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| Section | Objectives |
|---|---|
| Topic 1: Generative AI and Large Language Models | - Large Language Models
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| Topic 2: Oracle AI and Machine Learning Services | - OCI Machine Learning Services
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| Topic 3: OCI Generative AI and Oracle Database AI Capabilities | - AI Application Frameworks
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| Topic 4: Artificial Intelligence and Machine Learning Fundamentals | - Artificial Intelligence Concepts
|
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質問 # 12
In machine learning, what does the term " model training " mean?
正解：C
解説：
In machine learning, " model training " refers to the process of teaching a model to make predictions or decisions by learning the relationships between input features and the corresponding output. During training, the model is fed a large dataset where the inputs are paired with known outputs (labels). The model adjusts its internal parameters to minimize the error between its predictions and the actual outputs. Over time, the model learns to generalize from the training data to make accurate predictions on new, unseen data.
質問 # 13
Which feature is NOT available as part of OCI Speech capabilities?
正解：A
解説：
OCI Speech capabilities are designed to be user-friendly and do not require extensive data science experience to operate. The service provides features such as transcribing audio and video files into text, offering grammatically accurate transcriptions, supporting multiple languages, and providing timestamped outputs.
These capabilities are built to be accessible to a broad range of users, making speech-to-text conversion seamless and straightforward without the need for deep technical expertise.
質問 # 14
What is the key feature of Recurrent Neural Networks (RNNs)?
正解：B
解説：
Recurrent Neural Networks (RNNs) are a class of neural networks where connections between nodes can form cycles. This cycle creates a feedback loop that allows the network to maintain an internal state or memory, which persists across different time steps. This is the key feature of RNNs that distinguishes them from other neural networks, such as feedforward neural networks that process inputs in one direction only and do not have internal states.
RNNs are particularly useful for tasks where context or sequential information is important, such as in language modeling, time-series prediction, and speech recognition. The ability to retain information from previous inputs enables RNNs to make more informed predictions based on the entire sequence of data, not just the current input.
In contrast:
* Option A (They process data in parallel) is incorrect because RNNs typically process data sequentially, not in parallel.
* Option B (They are primarily used for image recognition tasks) is incorrect because image recognition is more commonly associated with Convolutional Neural Networks (CNNs), not RNNs.
* Option D (They do not have an internal state) is incorrect because having an internal state is a defining characteristic of RNNs.
This feedback loop is fundamental to the operation of RNNs and allows them to handle sequences of data effectively by " remembering " past inputs to influence future outputs. This memory capability is what makes RNNs powerful for applications that involve sequential or time-dependent data.
質問 # 15
What is the main function of the hidden layers in an Artificial Neural Network (ANN) when recognizing handwritten digits?
正解：C
解説：
In an Artificial Neural Network (ANN) designed for recognizing handwritten digits, the hidden layers serve the crucial function of capturing the internal representation of the raw image data. These layers learn to extract and represent features such as edges, shapes, and textures from the input pixels, which are essential for distinguishing between different digits. By transforming the input data through multiple hidden layers, the network gradually abstracts the raw pixel data into higher-level representations, which are more informative and easier to classify into the correct digit categories.
質問 # 16
Emma is developing a customer support chatbot for an e-commerce website. The chatbot needs to provide accurate and up-to-date return policies, which change frequently. She initially tries fine-tuning but finds that the model still uses outdated information. Which approach should Emma use instead?
正解：C
解説：
Retrieval-Augmented Generation is the appropriate approach when a chatbot must answer using information that changes frequently. Oracle defines RAG as a technique that retrieves information from specific external data sources and augments an LLM ' s response with that retrieved context, producing grounded answers.
Oracle Docs Oracle further explains that RAG can incorporate information that is more current than the model
' s original training data and that knowledge repositories can be continually updated without retraining the underlying LLM. Oracle Docs Fine-tuning is better suited to adapting model behavior or specialization, not continuously changing factual information. Prompt engineering and zero-shot prompting control how instructions are presented but do not independently supply current return-policy data. Therefore, RAG is the correct solution for providing accurate, current, organization-specific policy responses.
質問 # 17
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1z0-1122-26認定試験を受験したいですか。1z0-1122-26認証資格を取得したいですか。試験に準備する時間が足りないあなたは、どうやって試験に合格できますか。しようがないわけではないです。短時間の準備でも楽に試験に合格することができるようになりますよ。それでは、どのようにすればそれを達成できますか。実は方法がとても簡単です。すなわちShikenPASSの1z0-1122-26問題集を利用して試験の準備をすることです。
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