1z0-1122-26 Prüfungsinformationen - 1z0-1122-26 German

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Oracle 1z0-1122-26 Exam Syllabus Topics:

Section	Objectives
OCI Generative AI and Oracle Database AI Capabilities	- Oracle AI Database 1. Vector database concepts 2. AI capabilities in Oracle Database - AI Application Frameworks 1. Language frameworks 2. Retrieval and AI application concepts - OCI Generative AI 1. Generative AI models and applications 2. OCI Generative AI services
Oracle AI and Machine Learning Services	- OCI AI Services 1. Document Understanding 2. Language 3. Vision 4. Speech - OCI Machine Learning Services 1. Machine learning capabilities in OCI 2. OCI Data Science and machine learning workflows - Oracle AI Stack 1. AI infrastructure 2. AI data and machine learning services
Generative AI and Large Language Models	- Generative AI Fundamentals 1. Generative AI use cases 2. Generative AI concepts and capabilities - Large Language Models 1. Language models and generative AI applications 2. LLM fundamentals
Artificial Intelligence and Machine Learning Fundamentals	- Machine Learning Fundamentals 1. Machine learning models and architectures 2. Supervised and unsupervised learning - Deep Learning Fundamentals 1. Neural networks 2. Convolutional and sequence models - Artificial Intelligence Concepts 1. AI use cases and applications 2. AI fundamentals and terminology

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1z0-1122-26 German & 1z0-1122-26 Fragen Antworten

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Oracle Cloud Infrastructure 2026 AI Foundations Associate 1z0-1122-26 Prüfungsfragen mit Lösungen (Q45-Q50):

45. Frage
Which AI Ethics principle leads to the Responsible AI requirement of transparency?

A. Fairness
B. Explicability
C. Prevention of harm
D. Respect for human autonomy

Antwort: B

Begründung:
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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46. Frage
What is the main function of the hidden layers in an Artificial Neural Network (ANN) when recognizing handwritten digits?

A. Providing labels for the output neurons
B. Capturing the internal representation of the raw image data
C. Storing the input pixel values
D. Directly predicting the final output

Antwort: B

Begründung:
In an Artificial Neural Network (ANN) designed for recognizing handwritten digits, the hidden layers serve the crucial function of capturing the internal representation of the raw image data. These layers learn to extract and represent features such as edges, shapes, and textures from the input pixels, which are essential for distinguishing between different digits. By transforming the input data through multiple hidden layers, the network gradually abstracts the raw pixel data into higher-level representations, which are more informative and easier to classify into the correct digit categories.

47. Frage
You are part of the medical transcription team and need to automate transcription tasks. Which OCI AI service are you most likely to use?

A. Speech
B. Document Understanding
C. Vision
D. Language

Antwort: A

Begründung:
For automating transcription tasks in a medical transcription team, the most appropriate OCI AI service to use would be the " Speech " service. This service is designed to convert spoken language into text, which is essential for transcribing spoken medical reports or consultations into written form. The OCI Speech service provides capabilities such as speech-to-text conversion, which is specifically tailored for handling audio input and producing accurate transcriptions.

48. Frage
Which type of machine learning is used to understand relationships within data and is not focused on making predictions or classifications?

A. Reinforcement learning
B. Unsupervised learning
C. Supervised learning
D. Active learning

Antwort: B

Begründung:
Unsupervised learning is a type of machine learning that focuses on understanding relationships within data without the need for labeled outcomes. Unlike supervised learning, which requires labeled data to train models to make predictions or classifications, unsupervised learning works with unlabeled data and aims to discover hidden patterns, groupings, or structures within the data.
Common applications of unsupervised learning include clustering, where the algorithm groups data points into clusters based on similarities, and association, where it identifies relationships between variables in the dataset. Since unsupervised learning does not predict outcomes but rather uncovers inherent structures, it is ideal for exploratory data analysis and discovering previously unknown patterns in data .

49. Frage
What feature of OCI Data Science provides an interactive coding environment for building and training models?

A. Model catalog
B. Conda environment
C. Notebook sessions
D. Accelerated Data Science (ADS) SDK

Antwort: C

Begründung:
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.

50. Frage
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