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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Generative AI and Large Language Models | 15% | - Generative AI concepts
|
| Topic 2: Machine Learning Foundations | 15% | - Machine Learning fundamentals
|
| Topic 3: OCI AI Portfolio | 15% | - Overview of OCI AI offerings
|
| Topic 4: OCI Generative AI and Oracle 23ai | 10% | - OCI Generative AI Service features
|
| Topic 5: AI Foundations | 10% | - Artificial Intelligence basics and terminology
|
| Topic 6: Deep Learning Foundations | 15% | - Deep Learning and neural networks
|
| Topic 7: Introduction to OCI AI Services | 20% | - OCI AI Service APIs
|
>> 1z0-1122-26 Answers Real Questions <<
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NEW QUESTION # 13
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 # 14
You are working on a multilingual public announcement system. Which AI task will you use to implement it?
Answer: A
Explanation:
For a multilingual public announcement system, the AI task that would be most relevant is " Text to Speech " (TTS). This task involves converting written text into spoken words, which can then be broadcasted over public address systems in multiple languages.
Text to Speech technology is crucial for creating accessible and understandable announcements in different languages, especially in environments like airports, train stations, or public events where clear verbal communication is essential. The TTS system would be configured to support multiple languages, allowing it to deliver announcements to diverse audiences effectively .
NEW QUESTION # 15
Which statement best describes the relationship between Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL)?
Answer: C
Explanation:
Artificial Intelligence (AI) is the broadest field encompassing all technologies that enable machines to perform tasks that typically require human intelligence. Within AI, Machine Learning (ML) is a subset focused on the development of algorithms that allow systems to learn from and make predictions or decisions based on data. Deep Learning (DL) is a further subset of ML, characterized by the use of artificial neural networks with many layers (hence " deep " ).
In this hierarchy:
* AI includes all methods to make machines intelligent.
* ML refers to the methods within AI that focus on learning from data.
* DL is a specialized field within ML that deals with deep neural networks.
NEW QUESTION # 16
A customer feedback analysis team needs to extract insights from thousands of online product reviews. They want to determine whether customers express positive or negative opinions about different aspects of a product or service. Which feature of OCI Language is helpful?
Answer: D
Explanation:
OCI Language provides pretrained natural language processing capabilities for analyzing unstructured text, including sentiment analysis. In this scenario, the requirement is specifically to determine whether customers express positive or negative opinions concerning individual aspects of a product or service. Oracle ' s OCI Language customer-feedback architecture describes extracting aspects from reviews and associating sentiments such as positive, negative, or neutral with those aspects. Oracle Docs Key phrase extraction identifies important topics or phrases, named entity recognition identifies entities such as organizations, people, or locations, and language detection determines the language in which content is written. None directly evaluates customer opinion. Sentiment analysis therefore precisely matches the business requirement because it transforms subjective review content into structured sentiment information that can be analyzed at scale.
NEW QUESTION # 17
Which feature is NOT supported as part of the OCI Language service ' s pretrained language processing capabilities?
Answer: A
Explanation:
The OCI Language service offers several pretrained language processing capabilities, including Text Classification, Sentiment Analysis, and Language Detection. However, it does not natively support Text Generation as a part of its core language processing capabilities. Text Generation typically involves creating new content based on input prompts, which is a feature more commonly associated with models specifically designed for natural language generation.
NEW QUESTION # 18
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