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1z0-1110-25최신버전공부문제 - 1z0-1110-25적중율높은시험덤프

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Oracle 1z0-1110-25 시험요강:

주제
소개

주제 1

Implement End-to-End Machine Learning Lifecycle: This section evaluates the abilities of Machine Learning Engineers and includes an end-to-end walkthrough of the ML lifecycle within OCI. It involves data acquisition from various sources, data preparation, visualization, profiling, model building with open-source libraries, Oracle AutoML, model evaluation, interpretability with global and local explanations, and deployment using the model catalog.

주제 2

Use Related OCI Services: This final section measures the competence of Machine Learning Engineers in utilizing OCI-integrated services to enhance data science capabilities. It includes creating Spark applications through OCI Data Flow, utilizing the OCI Open Data Service, and integrating other tools to optimize data handling and model execution workflows.

주제 3

Apply MLOps Practices: This domain targets the skills of Cloud Data Scientists and focuses on applying MLOps within the OCI ecosystem. It covers the architecture of OCI MLOps, managing custom jobs, leveraging autoscaling for deployed models, monitoring, logging, and automating ML workflows using pipelines to ensure scalable and production-ready deployments.

주제 4

OCI Data Science - Introduction & Configuration: This section of the exam measures the skills of Machine Learning Engineers and covers foundational concepts of Oracle Cloud Infrastructure (OCI) Data Science. It includes an overview of the platform, its architecture, and the capabilities offered by the Accelerated Data Science (ADS) SDK. It also addresses the initial configuration of tenancy and workspace setup to begin data science operations in OCI.

주제 5

Create and Manage Projects and Notebook Sessions: This part assesses the skills of Cloud Data Scientists and focuses on setting up and managing projects and notebook sessions within OCI Data Science. It also covers managing Conda environments, integrating OCI Vault for credentials, using Git-based repositories for source code control, and organizing your development environment to support streamlined collaboration and reproducibility.

최신 Oracle Cloud 1z0-1110-25 무료샘플문제 (Q112-Q117):

질문 # 112
Why is data sampling useful for data scientists?

A. It reduces the amount of data storage space that's required for data science applications.
B. It lets them analyze datasets in small batches to reduce their use of system resources.
C. It enables them to use a representative subset of data to build accurate analytical models more quickly.

정답：C

설명：
Detailed Answer in Step-by-Step Solution:
* Objective: Determine the primary benefit of data sampling.
* Define Sampling: Selecting a subset of data to represent the whole-used in ML/statistics.
* Evaluate Options:
* A: Small batches reduce resources-True but not the main purpose.
* B: Reduces storage-Incidental, not the goal.
* C: Representative subset for faster, accurate models-Core purpose of sampling.
* Reasoning: Sampling speeds up analysis while maintaining accuracy (e.g., training on 10% of data).
* Conclusion: C is correct.
OCI documentation states: "Data sampling allows data scientists to use a representative subset of a large dataset to build accurate models more quickly, especially when processing full datasets is impractical." A focuses on resources (secondary), B on storage (not primary)-only C captures the analytical intent per OCI's AutoML sampling approach.
Oracle Cloud Infrastructure Data Science Documentation, "Data Sampling Techniques".

질문 # 113
Six months ago, you created and deployed a model that predicts customer churn for a call centre. Initially, it was yielding quality predictions. However, over the last two months, users are questioning the credibility of the predictions. Which TWO methods would you employ to verify the accuracy of the model?

A. Retrain the model
B. Validate the model using recent data
C. Redeploy the model
D. Drift monitoring
E. Operational monitoring

정답：A,D

설명：
Detailed Answer in Step-by-Step Solution:
* Objective: Address declining prediction accuracy and verify model performance.
* Analyze Problem: Degradation over time suggests data drift or model staleness-common ML issues.
* Evaluate Options:
* A. Retrain the model: Uses new data to update the model-fixes accuracy-correct.
* B. Validate with recent data: Tests performance but doesn't fix-diagnostic only.
* C. Drift monitoring: Detects data distribution shifts-verifies cause-correct.
* D. Redeploy the model: Repeats deployment, doesn't address root cause.
* E. Operational monitoring: Tracks infra (e.g., latency), not prediction accuracy.
* Reasoning: C identifies drift (why accuracy dropped), A corrects it-best pair for verification and improvement.
* Conclusion: A and C are correct.
OCI documentation states: "Drift monitoring (C) detects changes in data distribution that impact accuracy, while retraining (A) with new data restores model performance." Validation (B) checks but doesn't fix, redeployment (D) is redundant, and operational monitoring (E) is infra-focused-only A and C align with OCI's model maintenance strategy.
Oracle Cloud Infrastructure Data Science Documentation, "Model Monitoring and Retraining".

질문 # 114
Which THREE types of data are used for Data Labeling?

A. Text Document
B. Audio
C. Graphs
D. Images

정답：A,D

설명：
Detailed Answer in Step-by-Step Solution:
* Objective: Identify three data types for OCI Data Labeling (question likely incomplete-assuming B, C, D, E options).
* Understand Data Labeling: Annotates data for ML-focuses on specific types.
* Evaluate Options (Assuming Typical Set):
* A: Audio-Not supported-incorrect.
* B: Text Document-Supported (e.g., NER)-correct.
* C: Images-Supported (e.g., object detection)-correct.
* D: Graphs-Not a standard type-incorrect.
* Assumed E: Videos-Supported but missing-adjust to fit.
* Reasoning: OCI supports text, images, and videos-question lists only four, so B and C are definite.
* Conclusion: B, C (third likely video, missing).
OCI documentation states: "Data Labeling supports text documents (B), images (C), and videos for annotation-audio (A) and graphs (D) are not included." Question likely meant three from a larger set; B and C are confirmed per OCI's Data Labeling capabilities.
Oracle Cloud Infrastructure Data Labeling Documentation, "Supported Data Types".

질문 # 115
Which statement accurately describes an aspect of machine learning models?

A. Static predictions become increasingly accurate over time.
B. Data models are more static and generally require fewer updates than software code.
C. A high-quality model will not need to be retrained as new information is received.
D. Model performance degrades over time due to changes in data.

정답：D

설명：
Detailed Answer in Step-by-Step Solution:
* Objective: Find a true statement about ML models.
* Evaluate Options:
* A: True-Data drift (changes in data distribution) degrades performance over time.
* B: False-Static predictions don't improve without retraining.
* C: False-Models need updates as data changes, unlike static software.
* D: False-Even high-quality models require retraining with new data.
* Reasoning: A reflects the reality of data drift, a common ML challenge.
* Conclusion: A is correct.
OCI documentation notes: "Model performance can degrade over time due to data drift, where the underlying data distribution changes, necessitating monitoring and retraining." B, C, and D contradict this-static predictions don't improve (B), models aren't static (C), and retraining is needed (D). A is the accurate aspect.
Oracle Cloud Infrastructure Data Science Documentation, "Model Monitoring and Drift".

질문 # 116
You are given the task of writing a program that sorts document images by language. Which Oracle service would you use?

A. OCI Language
B. OCI Vision
C. Oracle Digital Assistant
D. OCI Speech

정답：A

설명：
Detailed Answer in Step-by-Step Solution:
* Objective: Identify the Oracle service to sort document images by language.
* Task Breakdown: Requires extracting text from images (OCR) and detecting language-two potential services involved.
* Evaluate Options:
* A. Oracle Digital Assistant: Builds chatbots-irrelevant to image or language processing.
* B. OCI Language: Detects and classifies languages in text-ideal for sorting after text extraction.
* C. OCI Speech: Transcribes audio to text-not applicable to images.
* D. OCI Vision: Performs OCR to extract text from images-necessary but not sufficient for language sorting.
* Reasoning: The task emphasizes "sorting by language." OCI Vision extracts text, but OCI Language identifies the language (e.g., English, Spanish). Since the question asks for one service and focuses on sorting, OCI Language (B) is the best fit, assuming text extraction is a precursor step.
* Conclusion: B is correct.
OCI Language "provides language detection and classification capabilities, enabling identification of languages in text extracted from documents," per the documentation. OCI Vision handles OCR, but the sorting task aligns with OCI Language (B). Digital Assistant (A) and Speech (C) don't apply, and while Vision (D) is a prerequisite, B is the primary service for language sorting as per OCI's AI service design.
Oracle Cloud Infrastructure Language Documentation, "Language Detection Features".

질문 # 117
......

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