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Study 1z0-1127-24 Dumps - 1z0-1127-24 Latest Braindumps Questions

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Oracle 1z0-1127-24 Exam Syllabus Topics:

Topic
Details

Topic 1

Using OCI Generative AI Service: For AI Specialists, this section covers dedicated AI clusters for fine-tuning and inference. The topic also focuses on the fundamentals of OCI Generative AI service, foundational models for Generation, Summarization, and Embedding.

Topic 2

Building an LLM Application with OCI Generative AI Service: For AI Engineers, this section covers Retrieval Augmented Generation (RAG) concepts, vector database concepts, and semantic search concepts. It also focuses on deploying an LLM, tracing and evaluating an LLM, and building an LLM application with RAG and LangChain.

Topic 3

Fundamentals of Large Language Models (LLMs): For AI developers and Cloud Architects, this topic discusses LLM architectures and LLM fine-tuning. Additionally, it focuses on prompts for LLMs and fundamentals of code models.

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Oracle Cloud Infrastructure 2024 Generative AI Professional Sample Questions (Q33-Q38):

NEW QUESTION # 33
In which scenario is soft prompting appropriate compared to other training styles?

A. When there is a significant amount of labeled, task-specific data available
B. When there is a need to add learnable parameters to a Large Language Model (LLM) without task-specific training
C. When the model requires continued pretraining on unlabeled data
D. When the model needs to be adapted to perform well in a domain on which it was not originally trained

Answer: B

Explanation:
Soft prompting is an efficient method for modifying LLM behavior without full retraining. Unlike fine-tuning, soft prompting adds learnable embeddings (soft prompts) to guide the model.
When Soft Prompting is Useful:
Enhances model behavior without full retraining.
Uses small trainable prompt tokens, avoiding large parameter updates.
Works well when labeled, task-specific data is unavailable.
Why Other Options Are Incorrect:
(A) is incorrect because continued pretraining involves modifying core model weights.
(C) is incorrect because adapting a model to a new domain is better suited to fine-tuning or full retraining.
(D) is incorrect because soft prompting is designed for low-data scenarios, while full fine-tuning requires labeled datasets.
🔹 Oracle Generative AI Reference:
Oracle AI supports efficient adaptation methods, including soft prompting and LoRA, to improve LLM flexibility.

NEW QUESTION # 34
What issue might arise from using small data sets with the Vanilla fine-tuning method in the OCI Generative AI service?

A. Data Leakage
B. Overfilling
C. Underfitting
D. Model Drift

Answer: C

Explanation:
Using small data sets with the Vanilla fine-tuning method in the OCI Generative AI service might result in underfitting. Underfitting occurs when a model is too simplistic to capture the underlying patterns in the data, leading to poor performance on both training and validation data. This is particularly problematic with small data sets because there may not be enough information for the model to learn the necessary patterns and relationships.
Reference
Articles on machine learning challenges with small data sets
Technical documentation on fine-tuning models in OCI

NEW QUESTION # 35
What does a dedicated RDMA cluster network do during model fine-tuning and inference?

A. It increases G PU memory requirements for model deployment.
B. It enables the deployment of multiple fine-tuned models.
C. It limits the number of fine-tuned model deployable on the same GPU cluster.
D. It leads to higher latency in model inference.

Answer: B

NEW QUESTION # 36
How does the structure of vector databases differ from traditional relational databases?

A. It is based on distances and similarities in a vector space.
B. It is not optimized for high-dimensional spaces.
C. It uses simple row-based data storage.
D. A vector database stores data in a linear or tabular format.

Answer: A

Explanation:
Vector databases are specialized database systems designed to store and retrieve high-dimensional vector embeddings. Unlike traditional relational databases (RDBMS), which organize data into tables with rows and columns, vector databases function using mathematical distances in a multi-dimensional vector space.
How Vector Databases Differ:
Optimized for High-Dimensional Spaces: Designed to efficiently search for similar embeddings in large AI-driven applications (e.g., recommendation systems, image search).
Similarity-Based Retrieval: Uses distance metrics such as cosine similarity, Euclidean distance, or Manhattan distance to find the closest vectors.
Indexing Techniques: Implements approximate nearest neighbor (ANN) algorithms to speed up searches.
Why Other Options Are Incorrect:
(A) is incorrect because vector databases are optimized for high-dimensional spaces.
(C) & (D) are incorrect because vector databases do not use row-based or tabular storage.
🔹 Oracle Generative AI Reference:
Oracle integrates vector databases into its AI and ML solutions, enabling efficient similarity searches and AI-driven applications.

NEW QUESTION # 37
Which is a key advantage of usingT-Few over Vanilla fine-tuning in the OCI Generative AI service?

A. Foster training time and lower cost
B. Increased model interpretability
C. Reduced model complexity
D. Enhanced generalization to unseen data

Answer: A

Explanation:
The key advantage of using T-Few over Vanilla fine-tuning in the OCI Generative AI service is faster training time and lower cost. T-Few fine-tuning is designed to be more efficient by updating only a fraction of the model's parameters, which significantly reduces the computational resources and time required for fine-tuning. This efficiency translates to lower costs, making it a more economical choice for model fine-tuning.
Reference
Technical documentation on T-Few fine-tuning
Research articles comparing fine-tuning methods in machine learning

NEW QUESTION # 38
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