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What is the role of temperature in the decoding process of a Large Language Model (LLM)?. To increase the accuracy of the most likely word in the vocabulary. To determine the number of words to generate in a single decoding step. To decide to which part of speech the next word should belong. To adjust the sharpness of probability distribution over vocabulary when selecting the next word.

Which statement accurately reflects the differences between these approaches in terms of the number of parameters modified and the type of data used?. Fine-tuning and continuous pretraining both modify all parameters and use labeled, task-specific data. Parameter Efficient Fine-Tuning and Soft Prompting modify all parameters of the model using unlabeled data. Fine-tuning modifies all parameters using labeled, task-specific data, whereas Parameter Efficient Fine-Tuning updates a few, new parameters also with labeled, task-specific data. Soft Prompting and continuous pretraining are both methods that require no modification to the original parameters of the model.

What is prompt engineering in the context of Large Language Models (LLMs)?. Iteratively refining the ask to elicit a desired response. Adding more layers to the neural network. Adjusting the hyperparameters of the model. Training the model on a large dataset.

What does the term "hallucination" refer to in the context of Large Language Models (LLMs)?. The model's ability to generate imaginative and creative content. A technique used to enhance the model's performance on specific tasks. The process by which the model visualizes and describes images in detail. The phenomenon where the model generates factually incorrect information or unrelated content as if it were true.

What does in-context learning in Large Language Models involve?. Pretraining the model on a specific domain. Training the model using reinforcement learning. Conditioning the model with task-specific instructions or demonstrations. Adding more layers to the model.

How does the Retrieval-Augmented Generation (RAG) Token technique differ from RAG Sequence when generating a model's response?. RAG Token retrieves relevant documents for each part of the response and constructs the answer incrementally. RAG Token does not use document retrieval but generates responses based on pre-existing knowledge only. RAG Token retrieves documents oar/at the beginning of the response generation and uses those for the entire content. Unlike RAG Sequence, RAG Token generates the entire response at once without considering individual parts.

Which Oracle Accelerated Data Science (ADS) class can be used to deploy a Large Language Model (LLM) application to OCI Data Science model deployment?. Chain Deployment. Retrieval QA. Generative AI. Text Leader.

Which is a distinguishing feature of "Parameter-Efficient Fine-tuning (PEFT)" as opposed to classic Tine- tuning" in Large Language Model training?. PEFT involves only a few or new parameters and uses labeled, task-specific data. PEFT modifies all parameters and uses unlabeled, task-agnostic data. PEFT parameters and b typically used when no training data exists. PEFT does not modify any parameters but uses soft prompting with unlabeled data. PEFT modifies.

How does the integration of a vector database into Retrieval-Augmented Generation (RAG)-based Large Language Models(LLMS) fundamentally alter their responses?. It transforms their architecture from a neural network to a traditional database system. It limits their ability to understand and generate natural language. It shifts the basis of their responses from pretrained internal knowledge to real-time data retrieval. It enables them to bypass the need for pretraining on large text corpora.

Why is normalization of vectors important before indexing in a hybrid search system?. It ensures that all vectors represent keywords only. It converts all sparse vectors to dense vectors. It standardizes vector lengths for meaningful comparison using metrics such as Cosine Similarity. It significantly reduces the size of the database.

In LangChain, which retriever search type is used to balance between relevancy and diversity?. top k. similarity_score_threshold. similarity. mmr.

How does the architecture of dedicated Al clusters contribute to minimizing GPU memory overhead forT- Few fine-tuned model inference?. By allocating separate GPUS for each model instance. By optimizing GPIJ memory utilization for each model's unique para. By sharing base model weights across multiple fine-tuned model's on the same group of GPUs. By loading the entire model into G PU memory for efficient processing.

What does a dedicated RDMA cluster network do during model fine-tuning and inference?. It increases G PU memory requirements for model deployment. It enables the deployment of multiple fine-tuned models. It leads to higher latency in model inference. It limits the number of fine-tuned model deployable on the same GPU cluster.

How do Dot Product and Cosine Distance differ in their application to comparing text embeddings in natural language?. Dot Product measures the magnitude and direction vectors, whereas Cosine Distance focuses on the orientation regardless of magnitude. Dot Product is used for semantic analysis, whereas Cosine Distance is used for syntactic comparisons. Dot Product assesses the overall similarity in content, whereas Cosine Distance measures topical relevance. Dot Product calculates the literal overlap of words, whereas Cosine Distance evaluates the stylistic similarity.

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. 1:Chain-of-Thought ,2:Step-Back, 3:Least-to most. 1:Chain-of-throught, 2: Least-to-most, 3:Step-Back. 1:Least-to-most, 2 Chain-of-Thought, 3:Step-Back. 1:Step-Back, 2:Chain-of-Thought, 3:Least-to-most.

Given the following code: chain = prompt |11m. LCEL is a programming language used to write documentation for LangChain. LCEL is a declarative and preferred way to compose chains together. Which statement is true about LangChain Expression language (ICED?. LCEL is a legacy method for creating chains in LangChain.

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

What is the purpose of the "stop sequence" parameter in the OCI Generative AI Generation models?. It specifies a string that tells the model to stop generating more content. It controls the randomness of the model's output, affecting its creativity. It assigns a penalty to frequently occurring tokens to reduce repetitive text. It determines the maximum number of tokens the model can generate per response.

Which is a key advantage of usingT-Few over Vanilla fine-tuning in the OCI Generative AI service?. Enhanced generalization to unseen data. Increased model interpretability. Foster training time and lower cost. Reduced model complexity.

How does the utilization of T-Few transformer layers contribute to the efficiency of the fine-tuning process?. By restricting updates to only a specific croup of transformer Layers. By excluding transformer layers from the fine-tuning process entirely. By incorporating additional layers to the base model. By allowing updates across all layers of the model.

Which statement best describes the role of encoder and decoder models in natural language processing?. Encoder models convert a sequence of words into a vector representation, and decoder models take this vector representation to sequence of words. Encoder models take a sequence of words and predict the next word in the sequence, whereas decoder models convert a sequence of words into a numerical representation. Encoder models are used only for numerical calculations, whereas decoder models are used to interpret the calculated numerical values back into text. Encoder models and decoder models both convert sequence* of words into vector representations without generating new text.

Which component of Retrieval-Augmented Generation (RAG) evaluates and prioritizes the information retrieved by the retrieval system?. Retriever. Ranker. Generator. Encoder-decoder.

What does "k-shot prompting* refer to when using Large Language Models for task-specific applications?. Limiting the model to only k possible outcomes or answers for a given task. Explicitly providing k examples of the intended task in the prompt to guide the models output. Providing the exact k words in the prompt to guide the model's response. The process of training the model on k different tasks simultaneously to improve its versatility.

Which technique involves prompting the Large Language Model (LLM) to emit intermediate reasoning steps as part of its response?. Chain-of-Through. Step-Bock Prompting. Least to most Prompting. In context Learning.

Which is the main characteristic of greedy decoding in the context of language model word prediction?. It requires a large temperature setting to ensure diverse word selection. It selects words bated on a flattened distribution over the vocabulary. It picks the most likely word email at each step of decoding. It chooses words randomly from the set of less probable candidates.

In the context of generating text with a Large Language Model (LLM), what does the process of greedy decoding entail?. Picking a word based on its position in a sentence structure. Choosing the word with the highest probability at each step of decoding. Selecting a random word from the entire vocabulary at each step. Using a weighted random selection based on a modulated distribution.

Which role does a "model endpoint" serve in the inference workflow of the OCI Generative AI service?. Hosts the training data for fine-tuning custom models. Updates the weights of the base model during the fine-tuning process. Serves as a designated point for user requests and model responses. Evaluates the performance metrics of the custom models.

What is the purpose of Retrieval Augmented Generation (RAG) in text generation?. To store text in an external database without using it for generation. To generate text based only on the model's internal knowledge without external data. To generate text using extra information obtained from an external data source. To retrieve text from an external source and present it without any modifications.

Which statement describes the difference between "Top k" and "Top p" in selecting the next token in the OCI Generative Al Generation models?. 'Top k'' and 'Top p'' are identical in their approach to token selection but differ in their application of penalties to tokens. ''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 tokens. ''Top k'' considers the sum of probabilities of the top tokens, whereas ''Top p '' selects from the ''Top k '' tokens sorted by probability. ''Top k'' and 'Top p'' both select from the same set of tokens but use different methods to prioritize them based on frequency.

What differentiates a code model from a standard LLM?. Code models are specifically designed to generate different creative text formats. Code models are trained on datasets containing code alongside natural language. Code models require a different neural network architecture compared to LLMs. Code models can only process and understand code, not natural language.

Which is NOT a built-in memory type in LangChain?. ConversationBufferMemory. Conversation TokenBufferMemory. ConversationImageMemory. ConversationSummaryMemory.

When creating a model endpoint in OCI Generative AI, what information is mandatory to specify?. The specific hardware configuration required for the endpoint. The pre-trained model you want to use without modification. The fine-tuned model you want to deploy for real-time inference. The validation dataset used during the fine-tuning process.

How do pre-trained models used for text embedding in OCI Generative AI represent textual data?. By assigning a unique numerical identifier to each individual word. By converting text into an image format suitable for computer vision tasks. By creating a high-dimensional vector capturing the semantic meaning of the text. By identifying and extracting named entities like people, locations,.

In the context of fine-tuning with OCI Generative AI Service, what is the role of a custom dataset?. It defines the new tasks the model should be able to perform. It provides additional training data specific to your domain. It acts as a validation set to evaluate the fine-tuned model's performance. It replaces the pre-trained weights of the base model entirely.

When troubleshooting issues with a deployed model endpoint, which OCI service can be helpful for monitoring and logging?. Oracle Container Engine for Kubernetes (OKE) for managing containerized deployments. Oracle Cloud Monitoring for tracking endpoint performance and resource utilization. Oracle Data Catalog for organizing and searching datasets used for fine-tuning. Oracle Cloud Functions for deploying serverless functions alongside.

What does accuracy measure in the context of fine-tuning results for a generative model?. The depth of the neural network layers used in the model. The number of predictions a model makes, regardless of whether they are correct or incorrect. How many predictions the model made correctly out of all the predictions in an evaluation. The proportion of incorrect predictions made by the model during an evaluation.

In the context of OCI Generative AI security, what responsibility falls on the user regarding the custom data uploaded for fine-tuning?. OCI is responsible for ensuring the privacy and security of all uploaded data. Users have no control over how their data is used after uploading it. Users retain ownership and responsibility for securing their uploaded data. Uploading data automatically grants OCI permission to share it with th.

Most relevant metric When evaluating the performance of semantic search in an LLM application built with OCI Generative AI?. The average time taken to retrieve documents from the vector database. The number of documents retrieved for a given user query. The accuracy of retrieved documents in addressing the user's intent. The similarity score between the query vector and the retrieved document vectors.

How does OCI Generative AI contribute to a secure data lifecycle when working with your custom datasets?. Users are responsible for implementing their own data encryption methods. OCI Generative AI automatically encrypts data at rest and in transit by default. Data uploaded to OCI Generative AI becomes publicly accessible for collaboration. Users retain full control over data location and access within the O.

What security best practice should be considered when configuring access to your deployed model endpoint?. Leaving the endpoint publicly accessible for ease of use. Restricting access using IAM policies and authentication tokens. Sharing endpoint details with all relevant users within your organization. Disabling monitoring for the endpoint to reduce resource consumption.

After fine-tuning a model in a dedicated AI cluster, which option allows you to deploy the model for real-world use cases?. Directly exporting the model from the cluster for local deployment. Configuring the cluster for continuous inference on incoming requests. Saving the fine-tuned model as a file and uploading it to a separate service. Creating an endpoint within the dedicated AI cluster for model access.

Which of the following statements is true about OCI Generative AI service?. It provides pre-trained large language models (LLMs) only. It allows fine-tuning pre-trained models with your own data. It requires users to manage their own AI clusters for inference. It is limited to text generation tasks only.

What must be done to activate content moderation in OCI Generative AI Agents?. Enable it in the session trace settings. Use a third-party content moderation API. Configure it in the Object Store metadata settings. Enable it when creating an endpoint for an agent.

In the given code, what does setting truncate = "NONE" do? embed_text_detail = oci.generative_ai_inference.models.EmbedTextDetail embed_text_detail.serving_mode = oci.generative_ai_inference.models.OnDemandServingMode(model_id="coher-english-v3.0") embed_text_detail.inputs = inputs embed_text_detail.truncate = "NONE". It prevents input text from being truncated before processing. It ensures that only a single word from the input is used for embedding. It removes all white space from the input text. It forces the model to limit the output text length.

A data scientist is preparing a custom dataset to fine-tune an OCI Generative Which criterion must be ensured for the dataset to be accepted?. The dataset must be in a proprietary binary format. The dataset must contain at least 32 prompt/completion pairs. The dataset must be divided into separate files for training and validation. The dataset must have a maximum of 1000 sentences per file.

Which is true about the OCI Generative AI service?. It’s a fully managed, serverless service that provides access to large language models via a unified API. It requires customers to bring their own GPU hardware. It is only for computer vision tasks, not text generation. It cannot be accessed through the OCI Console, only via API.

What does it mean that OCI GenAI provides “single API” access to multiple models?. You must integrate separate APIs for each foundation model vendor. The same unified endpoint and API format lets you switch between different underlying models with minimal code changes. All users share one global API key for the service. The API only supports one model at a time.

Which foundation models are available out of the box in OCI’s Generative AI Service for text tasks?. Models from Meta (Llama 2) and Cohere (Command family for chat; Embed for embeddings). Only Oracle’s proprietary LLMs are trained in-house. OpenAI’s GPT-4 and GPT-3 models. Google’s PaLM model family.

You need to analyze a large PDF (300 pages) for semantic search. Which model and approach should you use on OCI GenAI?. Use a chat completion model to directly input all 300 pages as a prompt. Use the embedding model to convert chunks of the document into vectors, then use similarity search for relevant content. Fine-tune the chat model on the PDF content first. This is not possible with the OCI GenAI service.

The Cohere Command-R vs. Command-R-Plus models in OCI GenAI differ primarily in what way?. The R-Plus model supports a far larger context window (prompt size up to 128k tokens) and higher performance, whereas Command-R is limited to 16k context. Command-R-Plus handles only code, Command-R handles only text. Command-R is for English, R-Plus is for multilingual tasks. R-Plus is cheaper to run but less capable than R.