1Z0-1122-24 STUDY MATERIALS - VALID 1Z0-1122-24 TEST PAPERS

1z0-1122-24 Study Materials - Valid 1z0-1122-24 Test Papers

1z0-1122-24 Study Materials - Valid 1z0-1122-24 Test Papers

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Oracle 1z0-1122-24 Exam Syllabus Topics:

TopicDetails
Topic 1
  • 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 2
  • 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 3
  • Intro to AI Foundations: This section covers the fundamentals of AI are essential for understanding its wide-ranging impact and applications.
Topic 4
  • 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 5
  • 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.

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Oracle Cloud Infrastructure 2024 AI Foundations Associate Sample Questions (Q39-Q44):

NEW QUESTION # 39
What is a key advantage of using dedicated AI clusters in the OCI Generative AI service?

  • A. They allow access to unlimited database resources.
  • B. They are free of charge for all users.
  • C. They provide high performance compute resources for fine-tuning tasks.
  • D. They provide faster internet connection speeds.

Answer: C

Explanation:
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.


NEW QUESTION # 40
Which feature is NOT available as part of OCI Speech capabilities?

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

Answer: A

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 # 41
What is the main function of the hidden layers in an Artificial Neural Network (ANN) when recognizing handwritten digits?

  • A. Capturing the internal representation of the raw image data
  • B. Storing the input pixel values
  • C. Directly predicting the final output
  • D. Providing labels for the output neurons

Answer: A

Explanation:
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.


NEW QUESTION # 42
Which statement best describes the relationship between Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL)?

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

Answer: A

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 # 43
What is the purpose of Attention Mechanism in Transformer architecture?

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

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 # 44
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