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| Section | Weight | Objectives |
|---|---|---|
| Intro to OCI AI Services | 20% | - OCI Speech - OCI Language - OCI Vision - OCI Document Understanding |
| Intro to AI Foundations | 10% | - Discuss AI Applications and Types of Data - Discuss AI Basics - Explain AI vs ML vs DL |
| OCI Generative AI and Oracle 23ai | 10% | - Discuss Oracle Vector Search - Discuss Autonomous Database Select AI - Describe OCI Generative AI Services |
| Get started with OCI AI Portfolio | 15% | - Discuss OCI ML Services Overview - Discuss OCI AI Services Overview - Explain Responsible AI - Discuss OCI AI Infrastructure Overview |
| Intro to Generative AI and LLMs | 15% | - Discuss Large Language Models Fundamentals - Explain Transformers Fundamentals - Explain Prompt Engineering and Instruction Tuning - Discuss Generative AI Overview - Explain LLM Fine Tuning |
| Intro to ML Foundations | 15% | - Discuss Reinforcement Learning Fundamentals - Discuss Unsupervised Learning Fundamentals - Explain Machine Learning Basics - Discuss Supervised Learning Fundamentals
|
| Intro to DL Foundations | 15% | - Explain Convolutional Models (CNN) - Discuss Deep Learning Fundamentals - Explain Sequence Models (RNN and LSTM) |
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NEW QUESTION # 26
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: B
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 # 27
What is the primary benefit of using Oracle Cloud Infrastructure Supercluster for AI workloads?
Answer: A
NEW QUESTION # 28
Which statement describes the Optical Character Recognition (OCR) feature of Oracle Cloud Infrastructure Document Understanding?
Answer: D
Explanation:
The Optical Character Recognition (OCR) feature of Oracle Cloud Infrastructure (OCI) Document Understanding recognizes and extracts text from documents. This capability is fundamental for converting printed or handwritten text into a machine-readable format, allowing for further processing, such as text analysis, search, and archiving. OCI ' s OCR is an essential tool in automating document processing workflows, enabling businesses to digitize and manage their documents efficiently.
NEW QUESTION # 29
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: A
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 # 30
What is the main function of the hidden layers in an Artificial Neural Network (ANN) when recognizing handwritten digits?
Answer: B
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 # 31
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