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Oracle 1z0-1122-24 Exam Syllabus Topics:
Topic Details
Topic 1 OCI Generative AI and Oracle 23ai: This section covers CI Generative AI Services that are a key component of Oracle's AI offerings, and exploring these services provides a clear understanding of how Oracle supports generative AI applications.
Topic 2 Intro to AI Foundations: This section covers the fundamentals of AI are essential for understanding its wide-ranging impact and applications.
Topic 3 Intro to DL Foundations: This section covers Deep Learning (DL) is a subset of ML that focuses on neural networks with many layers, and understanding its core concepts is vital for working with complex models.
Topic 4 Intro to OCI AI Services: This section is about exploring OCI AI Services and their related APIs, such as those for Language, Vision, Document Understanding, and Speech, which are essential for developers and businesses looking to integrate AI into their operations.
Topic 5 Get Started with OCI AI Portfolio: This section is about the OCI AI Portfolio which offers a comprehensive suite of services and infrastructure for developing and deploying AI models. Exploring the overview of OCI AI Services provides insight into the tools available for AI development.
Topic 6 Intro to Generative AI & LLMs: This section is about covering generative AI which represents a powerful area of AI that involves creating new content or data. Exploring the overview of Generative AI helps in understanding its potential and applications.
Oracle Cloud Infrastructure 2024 AI Foundations Associate Sample Questions (Q13-Q18):
NEW QUESTION # 13
What are Convolutional Neural Networks (CNNs) primarily used for?
A. Time series prediction
B. Image generation
C. Text processing
D. Image classification
Answer: D
Explanation:
Convolutional Neural Networks (CNNs) are primarily used for image classification and other tasks involving spatial data. CNNs are particularly effective at recognizing patterns in images due to their ability to detect features such as edges, textures, and shapes across multiple layers of convolutional filters. This makes them the model of choice for tasks such as object recognition, image segmentation, and facial recognition.
CNNs are also used in other domains like video analysis and medical image processing, but their primary application remains in image classification.
NEW QUESTION # 14
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. Doubling the neural network layers
D. Upgrading the hardware of the AI clusters
Answer: B
Explanation:
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.
NEW QUESTION # 15
Which type of machine learning is used to understand relationships within data and is not focused on making predictions or classifications?
A. Active learning
B. Unsupervised learning
C. Reinforcement learning
D. Supervised learning
Answer: B
Explanation:
Unsupervised learning is a type of machine learning that focuses on understanding relationships within data without the need for labeled outcomes. Unlike supervised learning, which requires labeled data to train models to make predictions or classifications, unsupervised learning works with unlabeled data and aims to discover hidden patterns, groupings, or structures within the data.
Common applications of unsupervised learning include clustering, where the algorithm groups data points into clusters based on similarities, and association, where it identifies relationships between variables in the dataset. Since unsupervised learning does not predict outcomes but rather uncovers inherent structures, it is ideal for exploratory data analysis and discovering previously unknown patterns in data .
NEW QUESTION # 16
What would you use Oracle AI Vector Search for?
A. Manage database security protocols.
B. Store business data in a cloud database.
C. Query data based on semantics.
D. Query data based on keywords.
Answer: C
Explanation:
Oracle AI Vector Search is designed to query data based on semantics rather than just keywords. This allows for more nuanced and contextually relevant searches by understanding the meaning behind the words used in a query. Vector search represents data in a high-dimensional vector space, where semantically similar items are placed closer together. This capability makes it particularly powerful for applications such as recommendation systems, natural language processing, and information retrieval where the meaning and context of the data are crucial .
NEW QUESTION # 17
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. AI is a subset of DL, which is a subset of ML.
C. DL is a subset of AI, and ML is a subset of DL.
D. ML is a subset of AI, and DL is a subset of ML.
Answer: D
Explanation:
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.
NEW QUESTION # 18
......
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One way to makes yourself competitive is to pass the 1z0-1122-24 certification exams. Hence, if you need help to get certified, you are in the right place. ExamDiscuss offers the most comprehensive and updated braindumps for Oracleโs certifications. To ensure that our products are of the highest quality, we have tapped the services of Oracle experts to review and evaluate our 1z0-1122-24 Certification test materials. In fact, we continuously provide updates to every customer to ensure that our 1z0-1122-24 products can cope with the fast changing trends in 1z0-1122-24 certification programs.
>> 1z0-1122-24 Testking Learning Materials <<
Oracle 1z0-1122-24 Trustworthy Dumps - 1z0-1122-24 Braindump Free
As is known to all, practice makes perfect. This proverb also can be replied into the exam. We have the 1z0-1122-24 Study Materials with good reputation in the market. The 1z0-1122-24 exam dumps not only contains the quality, but also have the quantity, therefore it will meet your needs. Just think that you just need to practice it for some time, a certificate will be obtained by your own efforts, it will be a quite delightful thing. So act now, you will be very happy to see it come true.
Oracle 1z0-1122-24 Exam Syllabus Topics:
Topic Details
Topic 1 OCI Generative AI and Oracle 23ai: This section covers CI Generative AI Services that are a key component of Oracle's AI offerings, and exploring these services provides a clear understanding of how Oracle supports generative AI applications.
Topic 2 Intro to AI Foundations: This section covers the fundamentals of AI are essential for understanding its wide-ranging impact and applications.
Topic 3 Intro to DL Foundations: This section covers Deep Learning (DL) is a subset of ML that focuses on neural networks with many layers, and understanding its core concepts is vital for working with complex models.
Topic 4 Intro to OCI AI Services: This section is about exploring OCI AI Services and their related APIs, such as those for Language, Vision, Document Understanding, and Speech, which are essential for developers and businesses looking to integrate AI into their operations.
Topic 5 Get Started with OCI AI Portfolio: This section is about the OCI AI Portfolio which offers a comprehensive suite of services and infrastructure for developing and deploying AI models. Exploring the overview of OCI AI Services provides insight into the tools available for AI development.
Topic 6 Intro to Generative AI & LLMs: This section is about covering generative AI which represents a powerful area of AI that involves creating new content or data. Exploring the overview of Generative AI helps in understanding its potential and applications.
Oracle Cloud Infrastructure 2024 AI Foundations Associate Sample Questions (Q13-Q18):
NEW QUESTION # 13
What are Convolutional Neural Networks (CNNs) primarily used for?
A. Time series prediction
B. Image generation
C. Text processing
D. Image classification
Answer: D
Explanation:
Convolutional Neural Networks (CNNs) are primarily used for image classification and other tasks involving spatial data. CNNs are particularly effective at recognizing patterns in images due to their ability to detect features such as edges, textures, and shapes across multiple layers of convolutional filters. This makes them the model of choice for tasks such as object recognition, image segmentation, and facial recognition.
CNNs are also used in other domains like video analysis and medical image processing, but their primary application remains in image classification.
NEW QUESTION # 14
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. Doubling the neural network layers
D. Upgrading the hardware of the AI clusters
Answer: B
Explanation:
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.
NEW QUESTION # 15
Which type of machine learning is used to understand relationships within data and is not focused on making predictions or classifications?
A. Active learning
B. Unsupervised learning
C. Reinforcement learning
D. Supervised learning
Answer: B
Explanation:
Unsupervised learning is a type of machine learning that focuses on understanding relationships within data without the need for labeled outcomes. Unlike supervised learning, which requires labeled data to train models to make predictions or classifications, unsupervised learning works with unlabeled data and aims to discover hidden patterns, groupings, or structures within the data.
Common applications of unsupervised learning include clustering, where the algorithm groups data points into clusters based on similarities, and association, where it identifies relationships between variables in the dataset. Since unsupervised learning does not predict outcomes but rather uncovers inherent structures, it is ideal for exploratory data analysis and discovering previously unknown patterns in data .
NEW QUESTION # 16
What would you use Oracle AI Vector Search for?
A. Manage database security protocols.
B. Store business data in a cloud database.
C. Query data based on semantics.
D. Query data based on keywords.
Answer: C
Explanation:
Oracle AI Vector Search is designed to query data based on semantics rather than just keywords. This allows for more nuanced and contextually relevant searches by understanding the meaning behind the words used in a query. Vector search represents data in a high-dimensional vector space, where semantically similar items are placed closer together. This capability makes it particularly powerful for applications such as recommendation systems, natural language processing, and information retrieval where the meaning and context of the data are crucial .
NEW QUESTION # 17
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. AI is a subset of DL, which is a subset of ML.
C. DL is a subset of AI, and ML is a subset of DL.
D. ML is a subset of AI, and DL is a subset of ML.
Answer: D
Explanation:
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.
NEW QUESTION # 18
......
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