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| Section | Objectives |
|---|---|
| Implement End-to-End Machine Learning Lifecycle | - Build, train, and evaluate machine learning models - Automate machine learning workflows and pipelines - Prepare and manage datasets - Save and manage models using Model Catalog - Deploy models and consume model endpoints |
| Design and Set Up Data Science Workspace | - Use Accelerated Data Science SDK and open source tools - Manage notebook sessions and compute resources - Create and configure Data Science projects |
| OCI Data Science - Introduction and Configuration | - Configure and manage Data Science resources - Use OCI Data Science notebooks and sessions - Understand OCI Data Science service concepts and architecture |
| Use Related OCI Services | - Design machine learning solutions for business use cases - Apply OCI services for data ingestion, storage, and processing - Integrate OCI Data and AI services |
| Apply MLOps Practices | - Monitor and maintain machine learning models - Implement model lifecycle management - Use best practices for operationalizing ML solutions |
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48. Frage
After you have created and opened a notebook session, you want to use the Accelerated Data Science (ADS) SDK to access your data and get started with exploratory data analysis. From which TWO places can you access the ADS SDK?
Antwort: B,D
Begründung:
Detailed Answer in Step-by-Step Solution:
Objective: Locate sources for ADS SDK in OCI.
Understand ADS SDK: A Python library for Data Science tasks (e.g., EDA).
Evaluate Options:
A: Big Data Service—Spark-focused, not ADS source.
B: Machine Learning—Separate service, not ADS-related.
C: Conda in OCI Data Science—Preinstalled ADS in notebook sessions.
D: PyPI—Public source to install ADS (pip install oracle-ads).
E: ADW—Database, not an SDK source.
Reasoning: C (preinstalled) and D (installable) are practical access points.
Conclusion: C and D are correct.
OCI documentation states: “The ADS SDK is available in OCI Data Science notebook sessions via preinstalled conda environments (C) and can be installed from PyPI (D) using pip install oracle-ads.” Big Data (A), Machine Learning (B), and ADW (E) don’t host ADS—only C and D apply.
1: Oracle Cloud Infrastructure Data Science Documentation, "ADS SDK Installation".
49. Frage
Which of the following TWO non-open source JupyterLab extensions has Oracle Cloud Infrastructure (OCI) Data Science developed and added to the notebook session experience?
Antwort: D,E
Begründung:
Detailed Answer in Step-by-Step Solution:
Objective: Identify two OCI-developed, non-open-source JupyterLab extensions.
Understand Extensions: OCI enhances JupyterLab with proprietary tools.
Evaluate Options:
A: Environment Explorer—OCI-specific, non-open—correct.
B: Table of Contents—Open-source Jupyter—incorrect.
C: Command Palette—Open-source Jupyter—incorrect.
D: Notebook Examples—OCI-specific, non-open—correct.
E: Terminal—Open-source Jupyter—incorrect.
Reasoning: A and D are OCI proprietary; others are standard JupyterLab.
Conclusion: A and D are correct.
OCI documentation states: “OCI Data Science adds non-open-source extensions like Environment Explorer (A) for conda management and Notebook Examples (D) for sample code—both proprietary enhancements.” B, C, and E are open-source JupyterLab defaults—only A and D are OCI-specific per the notebook session design.
1: Oracle Cloud Infrastructure Data Science Documentation, "JupyterLab Extensions".
50. Frage
You are given a task of writing a program that sorts document images by language. Which Oracle AI Service would you use?
Antwort: D
Begründung:
Detailed Answer in Step-by-Step Solution:
Objective: Select an OCI AI service to sort images by language.
Evaluate Options:
A: Digital Assistant—Chatbots, not image/language processing.
B: Vision—Image analysis (e.g., object detection), not language sorting.
C: Speech—Audio-to-text, not image-based.
D: Language—Text analysis (e.g., language detection) after OCR—correct.
Reasoning: Images need OCR (Vision) then language detection (Language)—D fits the sorting task.
Conclusion: D is correct.
OCI Language “detects and classifies languages in text,” often paired with OCI Vision’s OCR to process document images. Vision (B) extracts text, but Language (D) sorts by language—Digital Assistant (A) and Speech (C) don’t apply. Documentation supports this workflow.
1: Oracle Cloud Infrastructure Language Documentation, "Language Detection".
51. Frage
As a data scientist, you are trying to automate a machine learning (ML) workflow and have decided to use Oracle Cloud Infrastructure (OCI) AutoML Pipeline. Which THREE are part of the AutoML Pipeline?
Antwort: C,D,E
Begründung:
Detailed Answer in Step-by-Step Solution:
Objective: Identify three stages in OCI AutoML Pipeline.
Understand Pipeline: Automates ML steps from data to model training.
Evaluate Options:
A: Feature Selection—Selects relevant features—correct.
B: Adaptive Sampling—Reduces data size—correct.
C: Model Deployment—Post-pipeline step—incorrect.
D: Feature Extraction—Not explicit in OCI AutoML—incorrect.
E: Algorithm Selection—Chooses best model—correct.
Reasoning: A, B, E are core automated stages; C and D are separate.
Conclusion: A, B, E are correct.
OCI documentation lists “AutoML Pipeline stages as adaptive sampling (B), feature selection (A), algorithm selection (E), and hyperparameter tuning.” Deployment (C) is post-pipeline, and extraction (D) isn’t highlighted—only A, B, E are included per OCI’s design.
1: Oracle Cloud Infrastructure AutoML Documentation, "Pipeline Components".
52. Frage
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?
Antwort: A,B
Begründung:
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.
1: Oracle Cloud Infrastructure Data Science Documentation, "Model Monitoring and Retraining".
53. Frage
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