Oracle 1z0-1127-24 問題集

質問 1：
Which statement is true about Fine-tuning and Parameter-Efficient Fine-Tuning (PEFT)?
A. Fine-tuning and PEFT do not involve model modification; they differ only in the type of data used for training, with Fine-tuning requiring labeled data and PEFT using unlabeled data.
B. Both Fine-tuning and PEFT require the model to be trained from scratch on new data, making them equally data and computationally intensive.
C. PEFT requires replacing the entire model architecture with a new one designed specifically for the new task, making it significantly more data-intensive than Fine-tuning.
D. Fine-tuning requires training the entire model on new data, often leading to substantial computational costs, whereas PEFT involves updating only a small subset of parameters, minimizing computational requirements and data needs.
正解：D
解説: (Topexam メンバーにのみ表示されます)

質問 2：
In the simplified workflow for managing and querying vector data, what is the role of indexing?
A. To convert vectors into a nonindexed format for easier retrieval
B. To map vectors to a data structure for faster searching, enabling efficient retrieval
C. To categorize vectors based on their originating data type (text, images, audio)
D. To compress vector data for minimized storage usage
正解：B
解説: (Topexam メンバーにのみ表示されます)

質問 3：
How does the architecture of dedicated Al clusters contribute to minimizing GPU memory overhead forT- Few fine-tuned model inference?
A. By optimizing GPIJ memory utilization for each model's unique para
B. By allocating separate GPUS for each model instance
C. By loading the entire model into G PU memory for efficient processing
D. By sharing base model weights across multiple fine-tuned model's on the same group of GPUs
正解：D
解説: (Topexam メンバーにのみ表示されます)

質問 4：
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 leads to higher latency in model inference.
C. It limits the number of fine-tuned model deployable on the same GPU cluster.
D. It enables the deployment of multiple fine-tuned models.
正解：D
解説: (Topexam メンバーにのみ表示されます)

質問 5：
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.
A. 1:Chain-of-throught, 2: Least-to-most, 3:Step-Back
B. 1:Least-to-most, 2 Chain-of-Thought, 3:Step-Back
C. 1:Step-Back, 2:Chain-of-Thought, 3:Least-to-most
D. 1:Chain-of-Thought ,2:Step-Back, 3:Least-to most
正解：A
解説: (Topexam メンバーにのみ表示されます)

質問 6：
Which statement best describes the role of encoder and decoder models in natural language processing?
A. Encoder models convert a sequence of words into a vector representation, and decoder models take this vector representation to sequence of words.
B. 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.
C. Encoder models and decoder models both convert sequence* of words into vector representations without generating new text.
D. Encoder models are used only for numerical calculations, whereas decoder models are used to interpret the calculated numerical values back into text.
正解：A
解説: (Topexam メンバーにのみ表示されます)

質問 7：
What is the purpose of Retrieval Augmented Generation (RAG) in text generation?
A. To retrieve text from an external source and present it without any modifications
B. To store text in an external database without using it for generation
C. To generate text based only on the model's internal knowledge without external data
D. To generate text using extra information obtained from an external data source
正解：D
解説: (Topexam メンバーにのみ表示されます)