2024 1z0-1127-24 dumps review – Professional Quiz Study Materials [Q25-Q43]

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2024 1z0-1127-24 dumps review – Professional Quiz Study Materials

1z0-1127-24 Test Prep Training Practice Exam Questions Practice Tests

NEW QUESTION 25
Which is NOT a built-in memory type in LangChain?

Conversation Token Buffer Memory

Conversation ImgeMemory

Conversation Buffer Memory

Conversation Summary Memory

NEW QUESTION 26
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

NEW QUESTION 27
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 does not modify any parameters but uses soft prompting with unlabeled data. PEFT modifies

PEFT parameters and b typically used when no training data exists.

NEW QUESTION 28
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.

NEW QUESTION 29
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.

NEW QUESTION 30
What does a dedicated RDMA cluster network do during model fine-tuning and inference?

It leads to higher latency in model inference.

It enables the deployment of multiple fine-tuned models.

It limits the number of fine-tuned model deployable on the same GPU cluster.

It increases G PU memory requirements for model deployment.

NEW QUESTION 31
Which technique involves prompting the Large Language Model (LLM) to emit intermediate reasoning steps as part of its response?

Step-Bock Prompting

Chain-of-Through

Least to most Prompting

In context Learning

NEW QUESTION 32
Which is the main characteristic of greedy decoding in the context of language model word prediction?

It chooses words randomly from the set of less probable candidates.

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.

NEW QUESTION 33
How are fine-tuned customer models stored to enable strong data privacy and security in the OCI Generative AI service?

Stored in Object Storage encrypted by default

Shared among multiple customers for efficiency

Stored in Key Management service

Stored in an unencrypted form in Object Storage

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?

Continuously throughout the entire chain execution process

Only after the output has been generated

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

Before user input and after chain execution

NEW QUESTION 35
Which is a key advantage of usingT-Few over Vanilla fine-tuning in the OCI Generative AI service?

Reduced model complexity

Enhanced generalization to unseen data

Increased model interpretability

Foster training time and lower cost

NEW QUESTION 36
What is the primary purpose of LangSmith Tracing?

To monitor the performance of language models

To generate test cases for language models

To analyze the reasoning process of language

To debug issues in language model outputs

NEW QUESTION 37
Given the following code:
Prompt Template
(input_variable[”rhuman_input”,’city”], template-template)
Which statement is true about Promt Template in relation to input_variables?

PromptTemplate requires a minimum of two variables to function property.

PromptTemplate can support only a single variable M a time.

PromptTemplate supports Any number of variable*, including the possibility of having none.

PromptTemplate is unable to use any variables.

NEW QUESTION 38
Which statement describes the difference between Top V and Top p” in selecting the next token in the OCI Generative 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.

Top K considers the sum of probabilities of the top tokens, whereas Top” 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.

Top k and “Top p” are identical in their approach to token selection but differ in their application of penalties to tokens.

NEW QUESTION 39
In LangChain, which retriever search type is used to balance between relevancy and diversity?

mmr

similarity

similarity_score_threshold

top k

NEW QUESTION 40
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.

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