1z0-1122-26最新試題 - 1z0-1122-26證照資訊

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Oracle 1z0-1122-26 Exam Syllabus Topics:

Section	Weight	Objectives
Get started with OCI AI Portfolio	15%	- Explain Responsible AI - Discuss OCI AI Infrastructure Overview - Discuss OCI ML Services Overview - Discuss OCI AI Services Overview
Intro to OCI AI Services	20%	- OCI Document Understanding - OCI Vision - OCI Speech - OCI Language
OCI Generative AI and Oracle 23ai	10%	- Discuss Oracle Vector Search - Describe OCI Generative AI Services - Discuss Autonomous Database Select AI
Intro to DL Foundations	15%	- Explain Convolutional Models (CNN) - Discuss Deep Learning Fundamentals - Explain Sequence Models (RNN and LSTM)
Intro to ML Foundations	15%	- Discuss Unsupervised Learning Fundamentals - Explain Machine Learning Basics - Discuss Reinforcement Learning Fundamentals - Discuss Supervised Learning Fundamentals 1. Classification 2. Regression
Intro to AI Foundations	10%	- Discuss AI Basics - Discuss AI Applications and Types of Data - Explain AI vs ML vs DL
Intro to Generative AI and LLMs	15%	- Explain Prompt Engineering and Instruction Tuning - Discuss Generative AI Overview - Discuss Large Language Models Fundamentals - Explain Transformers Fundamentals - Explain LLM Fine Tuning

>> 1z0-1122-26最新試題 <<

1z0-1122-26證照資訊 & 1z0-1122-26證照考試

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最新的 Oracle Cloud Infrastructure 1z0-1122-26 免費考試真題 (Q44-Q49):

問題 #44
You are training a logistic regression model to classify emails as spam or not spam. The model is currently classifying too many emails as spam. What would you do to adjust the model?

A. Increase the threshold value for classification.
B. Decrease the number of iterations.
C. Decrease the weight of the feature representing the presence of certain keywords.
D. Increase the regularization parameter.

答案：A

解題說明：
In binary classification, the decision threshold determines how much predicted probability is required before an observation is assigned to the positive class. Here, spam represents the positive class, and the model is producing too many spam classifications, indicating excessive positive predictions or false positives.
Increasing the classification threshold requires a higher predicted probability before an email is labeled as spam, reducing the number of positive classifications. Oracle Machine Learning documentation defines the probability threshold as the decision point used for binary classification and explains that changing this threshold changes true-positive and false-positive behavior. Oracle Docs Altering individual feature weights, iteration counts, or regularization affects model training rather than directly controlling the classification decision boundary. Therefore, increasing the classification threshold is the most appropriate adjustment.

問題 #45
Which statement describes the Optical Character Recognition (OCR) feature of Oracle Cloud Infrastructure Document Understanding?

A. It converts audio files into text.
B. It provides real-time translation of text.
C. It recognizes and extracts text from a document.
D. It enhances the visual quality of documents.

答案：C

解題說明：
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.

問題 #46
How do Large Language Models (LLMs) handle the trade-off between model size, data quality, data size and performance?

A. They prioritize larger model sizes to achieve better performance.
B. They focus on increasing the number of tokens while keeping the model size constant.
C. They ensure that the model size, training time, and data size are balanced for optimal results.
D. They disregard model size and prioritize high-quality data only.

答案：C

解題說明：
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.

問題 #47
What distinguishes Generative AI from other types of AI?

A. Generative AI involves training models to perform tasks without human intervention.
B. Generative AI focuses on making decisions based on user interactions.
C. Generative AI creates diverse content such as text, audio, and images by learning patterns from existing data.
D. Generative AI uses algorithms to predict outcomes based on past data.

答案：C

解題說明：
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.

問題 #48
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?

A. Profanity filtering
B. Batch support
C. Confidence scoring
D. Timestamping

答案：B

解題說明：
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.

問題 #49
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1z0-1122-26證照資訊: https://www.vcesoft.com/1z0-1122-26-pdf.html