESTION # 24
An AI development company is working on an advanced AI assistant capable of handling queries in a seamless manner. Their goal is to create an assistant that can analyze images provided by users and generate descriptive text, as well as take text descriptions and produce accurate visual representations. Considering the capabilities, which type of model would the company likely focus on integrating into their AI assistant?
- A. A language model that operates on a token-by-token output basis
- B. A Retrieval Augmented Generation (RAG) model that uses text as input and output
- C. A diffusion model that specializes in producing complex outputs.
- D. A Large Language Model based agent that focuses on generating textual responses
Answer: C
Explanation:
An AI development company aiming to create an assistant capable of analyzing images and generating descriptive text, as well as converting text descriptions into accurate visual representations, would likely focus on integrating a diffusion model. Diffusion models are advanced generative models that specialize in producing complex outputs, including high-quality images from textual descriptions and vice versa.
Reference
Research papers on diffusion models and their applications
Technical documentation on generative models for image and text synthesis
NEW QUESTION # 25
Which is NOT a typical use case for LangSmith Evaluators?
- A. Detecting bias or toxicity
- B. Aliening code readability
- C. Evaluating factual accuracy of outputs
- D. Measuring coherence of generated text
Answer: B
Explanation:
LangSmith Evaluators are not typically used for aligning code readability. Instead, they are used for tasks such as measuring the coherence of generated text, evaluating the factual accuracy of outputs, and detecting bias or toxicity. Evaluators help ensure the quality and reliability of the outputs generated by language models.
Reference
LangSmith documentation on evaluators
Research articles on evaluation metrics for language models
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 parameters and b typically used when no training data exists.
- B. PEFT modifies all parameters and uses unlabeled, task-agnostic data.
- C. PEFT involves only a few or new parameters and uses labeled, task-specific data.
- D. PEFT does not modify any parameters but uses soft prompting with unlabeled data. PEFT modifies
Answer: C
NEW QUESTION # 27
Which is NOT a built-in memory type in LangChain?
- A. Conversation Token Buffer Memory
- B. Conversation ImgeMemory
- C. Conversation Summary Memory
- D. Conversation Buffer Memory
Answer: B
NEW QUESTION # 28
Which technique involves prompting the Large Language Model (LLM) to emit intermediate reasoning steps as part of its response?
- A. Step-Bock Prompting
- B. In context Learning
- C. Least to most Prompting
- D. Chain-of-Through
Answer: D
Explanation:
Chain-of-Thought prompting involves prompting the Large Language Model (LLM) to emit intermediate reasoning steps as part of its response. This technique helps the model articulate its thought process and reasoning, leading to more transparent and understandable outputs. By breaking down the problem into smaller, logical steps, the model can provide more accurate and detailed responses.
Reference
Research articles on Chain-of-Thought prompting
Technical guides on enhancing model transparency and reasoning with intermediate steps
NEW QUESTION # 29
What is the primary function of the "temperature" parameter in the OCI Generative AI Generation models?
- A. Assigns a penalty to tokens that have already appeared in the preceding text
- B. Controls the randomness of the model's output, affecting its creativity
- C. Specifies a string that tells the model to stop generating more content
- D. Determines the maximum number of tokens the model can generate per response
Answer: B
NEW QUESTION # 30
An AI development company is working on an advanced AI assistant capable of handling queries in a seamless manner. Their goal is to create an assistant that can analyze images provided by users and generate descriptive text, as well as take text descriptions and produce accurate visual representations. Considering the capabilities, which type of model would the company likely focus on integrating into their AI assistant?
- A. A language model that operates on a token-by-token output basis
- B. A diffusion model that specializes in producing complex outputs.
- C. A Retrieval Augmented Generation (RAG) model that uses text as input and output
- D. A Large Language Model based agent that focuses on generating textual responses
Answer: C
NEW QUESTION # 31
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 percentage of incorrect predictions made by the model compared with the total number of predictions in the evaluation
- B. The level of incorrectness in the models predictions, with lower values indicating better performance
- C. The difference between the accuracy of the model at the beginning of training and the accuracy of the deployed model
- D. The improvement in accuracy achieved by the model during training on the user-uploaded data set
Answer: B
NEW QUESTION # 32
Which is a cost-related benefit of using vector databases with Large Language Models (LLMs)?
- A. They increase the cost due to the need for real- time updates.
- B. They offer real-time updated knowledge bases and are cheaper than fine-tuned LLMs.
- C. They require frequent manual updates, which increase operational costs.
- D. They are more expensive but provide higher quality data.
Answer: B
NEW QUESTION # 33
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:Least-to-most, 2 Chain-of-Thought, 3:Step-Back
- 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:Step-Back, 2:Chain-of-Thought, 3:Least-to-most
Answer: D
NEW QUESTION # 34
How does the architecture of dedicated Al clusters contribute to minimizing GPU memory overhead forT- Few fine-tuned model inference?
- A. By allocating separate GPUS for each model instance
- B. By optimizing GPIJ memory utilization for each model's unique para
- C. By loading the entire model into G PU memory for efficient processing
- D. By sharing base model weights across multiple fine-tuned model's on the same group of GPUs
Answer: D
Explanation:
The architecture of dedicated AI clusters contributes to minimizing GPU memory overhead for fine-tuned model inference by sharing base model weights across multiple fine-tuned models on the same group of GPUs. This approach allows different fine-tuned models to leverage the shared base model weights, reducing the memory requirements and enabling efficient use of GPU resources. By not duplicating the base model weights for each fine-tuned model, the system can handle more models simultaneously with lower memory overhead.
Reference
Technical documentation on AI cluster architectures
Research articles on optimizing GPU memory utilization in model inference
NEW QUESTION # 35
Which technique involves prompting the Large Language Model (LLM) to emit intermediate reasoning steps as part of its response?
- A. Step-Bock Prompting
- B. In context Learning
- C. Least to most Prompting
- D. Chain-of-Through
Answer: D
NEW QUESTION # 36
How are fine-tuned customer models stored to enable strong data privacy and security in the OCI Generative AI service?
- A. Stored in an unencrypted form in Object Storage
- B. Stored in Object Storage encrypted by default
- C. Shared among multiple customers for efficiency
- D. Stored in Key Management service
Answer: B
Explanation:
Fine-tuned customer models in the OCI Generative AI service are stored in Object Storage, and they are encrypted by default. This encryption ensures strong data privacy and security by protecting the model data from unauthorized access. Using encrypted storage is a key measure in safeguarding sensitive information and maintaining compliance with security standards.
Reference
OCI documentation on data storage and security practices
Technical details on encryption and data privacy in OCI services
NEW QUESTION # 37
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