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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Intro to AI Foundations | 10% | - Explain AI vs ML vs DL - Discuss AI Basics - Discuss AI Applications and Types of Data |
| Topic 2: Intro to OCI AI Services | 20% | - OCI Document Understanding - OCI Language - OCI Vision - OCI Speech |
| Topic 3: OCI Generative AI and Oracle 23ai | 10% | - Describe OCI Generative AI Services - Discuss Autonomous Database Select AI - Discuss Oracle Vector Search |
| Topic 4: Intro to ML Foundations | 15% | - Discuss Reinforcement Learning Fundamentals - Explain Machine Learning Basics - Discuss Unsupervised Learning Fundamentals - Discuss Supervised Learning Fundamentals
|
| Topic 5: Get started with OCI AI Portfolio | 15% | - Discuss OCI AI Infrastructure Overview - Discuss OCI ML Services Overview - Discuss OCI AI Services Overview - Explain Responsible AI |
| Topic 6: Intro to Generative AI and LLMs | 15% | - Discuss Generative AI Overview - Explain Transformers Fundamentals - Discuss Large Language Models Fundamentals - Explain LLM Fine Tuning - Explain Prompt Engineering and Instruction Tuning |
| Topic 7: Intro to DL Foundations | 15% | - Explain Convolutional Models (CNN) - Explain Sequence Models (RNN and LSTM) - Discuss Deep Learning Fundamentals |
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NEW QUESTION # 46
You are working on a project for a healthcare organization that wants to develop a system to predict the severity of patients ' illnesses upon admission to a hospital. The goal is to classify patients into three categories - Low Risk, Moderate Risk, and High Risk - based on their medical history and vital signs. Which type of supervised learning algorithm is required in this scenario?
Answer: A
Explanation:
In this healthcare scenario, where the goal is to classify patients into three categories-Low Risk, Moderate Risk, and High Risk-based on their medical history and vital signs, a Multi-Class Classification algorithm is required. Multi-class classification is a type of supervised learning algorithm used when there are three or more classes or categories to predict. This method is well-suited for situations where each instance needs to be classified into one of several categories, which aligns with the requirement to categorize patients into different risk levels.
NEW QUESTION # 47
Which AI domain can be employed for identifying patterns in images and extract relevant features?
Answer: A
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 # 48
Which AI Ethics principle leads to the Responsible AI requirement of transparency?
Answer: D
NEW QUESTION # 49
Which algorithm is primarily used for adjusting the weights of connections between neurons during the training of an Artificial Neural Network (ANN)?
Answer: A
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 # 50
What would you use Oracle AI Vector Search for?
Answer: C
Explanation:
Oracle AI Vector Search is designed to query data based on semantics rather than just keywords. This allows for more nuanced and contextually relevant searches by understanding the meaning behind the words used in a query. Vector search represents data in a high-dimensional vector space, where semantically similar items are placed closer together. This capability makes it particularly powerful for applications such as recommendation systems, natural language processing, and information retrieval where the meaning and context of the data are crucial .
NEW QUESTION # 51
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