[Q19-Q34] Real Oracle 1z0-1127-24 Exam Questions [Updated 2024]

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Real Oracle 1z0-1127-24 Exam Questions [Updated 2024]

1z0-1127-24 Exam Dumps Pass with Updated 2024 Oracle Cloud Infrastructure 2024 Generative AI Professional

QUESTION 19
Which role docs a “model end point” serve in the inference workflow of the OCI Generative AI service?

Hosts the training data for fine-tuning custom model

Evaluates the performance metrics of the custom model

Serves as a designated point for user requests and model responses

Updates the weights of the base model during the fine-tuning process

QUESTION 20
How does the utilization of T-Few transformer layers contribute to the efficiency of the fine-tuning process?

By incorporating additional layers to the base model

By allowing updates across all layers of the model

By excluding transformer layers from the fine-tuning process entirely

By restricting updates to only a specific croup of transformer Layers

QUESTION 21
In LangChain, which retriever search type is used to balance between relevancy and diversity?

mmr

similarity

similarity_score_threshold

top k

QUESTION 22
Which statement best describes the role of encoder and decoder models in natural language processing?

Encoder models and decoder models both convert sequence* of words into vector representations without generating new text.

Encoder models are used only for numerical calculations, whereas decoder models are used to interpret the calculated numerical values back into text.

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 convert a sequence of words into a vector representation, and decoder models take this vector representation to sequence of words.

QUESTION 23
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 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.

It limits their ability to understand and generate natural language.

QUESTION 24
What is the purpose of the “stop sequence” parameter in the OCI Generative AI Generation models?

It com rob the randomness of the model* output, affecting its creativity.

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

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.

QUESTION 25
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

The difference between the accuracy of the model at the beginning of training and the accuracy of the deployed model

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

The improvement in accuracy achieved by the model during training on the user-uploaded data set

The level of incorrectness in the models predictions, with lower values indicating better performance

QUESTION 26
How does the architecture of dedicated Al clusters contribute to minimizing GPU memory overhead forT- Few fine-tuned model inference?

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

By optimizing GPIJ memory utilization for each model’s unique para

By allocating separate GPUS for each model instance

By loading the entire model into G PU memory for efficient processing

QUESTION 27
What distinguishes the Cohere Embed v3 model from its predecessor in the OCI Generative AI service?

Improved retrievals for Retrieval Augmented Generation (RAG) systems

Capacity to translate text in over u languages

Support for tokenizing longer sentences

Emphasis on syntactic clustering of word embedding’s

QUESTION 28
How does the Retrieval-Augmented Generation (RAG) Token technique differ from RAG Sequence when generating a model’s response?

Unlike RAG Sequence, RAG Token generates the entire response at once without considering individual parts.

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

RAG Token retrieves relevant documents for each part of the response and constructs the answer incrementally.

QUESTION 29
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:Step-Back, 2:Chain-of-Thought, 3:Least-to-most

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

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

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

QUESTION 30
Which statement is true about the “Top p” parameter of the OCI Generative AI Generation models?

Top p assigns penalties to frequently occurring tokens.

Top p determines the maximum number of tokens per response.

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

Top p selects tokens from the “Top k’ tokens sorted by probability.

QUESTION 31
You create a fine-tuning dedicated AI cluster to customize a foundational model with your custom training dat a. How many unit hours arc required for fine-tuning if the cluster is active for 10 hours?

10 unit hours

30 unit hours

15 unit hours

40 unit hours

QUESTION 32
What is the primary function of the “temperature” parameter in the OCI Generative AI Generation models?

Determines the maximum number of tokens the model can generate per response

Specifies a string that tells the model to stop generating more content

Assigns a penalty to tokens that have already appeared in the preceding text

Controls the randomness of the model’s output, affecting its creativity

QUESTION 33
ow do Dot Product and Cosine Distance differ in their application to comparing text embeddings in natural language?

Dot Product assesses the overall similarity in content, whereas Cosine Distance measures topical relevance.

Dot Product is used for semantic analysis, whereas Cosine Distance is used for syntactic comparisons.