AI Professional exam

Which statement describes the difference between "top K" and "top P" in selecting the next token in the OCI gen ai generation models?

"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.

Which statement is true about the "top p" parameter of the OCI gen AI generation models?

"top p" limits token selection based on the sum of their probabilities.

Which is not a category of pretrained foundational models available in the OCI generative AI service?

Translation models

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 group of transformer layers.

How does the architecture of dedicated AI clusters contribute to minimizing GPU memory overhead for Tfew Fine-tuning model inference?

By sharing base model weights across multiple fine-tuned models on the same group of GPUs

You create a fine-tuning dedicated AI cluster to customize foundational model with your custom training. How many unit hours are required for fine-tuning if the cluster is active for 10 hours?

20 unit hours

Given a block of code, what does a chain typically interact with memory during execution?

After user input but before chain execution, and again after core logic but before output.

Which Oracle Accelerated Data Science class can be used to deploy a large language model application to OCI data science model deployment?

Chain Deployment

Which technique involves prompting the large language model to emit intermediate reasoning steps as part of its response?

Chain of thought

What is the primary function of the "temperature" parameter in the OCI gen AI generation model?

Controls the randomness of the model's output, affecting its creativity.

Which is NOT a built - in memory type in langchain

Conversion image memory

How does DOT product and cosine distance differ in their application to comparing text embeddings in natural language processing?

DOT product is used for semantics, whereas cosine distance is used for syntactic comparison.

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 generate a sequence of words.

What should you use the t-few fine tuning methods for training a model?

For data sets with a few thousand samples or less

Which distinguishes the cohere embed v3 model from its predecessor in the OCI gen ai service?

Improved retrieval for retrieval augmented generation systems (RAGS)

What does a dedicated RDMA cluster network do during model fine tuning and inference?

It enables the deployment of multiple fine-tuned models within a single cluster.

What is the main characteristic of greedy decoding in the context of language model word prediction?

It picks the most likely word to emit at each step of decoding.

How does the integration of a vector database into retrieval-augmented generation based LLM fundamentally alter their responses?

It shifts the basis of their responses from pretrained internal knowledge to real-time data retrieval.

What is the purpose of the "stop sequence" parameter in the OCI gen AI generation models?

It specifies a string that tells the model to stop generating more content

Why is normalization of vectors important before indexing in a hybrid search system?

It standardizes vector lengths for meaningful comparison using metrics such as cosine similarity

Which statement is true about langchain expression language?

LCEL is a declarative and preferred way to compose chains together.

In lang chain, which retriever search type is used to balance between relevancy and diversity?

MMR

What issues might arise from using small data sets with the vanilla fine-tuning method in the oci gen ai service?

Overfitting

Which is a distinguishing feature of "parameter-efficinet fine tuning" (PEFT)" as opposed to classic "fine tuning" in LLM training?

PEFT involves only a few or new parameters and uses labeled, task specific data.

which statement is true about prompt template in relation to input variables?

Prompt template supports any number of variables, including the possibility of having none?

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.

An AI development company is working on an advanced AI assistant capable of handling queries in a seamless manner, their goal is t ocreate an assistant that can analyze images provided by users and generate descriptive texxt. Consider the capabilities, which type of model would the company likely focus on integrating into their AI assistant?

Retrieval-augmented generation model that uses text as input and output.

Which role does a "model endpoint" serve in the inference workflow of the oci gen ai service?

Serves as a designated point for user requests and model responses.

How are fine-tuned customer models stored to enable strong data privacy and security in the OCI gen-ai services?

Stored in object storage encrypted by default

Which is a key characteristic of the annotation process used in T-Few Fine tuning?

T-Few fine-tuning requires manual annotation of input-output pairs.

What does "loss" measure in the evaluation of OCI gen ai fine tuned models?

The percentage of incorrect predictions made by the model compared with the total number of predictions in the evaluation.

Which is a cost-related benefit of using vector databases with large language models?

They offer real time updated knowledge bases and are cheaper than fine-tuned llms

Which is not a typical use case for Langsmith Evaluators

assessing code reliability

What is a key advantage of t-few over vanilla fine tuning in the OCI gen ai service?

faster training time and lower cost.

Which component of RAG evaluates and prioritizes the information retrieved by the system?

ranker

What does a higher number assigned to a token signify in the "show likelihoods" feature of the language model token generation?

the token is more likely to follow the current token