Questions for the 1Z0-1122-25 were updated on : Dec 01 ,2025
Which AI domain is associated with tasks such as identifying the sentiment of text and translating
text between languages?
A
Explanation:
Natural Language Processing (NLP) is the AI domain associated with tasks such as identifying the
sentiment of text and translating text between languages. NLP focuses on enabling machines to
understand, interpret, and generate human language in a way that is both meaningful and useful.
This domain covers a wide range of applications, including text classification, language translation,
sentiment analysis, and more, all of which involve processing and analyzing natural language data.
Which capability is supported by the Oracle Cloud Infrastructure Vision service?
B
Explanation:
The Oracle Cloud Infrastructure (OCI) Vision service is designed for image analysis tasks, which
includes the capability to detect and recognize objects, such as vehicle number plates. This
functionality is particularly useful for applications such as automated enforcement of traffic laws,
where the system can identify vehicles exceeding speed limits and issue citations based on the
detected number plates. This capability leverages advanced computer vision techniques to process
and analyze visual data, making it suitable for applications in public safety, transportation, and law
enforcement.
What is the purpose of the model catalog in OCI Data Science?
C
Explanation:
The primary purpose of the model catalog in OCI Data Science is to store, track, share, and manage
machine learning models. This functionality is essential for maintaining an organized repository
where data scientists and developers can collaborate on models, monitor their performance, and
manage their lifecycle. The model catalog also facilitates model versioning, ensuring that the most
recent and effective models are available for deployment. This capability is crucial in a collaborative
environment where multiple stakeholders need access to the latest model versions for testing,
evaluation, and deployment.
Which feature of OCI Speech helps make transcriptions easier to read and understand?
D
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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How does Oracle Cloud Infrastructure Document Understanding service facilitate business
processes?
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.
Which feature is NOT available as part of OCI Speech capabilities?
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.
Which statement describes the Optical Character Recognition (OCR) feature of Oracle Cloud
Infrastructure Document Understanding?
C
Explanation:
The Optical Character Recognition (OCR) feature of Oracle Cloud Infrastructure (OCI) Document
Understanding recognizes and extracts text from documents. This capability is fundamental for
converting printed or handwritten text into a machine-readable format, allowing for further
processing, such as text analysis, search, and archiving. OCI's OCR is an essential tool in automating
document processing workflows, enabling businesses to digitize and manage their documents
efficiently.
How do Large Language Models (LLMs) handle the trade-off between model size, data quality, data
size and performance?
D
Explanation:
Large Language Models (LLMs) handle the trade-off between model size, data quality, data size, and
performance by balancing these factors to achieve optimal results. Larger models typically provide
better performance due to their increased capacity to learn from data; however, this comes with
higher computational costs and longer training times. To manage this trade-off effectively, LLMs are
designed to balance the size of the model with the quality and quantity of data used during training,
and the amount of time dedicated to training. This balanced approach ensures that the models
achieve high performance without unnecessary resource expenditure.
What would you use Oracle AI Vector Search for?
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.
What can Oracle Cloud Infrastructure Document Understanding NOT do?
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.
What is "in-context learning" in the realm of Large Language Models (LLMs)?
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.
What distinguishes Generative AI from other types of AI?
A
Explanation:
Generative AI is distinct from other types of AI in that it focuses on creating new content by learning
patterns from existing data. This includes generating text, images, audio, and other types of media.
Unlike AI that primarily analyzes data to make decisions or predictions, Generative AI actively creates
new and original outputs. This ability to generate diverse content is a hallmark of Generative AI
models like GPT-4, which can produce human-like text, create images, and even compose music
based on the patterns they have learned from their training data.
What is the primary benefit of using the OCI Language service for text analysis?
A
Explanation:
The primary benefit of using the OCI Language service for text analysis is its ability to scale text
analysis without requiring users to have extensive machine learning expertise. The service abstracts
the complexities of machine learning, allowing businesses to easily process and analyze large
amounts of text data through pre-built models. This accessibility makes it possible for a broader
range of users to leverage advanced text analysis capabilities, facilitating insights from textual data
without needing to develop and train models from scratch.
What feature of OCI Data Science provides an interactive coding environment for building and
training models?
D
Explanation:
In OCI Data Science, Notebook sessions provide an interactive coding environment that is essential
for building, training, and deploying machine learning models. These sessions allow data scientists to
write and execute code in real time, offering a flexible environment for data exploration, model
experimentation, and iterative development. The integration with various OCI services and support
for popular machine learning frameworks further enhances the utility of Notebook sessions, making
them a crucial tool in the data science workflow.
Which capability is supported by Oracle Cloud Infrastructure Language service?
C
Explanation:
Oracle Cloud Infrastructure (OCI) Language service is specifically designed to analyze text and extract
structured information such as sentiment, entities, key phrases, and language detection. This service
provides natural language processing (NLP) capabilities that help users gain insights from
unstructured text data. By identifying the sentiment (positive, negative, neutral) and recognizing
entities (like names, dates, or places), the service enables businesses to process large volumes of
text data efficiently, aiding in decision-making processes.