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Oracle Cloud Infrastructure 2024 AI Foundations Associate Sample Questions (Q33-Q38):

NEW QUESTION # 33
What can Oracle Cloud Infrastructure Document Understanding NOT do?

A. Generate transcript from documents
B. Extract text from documents
C. Classify documents into different types
D. Extract tables from documents

Answer: A

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 # 34
Which feature is NOT available as part of OCI Speech capabilities?

A. Provides timestamped, grammatically accurate transcriptions
B. Transcribes audio and video files into text
C. Uses extensive data science experience to operate
D. Supports multiple languages including English, Spanish, and Portuguese

Answer: C

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 # 35
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?

A. Regression
B. Binary Classification
C. Clustering
D. Multi-Class Classification

Answer: D

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 # 36
How does Oracle Cloud Infrastructure Document Understanding service facilitate business processes?

A. By analyzing sentiment in text documents
B. By transcribing spoken language
C. By generating lifelike speech from documents
D. By automating data extraction from documents

Answer: D

Explanation:
Oracle Cloud Infrastructure (OCI) Document Understanding service facilitates business processes by automating data extraction from documents. This service leverages machine learning to identify, classify, and extract relevant information from various document types, reducing the need for manual data entry and improving efficiency in document processing workflows. Automation of these tasks enables organizations to streamline operations and reduce errors associated with manual data handling.

NEW QUESTION # 37
What does "fine-tuning" refer to in the context of OCI Generative AI service?

A. Adjusting the model parameters to improve accuracy
B. Doubling the neural network layers
C. Encrypting the data for security reasons
D. Upgrading the hardware of the AI clusters

Answer: A

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 # 38
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