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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Generative AI and Large Language Models | 15% | - Generative AI concepts
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| Topic 2: Machine Learning Foundations | 15% | - Machine Learning fundamentals
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| Topic 3: OCI AI Portfolio | 15% | - Overview of OCI AI offerings
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| Topic 4: Introduction to OCI AI Services | 20% | - OCI AI Service APIs
|
| Topic 5: Deep Learning Foundations | 15% | - Deep Learning and neural networks
|
| Topic 6: OCI Generative AI and Oracle 23ai | 10% | - OCI Generative AI Service features
|
| Topic 7: AI Foundations | 10% | - Artificial Intelligence basics and terminology
|
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NEW QUESTION # 29
What would you use Oracle AI Vector Search for?
Answer: D
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 # 30
In machine learning, what does the term " model training " mean?
Answer: A
Explanation:
In machine learning, " model training " refers to the process of teaching a model to make predictions or decisions by learning the relationships between input features and the corresponding output. During training, the model is fed a large dataset where the inputs are paired with known outputs (labels). The model adjusts its internal parameters to minimize the error between its predictions and the actual outputs. Over time, the model learns to generalize from the training data to make accurate predictions on new, unseen data.
NEW QUESTION # 31
What is the primary benefit of using Oracle Cloud Infrastructure Supercluster for AI workloads?
Answer: B
Explanation:
Oracle Cloud Infrastructure Supercluster is designed to deliver exceptional performance and scalability for complex AI tasks. The primary benefit of this infrastructure is its ability to handle demanding AI workloads, offering high-performance computing (HPC) capabilities that are crucial for training large-scale AI models and processing massive datasets. The architecture of the Supercluster ensures low-latency networking, efficient resource allocation, and high-throughput processing, making it ideal for AI tasks that require significant computational power, such as deep learning, data analytics, and large-scale simulations.
NEW QUESTION # 32
You are training a deep learning model to classify images. What is the primary function of the convolutional layer?
Answer: D
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 # 33
Which AI Ethics principle leads to the Responsible AI requirement of transparency?
Answer: A
Explanation:
Explicability is the AI Ethics principle that leads to the Responsible AI requirement of transparency. This principle emphasizes the importance of making AI systems understandable and interpretable to humans.
Transparency is a key aspect of explicability, as it ensures that the decision-making processes of AI systems are clear and comprehensible, allowing users to understand how and why a particular decision or output was generated. This is critical for building trust in AI systems and ensuring that they are used responsibly and ethically.
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NEW QUESTION # 34
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