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Oracle Cloud Infrastructure 2024 AI Foundations Associate pass guide: latest 1z0-1122-24 exam prep collection

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Oracle 1z0-1122-24 Exam Syllabus Topics:

Topic
Details

Topic 1

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 2

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 3

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 4

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 5

Intro to AI Foundations: This section covers the fundamentals of AI are essential for understanding its wide-ranging impact and applications.

Topic 6

Intro to ML Foundations: This section covers Machine Learning (ML) which is a critical area within AI, and understanding its fundamentals is crucial for anyone interested in this field. The section covers delving into the basics of ML allowing for a better grasp of how machines learn from data.

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Oracle Cloud Infrastructure 2024 AI Foundations Associate Sample Questions (Q15-Q20):

NEW QUESTION # 15
Which AI domain can be employed for identifying patterns in images and extract relevant features?

A. Anomaly Detection
B. Natural Language Processing
C. Speech Processing
D. Computer Vision

Answer: D

Explanation:
Computer Vision is the AI domain specifically employed for identifying patterns in images and extracting relevant features. This field focuses on enabling machines to interpret and understand visual information from the world, automating tasks that the human visual system can perform, such as recognizing objects, analyzing scenes, and detecting anomalies. Techniques in Computer Vision are widely used in applications ranging from facial recognition and image classification to medical image analysis and autonomous vehicles.

NEW QUESTION # 16
What is "in-context learning" in the realm of Large Language Models (LLMs)?

A. Teaching a model through zero-shot learning
B. Training a model on a diverse range of tasks
C. Providing a few examples of a target task via the input prompt
D. Modifying the behavior of a pretrained LLM permanently

Answer: C

Explanation:
"In-context learning" in the realm of Large Language Models (LLMs) refers to the ability of these models to learn and adapt to a specific task by being provided with a few examples of that task within the input prompt. This approach allows the model to understand the desired pattern or structure from the given examples and apply it to generate the correct outputs for new, similar inputs. In-context learning is powerful because it does not require retraining the model; instead, it uses the examples provided within the context of the interaction to guide its behavior.

NEW QUESTION # 17
Which statement best describes the relationship between Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL)?

A. AI is a subset of DL, which is a subset of ML.
B. AI, ML, and DL are entirely separate fields with no overlap.
C. ML is a subset of AI, and DL is a subset of ML.
D. DL is a subset of AI, and ML is a subset of DL.

Answer: C

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
Which feature of OCI Speech helps make transcriptions easier to read and understand?

A. Profanity filtering
B. Audio tuning
C. Timestamping
D. Text normalization

Answer: D

Explanation:
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.
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NEW QUESTION # 19
How does AI enhance human efforts?

A. By deleting data humans need to handle
B. By increasing the physical strength of humans
C. By completely replacing human workers in all tasks
D. By processing data at a speed and effectiveness far beyond human capability

Answer: D

Explanation:
AI enhances human efforts by processing large volumes of data quickly and accurately, performing complex computations that would be time-consuming or impossible for humans to handle manually. This allows humans to focus on more strategic, creative, and decision-making tasks, leveraging AI's ability to provide insights, automate repetitive processes, and support decision-making. AI does not physically enhance human capabilities, nor does it replace human workers in all tasks. Instead, it serves as an augmentation tool, amplifying human productivity and capabilities.

NEW QUESTION # 20
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