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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: OCI Generative AI and Oracle 23ai | 10% | - Discuss Autonomous Database Select AI - Discuss Oracle Vector Search - Describe OCI Generative AI Services |
| Topic 2: Intro to Generative AI and LLMs | 15% | - Discuss Large Language Models Fundamentals - Explain LLM Fine Tuning - Explain Transformers Fundamentals - Explain Prompt Engineering and Instruction Tuning - Discuss Generative AI Overview |
| Topic 3: Get started with OCI AI Portfolio | 15% | - Explain Responsible AI - Discuss OCI AI Services Overview - Discuss OCI ML Services Overview - Discuss OCI AI Infrastructure Overview |
| Topic 4: Intro to AI Foundations | 10% | - Discuss AI Applications and Types of Data - Explain AI vs ML vs DL - Discuss AI Basics |
| Topic 5: Intro to ML Foundations | 15% | - Explain Machine Learning Basics - Discuss Unsupervised Learning Fundamentals - Discuss Supervised Learning Fundamentals
|
| Topic 6: Intro to DL Foundations | 15% | - Discuss Deep Learning Fundamentals - Explain Sequence Models (RNN and LSTM) - Explain Convolutional Models (CNN) |
| Topic 7: Intro to OCI AI Services | 20% | - OCI Document Understanding - OCI Vision - OCI Speech - OCI Language |
>> New Oracle 1z0-1122-26 Test Topics <<
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NEW QUESTION # 19
What is the primary benefit of using Oracle Cloud Infrastructure Supercluster for AI workloads?
Answer: A
NEW QUESTION # 20
Which AI Ethics principle leads to the Responsible AI requirement of transparency?
Answer: B
NEW QUESTION # 21
You are part of the medical transcription team and need to automate transcription tasks. Which OCI AI service are you most likely to use?
Answer: D
Explanation:
For automating transcription tasks in a medical transcription team, the most appropriate OCI AI service to use would be the " Speech " service. This service is designed to convert spoken language into text, which is essential for transcribing spoken medical reports or consultations into written form. The OCI Speech service provides capabilities such as speech-to-text conversion, which is specifically tailored for handling audio input and producing accurate transcriptions.
NEW QUESTION # 22
What is the difference between classification and regression in Supervised Machine Learning?
Answer: B
Explanation:
In supervised machine learning, the key difference between classification and regression lies in the nature of the output they predict. Classification algorithms are used to assign data points to one of several predefined categories or classes, making it suitable for tasks like spam detection, where an email is classified as either " spam " or " not spam. " On the other hand, regression algorithms predict continuous values, such as forecasting the price of a house based on features like size, location, and number of rooms. While classification answers " which category? " regression answers " how much? " or " what value? " .
NEW QUESTION # 23
What key objective does machine learning strive to achieve?
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 # 24
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