合格させるOracle 1z0-1122-24にはJPNTest提供の試験問題集で2024年10月更新
完全版最新の1z0-1122-24問題集、100%カバー率問題と解答があなたをリアル試験で合格させる
質問 # 14
What key objective does machine learning strive to achieve?
- A. Explicitly programming computers
- B. Creating algorithms to solve complex problems
- C. Enabling computers to learn and improve from experience
- D. Improving computer hardware
正解：C
解説：
The key objective of machine learning is to enable computers to learn from experience and improve their performance on specific tasks over time. This is achieved through the development of algorithms that can learn patterns from data and make decisions or predictions without being explicitly programmed for each task. As the model processes more data, it becomes better at understanding the underlying patterns and relationships, leading to more accurate and efficient outcomes.
質問 # 15
Which statement best describes the relationship between Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL)?
- A. AI, ML, and DL are entirely separate fields with no overlap.
- B. DL is a subset of AI, and ML is a subset of DL.
- C. ML is a subset of AI, and DL is a subset of ML.
- D. AI is a subset of DL, which is a subset of ML.
正解：C
解説：
Artificial Intelligence (AI) is the broadest field encompassing all technologies that enable machines to perform tasks that typically require human intelligence. Within AI, Machine Learning (ML) is a subset focused on the development of algorithms that allow systems to learn from and make predictions or decisions based on data. Deep Learning (DL) is a further subset of ML, characterized by the use of artificial neural networks with many layers (hence "deep").
In this hierarchy:
AI includes all methods to make machines intelligent.
ML refers to the methods within AI that focus on learning from data.
DL is a specialized field within ML that deals with deep neural networks.
質問 # 16
What distinguishes Generative AI from other types of AI?
- A. Generative AI creates diverse content such as text, audio, and images by learning patterns from existing data.
- B. Generative AI uses algorithms to predict outcomes based on past data.
- C. Generative AI involves training models to perform tasks without human intervention.
- D. Generative AI focuses on making decisions based on user interactions.
正解：A
解説：
Generative AI is distinct from other types of AI in that it focuses on creating new content by learning patterns from existing data. This includes generating text, images, audio, and other types of media. Unlike AI that primarily analyzes data to make decisions or predictions, Generative AI actively creates new and original outputs. This ability to generate diverse content is a hallmark of Generative AI models like GPT-4, which can produce human-like text, create images, and even compose music based on the patterns they have learned from their training data.
質問 # 17
You are working on a multilingual public announcement system. Which AI task will you use to implement it?
- A. Audio recording
- B. Speech recognition
- C. Text to speech
- D. Text summarization
正解：C
解説：
For a multilingual public announcement system, the AI task that would be most relevant is "Text to Speech" (TTS). This task involves converting written text into spoken words, which can then be broadcasted over public address systems in multiple languages.
Text to Speech technology is crucial for creating accessible and understandable announcements in different languages, especially in environments like airports, train stations, or public events where clear verbal communication is essential. The TTS system would be configured to support multiple languages, allowing it to deliver announcements to diverse audiences effectively .
質問 # 18
Which feature of OCI Speech helps make transcriptions easier to read and understand?
- A. Profanity filtering
- B. Text normalization
- C. Audio tuning
- D. Timestamping
正解：B
解説：
The text normalization feature of OCI Speech helps make transcriptions easier to read and understand by converting spoken language into a more standardized and grammatically correct format. This process includes correcting grammar, punctuation, and formatting, ensuring that the transcribed text is clear, accurate, and suitable for various use cases. Text normalization enhances the usability of transcriptions, making them more accessible and easier to process in downstream applications.
Top of Form
Bottom of Form
質問 # 19
What is the key feature of Recurrent Neural Networks (RNNs)?
- A. They have a feedback loop that allows information to persist across different time steps.
- B. They are primarily used for image recognition tasks.
- C. They process data in parallel.
- D. They do not have an internal state.
正解：A
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 # 20
What is the primary benefit of using Oracle Cloud Infrastructure Supercluster for AI workloads?
- A. It offers seamless integration with social media platforms.
- B. It provides a cost-effective solution for simple AI tasks.
- C. It delivers exceptional performance and scalability for complex AI tasks.
- D. It is ideal for tasks such as text-to-speech conversion.
正解：C
解説：
Oracle Cloud Infrastructure Supercluster is designed to deliver exceptional performance and scalability for complex AI tasks. The primary benefit of this infrastructure is its ability to handle demanding AI workloads, offering high-performance computing (HPC) capabilities that are crucial for training large-scale AI models and processing massive datasets. The architecture of the Supercluster ensures low-latency networking, efficient resource allocation, and high-throughput processing, making it ideal for AI tasks that require significant computational power, such as deep learning, data analytics, and large-scale simulations.
質問 # 21
What is a key advantage of using dedicated AI clusters in the OCI Generative AI service?
- A. They provide high performance compute resources for fine-tuning tasks.
- B. They are free of charge for all users.
- C. They provide faster internet connection speeds.
- D. They allow access to unlimited database resources.
正解：A
解説：
The primary advantage of using dedicated AI clusters in the Oracle Cloud Infrastructure (OCI) Generative AI service is the provision of high-performance compute resources that are specifically optimized for fine-tuning tasks. Fine-tuning is a critical step in the process of adapting pre-trained models to specific tasks, and it requires significant computational power. Dedicated AI clusters in OCI are designed to deliver the necessary performance and scalability to handle the intense workloads associated with fine-tuning large language models (LLMs) and other AI models, ensuring faster processing and more efficient training.
質問 # 22
How does Oracle Cloud Infrastructure Document Understanding service facilitate business processes?
- A. By generating lifelike speech from documents
- B. By analyzing sentiment in text documents
- C. By transcribing spoken language
- D. By automating data extraction from documents
正解：D
解説：
Oracle Cloud Infrastructure (OCI) Document Understanding service facilitates business processes by automating data extraction from documents. This service leverages machine learning to identify, classify, and extract relevant information from various document types, reducing the need for manual data entry and improving efficiency in document processing workflows. Automation of these tasks enables organizations to streamline operations and reduce errors associated with manual data handling.
質問 # 23
What is the main function of the hidden layers in an Artificial Neural Network (ANN) when recognizing handwritten digits?
- A. Providing labels for the output neurons
- B. Capturing the internal representation of the raw image data
- C. Directly predicting the final output
- D. Storing the input pixel values
正解：B
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 # 24
Which capability is supported by Oracle Cloud Infrastructure Language service?
- A. Translating text into speech
- B. Converting text into images
- C. Analyzing text to extract structured information like sentiment or entities
- D. Detecting objects and scenes in images
正解：C
解説：
Oracle Cloud Infrastructure (OCI) Language service is specifically designed to analyze text and extract structured information such as sentiment, entities, key phrases, and language detection. This service provides natural language processing (NLP) capabilities that help users gain insights from unstructured text data. By identifying the sentiment (positive, negative, neutral) and recognizing entities (like names, dates, or places), the service enables businesses to process large volumes of text data efficiently, aiding in decision-making processes.
質問 # 25
What is the purpose of the model catalog in OCI Data Science?
- A. To create and switch between different environments
- B. To provide a preinstalled open source library
- C. To deploy models as HTTP endpoints
- D. To store, track, share, and manage models
正解：D
解説：
The primary purpose of the model catalog in OCI Data Science is to store, track, share, and manage machine learning models. This functionality is essential for maintaining an organized repository where data scientists and developers can collaborate on models, monitor their performance, and manage their lifecycle. The model catalog also facilitates model versioning, ensuring that the most recent and effective models are available for deployment. This capability is crucial in a collaborative environment where multiple stakeholders need access to the latest model versions for testing, evaluation, and deployment.
質問 # 26
You are part of the medical transcription team and need to automate transcription tasks. Which OCI AI service are you most likely to use?
- A. Vision
- B. Speech
- C. Document Understanding
- D. Language
正解：B
解説：
For automating transcription tasks in a medical transcription team, the most appropriate OCI AI service to use would be the "Speech" service. This service is designed to convert spoken language into text, which is essential for transcribing spoken medical reports or consultations into written form. The OCI Speech service provides capabilities such as speech-to-text conversion, which is specifically tailored for handling audio input and producing accurate transcriptions.
質問 # 27
Which feature is NOT available as part of OCI Speech capabilities?
- A. Provides timestamped, grammatically accurate transcriptions
- B. Supports multiple languages including English, Spanish, and Portuguese
- C. Uses extensive data science experience to operate
- D. Transcribes audio and video files into text
正解：C
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 # 28
What feature of OCI Data Science provides an interactive coding environment for building and training models?
- A. Conda environment
- B. Accelerated Data Science (ADS) SDK
- C. Model catalog
- D. Notebook sessions
正解：D
解説：
In OCI Data Science, Notebook sessions provide an interactive coding environment that is essential for building, training, and deploying machine learning models. These sessions allow data scientists to write and execute code in real time, offering a flexible environment for data exploration, model experimentation, and iterative development. The integration with various OCI services and support for popular machine learning frameworks further enhances the utility of Notebook sessions, making them a crucial tool in the data science workflow.
質問 # 29
What does "fine-tuning" refer to in the context of OCI Generative AI service?
- A. Encrypting the data for security reasons
- B. Adjusting the model parameters to improve accuracy
- C. Upgrading the hardware of the AI clusters
- D. Doubling the neural network layers
正解：B
解説：
Fine-tuning in the context of the OCI Generative AI service refers to the process of adjusting the parameters of a pretrained model to better fit a specific task or dataset. This process involves further training the model on a smaller, task-specific dataset, allowing the model to refine its understanding and improve its performance on that specific task. Fine-tuning is essential for customizing the general capabilities of a pretrained model to meet the particular needs of a given application, resulting in more accurate and relevant outputs. It is distinct from other processes like encrypting data, upgrading hardware, or simply increasing the complexity of the model architecture.
質問 # 30
You are working on a project for a healthcare organization that wants to develop a system to predict the severity of patients' illnesses upon admission to a hospital. The goal is to classify patients into three categories - Low Risk, Moderate Risk, and High Risk - based on their medical history and vital signs. Which type of supervised learning algorithm is required in this scenario?
- A. Binary Classification
- B. Multi-Class Classification
- C. Regression
- D. Clustering
正解：B
解説：
In this healthcare scenario, where the goal is to classify patients into three categories-Low Risk, Moderate Risk, and High Risk-based on their medical history and vital signs, a Multi-Class Classification algorithm is required. Multi-class classification is a type of supervised learning algorithm used when there are three or more classes or categories to predict. This method is well-suited for situations where each instance needs to be classified into one of several categories, which aligns with the requirement to categorize patients into different risk levels.
質問 # 31
......
Oracle 1z0-1122-24 認定試験の出題範囲：
| トピック | 出題範囲 |
|---|---|
| トピック 1 |
|
| トピック 2 |
|
| トピック 3 |
|
最新1z0-1122-24試験問題集有効で最新の問題集：https://www.jpntest.com/shiken/1z0-1122-24-mondaishu
検証済み1z0-1122-24試験解答合格確定させる：https://drive.google.com/open?id=1boa7kpj6Wtp0Drf7TqthtW_HjsWPdZzA