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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Intro to AI Foundations | 10% | - Discuss AI Basics - Explain AI vs ML vs DL - Discuss AI Applications and Types of Data |
| Topic 2: Intro to OCI AI Services | 20% | - OCI Language - OCI Document Understanding - OCI Speech - OCI Vision |
| Topic 3: Intro to ML Foundations | 15% | - Discuss Supervised Learning Fundamentals
- Discuss Unsupervised Learning Fundamentals - Discuss Reinforcement Learning Fundamentals |
| Topic 4: Intro to Generative AI and LLMs | 15% | - Explain Transformers Fundamentals - Discuss Large Language Models Fundamentals - Explain LLM Fine Tuning - Explain Prompt Engineering and Instruction Tuning - Discuss Generative AI Overview |
| 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 DL Foundations | 15% | - Discuss Deep Learning Fundamentals - Explain Convolutional Models (CNN) - Explain Sequence Models (RNN and LSTM) |
| Topic 7: OCI Generative AI and Oracle 23ai | 10% | - Discuss Autonomous Database Select AI - Describe OCI Generative AI Services - Discuss Oracle Vector Search |
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NEW QUESTION # 31
What can Oracle Cloud Infrastructure Document Understanding NOT do?
Answer: B
Explanation:
Oracle Cloud Infrastructure (OCI) Document Understanding service offers several capabilities, including extracting tables, classifying documents, and extracting text. However, it does not generate transcripts from documents. Transcription typically refers to converting spoken language into written text, which is a function associated with speech-to-text services, not document understanding services. Therefore, generating a transcript is outside the scope of what OCI Document Understanding is designed to do .
NEW QUESTION # 32
Lisa is working on a project that involves transcribing thousands of audio files stored in Oracle Cloud. She wants to process multiple files efficiently instead of transcribing them one by one. Which OCI Speech feature should Lisa use?
Answer: C
Explanation:
OCI Speech supports batch transcription for efficiently processing prerecorded media. Lisa ' s key requirement is to process thousands of audio files without manually handling them individually. Oracle documentation explicitly identifies OCI Speech as supporting both real-time and batch transcription, while the OCI Speech documentation includes transcription jobs and large batch jobs among its supported workflows.
Oracle Docs Batch support therefore directly addresses high-volume processing of stored audio. Confidence scoring measures the service ' s certainty about recognized speech, timestamping associates transcript content with positions in the source recording, and profanity filtering controls how offensive language is represented.
Those capabilities enhance transcription output but do not provide the required multi-file processing mechanism. Consequently, Batch support is the appropriate OCI Speech capability for scalable transcription of Lisa ' s collection of audio files.
NEW QUESTION # 33
What is the difference between classification and regression in Supervised Machine Learning?
Answer: A
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 # 34
You are training a deep learning model to classify images. What is the primary function of the convolutional layer?
Answer: C
Explanation:
A convolutional layer is designed to learn local visual features from an input image. During training, small learnable filters move across the image and respond to patterns such as edges, corners, textures, and progressively more complex structures. The resulting feature maps preserve useful spatial relationships while transforming raw pixels into representations that later layers can use. Oracle documentation recognizes convolutional neural networks as suitable neural-network architectures for visual data and identifies architectures such as ResNet for processing images. Oracle Docs The convolutional layer itself does not primarily generate images or make the final classification decision. Reducing spatial dimensions is normally performed through pooling or strided operations. Therefore, detecting specific features in the input image is the correct function.
NEW QUESTION # 35
What is " in-context learning " in the realm of Large Language Models (LLMs)?
Answer: D
Explanation:
" In-context learning " in the realm of Large Language Models (LLMs) refers to the ability of these models to learn and adapt to a specific task by being provided with a few examples of that task within the input prompt.
This approach allows the model to understand the desired pattern or structure from the given examples and apply it to generate the correct outputs for new, similar inputs. In-context learning is powerful because it does not require retraining the model; instead, it uses the examples provided within the context of the interaction to guide its behavior.
NEW QUESTION # 36
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