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| Section | Weight | Objectives |
|---|---|---|
| OCI Generative AI and Oracle 23ai | 10% | - Describe OCI Generative AI Services - Discuss Autonomous Database Select AI - Discuss Oracle Vector Search |
| Intro to Generative AI and LLMs | 15% | - Discuss Large Language Models Fundamentals - Explain Prompt Engineering and Instruction Tuning - Discuss Generative AI Overview - Explain Transformers Fundamentals - Explain LLM Fine Tuning |
| Intro to OCI AI Services | 20% | - OCI Speech - OCI Language - OCI Document Understanding - OCI Vision |
| Intro to AI Foundations | 10% | - Discuss AI Basics - Discuss AI Applications and Types of Data - Explain AI vs ML vs DL |
| Intro to ML Foundations | 15% | - Explain Machine Learning Basics - Discuss Reinforcement Learning Fundamentals - Discuss Supervised Learning Fundamentals
|
| Intro to DL Foundations | 15% | - Explain Sequence Models (RNN and LSTM) - Explain Convolutional Models (CNN) - Discuss Deep Learning Fundamentals |
| Get started with OCI AI Portfolio | 15% | - Explain Responsible AI - Discuss OCI AI Infrastructure Overview - Discuss OCI ML Services Overview - Discuss OCI AI Services Overview |
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NEW QUESTION # 37
What is the main function of the hidden layers in an Artificial Neural Network (ANN) when recognizing handwritten digits?
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 # 38
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: B
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 # 39
You are training a logistic regression model to classify emails as spam or not spam. The model is currently classifying too many emails as spam. What would you do to adjust the model?
Answer: A
Explanation:
In binary classification, the decision threshold determines how much predicted probability is required before an observation is assigned to the positive class. Here, spam represents the positive class, and the model is producing too many spam classifications, indicating excessive positive predictions or false positives.
Increasing the classification threshold requires a higher predicted probability before an email is labeled as spam, reducing the number of positive classifications. Oracle Machine Learning documentation defines the probability threshold as the decision point used for binary classification and explains that changing this threshold changes true-positive and false-positive behavior. Oracle Docs Altering individual feature weights, iteration counts, or regularization affects model training rather than directly controlling the classification decision boundary. Therefore, increasing the classification threshold is the most appropriate adjustment.
NEW QUESTION # 40
What are Convolutional Neural Networks (CNNs) primarily used for?
Answer: A
Explanation:
Convolutional Neural Networks (CNNs) are primarily used for image classification and other tasks involving spatial data. CNNs are particularly effective at recognizing patterns in images due to their ability to detect features such as edges, textures, and shapes across multiple layers of convolutional filters. This makes them the model of choice for tasks such as object recognition, image segmentation, and facial recognition.
CNNs are also used in other domains like video analysis and medical image processing, but their primary application remains in image classification.
NEW QUESTION # 41
What does " fine-tuning " refer to in the context of OCI Generative AI service?
Answer: B
Explanation:
Fine-tuning in the context of the OCI Generative AI service refers to the process of adjusting the parameters of a pretrained model to better fit a specific task or dataset. This process involves further training the model on a smaller, task-specific dataset, allowing the model to refine its understanding and improve its performance on that specific task. Fine-tuning is essential for customizing the general capabilities of a pretrained model to meet the particular needs of a given application, resulting in more accurate and relevant outputs. It is distinct from other processes like encrypting data, upgrading hardware, or simply increasing the complexity of the model architecture.
NEW QUESTION # 42
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