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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Intro to Generative AI and LLMs | 15% | - Discuss Generative AI Overview - Explain Prompt Engineering and Instruction Tuning - Explain LLM Fine Tuning - Explain Transformers Fundamentals - Discuss Large Language Models Fundamentals |
| Topic 2: Get started with OCI AI Portfolio | 15% | - Discuss OCI ML Services Overview - Discuss OCI AI Services Overview - Discuss OCI AI Infrastructure Overview - Explain Responsible AI |
| Topic 3: Intro to AI Foundations | 10% | - Discuss AI Basics - Explain AI vs ML vs DL - Discuss AI Applications and Types of Data |
| Topic 4: OCI Generative AI and Oracle 23ai | 10% | - Discuss Autonomous Database Select AI - Discuss Oracle Vector Search - Describe OCI Generative AI Services |
| Topic 5: Intro to ML Foundations | 15% | - Explain Machine Learning Basics - Discuss Unsupervised Learning Fundamentals - Discuss Supervised Learning Fundamentals
|
| Topic 6: Intro to OCI AI Services | 20% | - OCI Language - OCI Vision - OCI Document Understanding - OCI Speech |
| 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 # 38
Which is NOT a capability of OCI Vision ' s image analysis?
Answer: A
Explanation:
OCI Vision ' s image analysis capabilities include locating and extracting text from images, assigning classification labels to images, and detecting objects with bounding boxes. However, translating text in images to another language is not a capability of OCI Vision ' s image analysis. This functionality typically requires an additional layer of processing, such as integration with a language translation service, which is beyond the scope of OCI Vision ' s core image analysis features.
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NEW QUESTION # 39
Which feature is NOT available as part of OCI Speech capabilities?
Answer: A
Explanation:
OCI Speech capabilities are designed to be user-friendly and do not require extensive data science experience to operate. The service provides features such as transcribing audio and video files into text, offering grammatically accurate transcriptions, supporting multiple languages, and providing timestamped outputs.
These capabilities are built to be accessible to a broad range of users, making speech-to-text conversion seamless and straightforward without the need for deep technical expertise.
NEW QUESTION # 40
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: D
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 # 41
Which feature of OCI Speech helps make transcriptions easier to read and understand?
Answer: B
Explanation:
The text normalization feature of OCI Speech helps make transcriptions easier to read and understand by converting spoken language into a more standardized and grammatically correct format. This process includes correcting grammar, punctuation, and formatting, ensuring that the transcribed text is clear, accurate, and suitable for various use cases. Text normalization enhances the usability of transcriptions, making them more accessible and easier to process in downstream applications.
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NEW QUESTION # 42
What key objective does machine learning strive to achieve?
Answer: A
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 # 43
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