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Oracle 1z0-1122-24 Exam Syllabus Topics:

TopicDetails
Topic 1
  • 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 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 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 AI Foundations: This section covers the fundamentals of AI are essential for understanding its wide-ranging impact and applications.
Topic 5
  • 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 (Q32-Q37):

NEW QUESTION # 32
What is the purpose of Attention Mechanism in Transformer architecture?

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

Answer: B

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 # 33
Which feature is NOT available as part of OCI Speech capabilities?

  • A. Transcribes audio and video files into text
  • B. Provides timestamped, grammatically accurate transcriptions
  • C. Supports multiple languages including English, Spanish, and Portuguese
  • D. Uses extensive data science experience to operate

Answer: D

Explanation:
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.


NEW QUESTION # 34
Which feature of OCI Speech helps make transcriptions easier to read and understand?

  • A. Audio tuning
  • B. Timestamping
  • C. Profanity filtering
  • 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 # 35
In machine learning, what does the term "model training" mean?

  • A. Writing code for the entire program
  • B. Establishing a relationship between input features and output
  • C. Analyzing the accuracy of a trained model
  • D. Performing data analysis on collected and labeled data

Answer: B

Explanation:
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.


NEW QUESTION # 36
What key objective does machine learning strive to achieve?

  • A. Explicitly programming computers
  • B. Improving computer hardware
  • C. Creating algorithms to solve complex problems
  • D. Enabling computers to learn and improve from experience

Answer: D

Explanation:
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.


NEW QUESTION # 37
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