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Oracle 1z0-1122-24 Exam Syllabus Topics:

TopicDetails
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
  • 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 3
  • 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 4
  • 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.

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Oracle Cloud Infrastructure 2024 AI Foundations Associate Sample Questions (Q16-Q21):

NEW QUESTION # 16
Which AI domain can be employed for identifying patterns in images and extract relevant features?

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

Answer: C

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 # 17
Which capability is supported by Oracle Cloud Infrastructure Language service?

  • A. Detecting objects and scenes in images
  • B. Converting text into images
  • C. Analyzing text to extract structured information like sentiment or entities
  • D. Translating text into speech

Answer: C

Explanation:
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.


NEW QUESTION # 18
What is the primary benefit of using the OCI Language service for text analysis?

  • A. It requires extensive machine learning expertise to use.
  • B. It allows for text analysis at scale without machine learning expertise.
  • C. It provides image processing capabilities.
  • D. It only works with structured data.

Answer: B

Explanation:
The primary benefit of using the OCI Language service for text analysis is its ability to scale text analysis without requiring users to have extensive machine learning expertise. The service abstracts the complexities of machine learning, allowing businesses to easily process and analyze large amounts of text data through pre-built models. This accessibility makes it possible for a broader range of users to leverage advanced text analysis capabilities, facilitating insights from textual data without needing to develop and train models from scratch.


NEW QUESTION # 19
Which AI Ethics principle leads to the Responsible AI requirement of transparency?

  • A. Respect for human autonomy
  • B. Prevention of harm
  • C. Fairness
  • D. Explicability

Answer: D


NEW QUESTION # 20
What is the purpose of Attention Mechanism in Transformer architecture?

  • A. Convert tokens into numerical forms (vectors) that the model can understand.
  • B. Break down a sentence into smaller pieces called tokens.
  • C. Weigh the importance of different words within a sequence and understand the context.
  • D. Apply a specific function to each word individually.

Answer: C

Explanation:
The purpose of the Attention Mechanism in Transformer architecture is to weigh the importance of different words within a sequence and understand the context. In essence, the attention mechanism allows the model to focus on specific parts of the input sequence when producing an output, which is crucial for understanding context and maintaining coherence over long sequences. It does this by assigning different weights to different words in the sequence, enabling the model to capture relationships between words that are far apart and to emphasize relevant parts of the input when generating predictions.
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NEW QUESTION # 21
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