1Z0-1122-25 INTERACTIVE COURSE - RELIABLE 1Z0-1122-25 TEST PASS4SURE

1Z0-1122-25 Interactive Course - Reliable 1Z0-1122-25 Test Pass4sure

1Z0-1122-25 Interactive Course - Reliable 1Z0-1122-25 Test Pass4sure

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Free PDF Oracle - 1Z0-1122-25 - Oracle Cloud Infrastructure 2025 AI Foundations Associate Latest Interactive Course

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Oracle 1Z0-1122-25 Exam Syllabus Topics:

TopicDetails
Topic 1
  • Intro to AI Foundations: This section of the exam measures the skills of AI Practitioners and Data Analysts in understanding the fundamentals of artificial intelligence. It covers key concepts, AI applications across industries, and the types of data used in AI models. It also explains the differences between artificial intelligence, machine learning, and deep learning, providing clarity on how these technologies interact and complement each other.
Topic 2
  • Intro to Generative AI & LLMs: This section tests the abilities of AI Developers to understand generative AI and large language models. It introduces the principles of generative AI, explains the fundamentals of large language models (LLMs), and discusses the core workings of transformers, prompt engineering, instruction tuning, and LLM fine-tuning for optimizing AI-generated content.
Topic 3
  • Intro to OCI AI Services: This section tests the expertise of AI Solutions Engineers in working with OCI AI services and related APIs. It provides insights into key AI services such as language processing, computer vision, document understanding, and speech recognition, allowing professionals to leverage Oracle’s AI ecosystem for building intelligent applications.

Oracle Cloud Infrastructure 2025 AI Foundations Associate Sample Questions (Q21-Q26):

NEW QUESTION # 21
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. Uses extensive data science experience to operate
  • D. Supports multiple languages including English, Spanish, and Portuguese

Answer: C

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 # 22
What is the primary benefit of using Oracle Cloud Infrastructure Supercluster for AI workloads?

  • A. It provides a cost-effective solution for simple AI tasks.
  • B. It offers seamless integration with social media platforms.
  • C. It delivers exceptional performance and scalability for complex AI tasks.
  • D. It is ideal for tasks such as text-to-speech conversion.

Answer: C

Explanation:
Oracle Cloud Infrastructure Supercluster is designed to deliver exceptional performance and scalability for complex AI tasks. The primary benefit of this infrastructure is its ability to handle demanding AI workloads, offering high-performance computing (HPC) capabilities that are crucial for training large-scale AI models and processing massive datasets. The architecture of the Supercluster ensures low-latency networking, efficient resource allocation, and high-throughput processing, making it ideal for AI tasks that require significant computational power, such as deep learning, data analytics, and large-scale simulations.


NEW QUESTION # 23
Which algorithm is primarily used for adjusting the weights of connections between neurons during the training of an Artificial Neural Network (ANN)?

  • A. Gradient Descent
  • B. Random Forest
  • C. Support Vector Machine
  • D. Backpropagation

Answer: D

Explanation:
Backpropagation is the algorithm primarily used for adjusting the weights of connections between neurons during the training of an Artificial Neural Network (ANN). It is a supervised learning algorithm that calculates the gradient of the loss function with respect to each weight by applying the chain rule, propagating the error backward from the output layer to the input layer. This process updates the weights to minimize the error, thus improving the model's accuracy over time.
Gradient Descent is closely related as it is the optimization algorithm used to adjust the weights based on the gradients computed by backpropagation, but backpropagation is the specific method used to calculate these gradients.


NEW QUESTION # 24
How is "Prompt Engineering" different from "Fine-tuning" in the context of Large Language Models (LLMs)?

  • A. Prompt Engineering modifies training data, while Fine-tuning alters the model's structure.
  • B. Prompt Engineering adjusts the model's parameters, while Fine-tuning crafts input prompts.
  • C. Both involve retraining the model, but Prompt Engineering does it more often.
  • D. Prompt Engineering creates input prompts, while Fine-tuning retrains the model on specific data.

Answer: D

Explanation:
In the context of Large Language Models (LLMs), Prompt Engineering and Fine-tuning are two distinct methods used to optimize the performance of AI models.
Prompt Engineering involves designing and structuring input prompts to guide the model in generating specific, relevant, and high-quality responses. This technique does not alter the model's internal parameters but instead leverages the existing capabilities of the model by crafting precise and effective prompts. The focus here is on optimizing how you ask the model to perform tasks, which can involve specifying the context, formatting the input, and iterating on the prompt to improve outputs .
Fine-tuning, on the other hand, refers to the process of retraining a pretrained model on a smaller, task-specific dataset. This adjustment allows the model to adapt its parameters to better suit the specific needs of the task at hand, effectively "specializing" the model for particular applications. Fine-tuning involves modifying the internal structure of the model to improve its accuracy and performance on the targeted tasks .
Thus, the key difference is that Prompt Engineering focuses on how to use the model effectively through input manipulation, while Fine-tuning involves altering the model itself to improve its performance on specialized tasks.


NEW QUESTION # 25
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. 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 # 26
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