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| Section | Objectives |
|---|---|
| Topic 1: OCI Data Science - Introduction and Configuration | - Use OCI Data Science notebooks and sessions - Understand OCI Data Science service concepts and architecture - Configure and manage Data Science resources |
| Topic 2: Use Related OCI Services | - Integrate OCI Data and AI services - Design machine learning solutions for business use cases - Apply OCI services for data ingestion, storage, and processing |
| Topic 3: Apply MLOps Practices | - Monitor and maintain machine learning models - Use best practices for operationalizing ML solutions - Implement model lifecycle management |
| Topic 4: Implement End-to-End Machine Learning Lifecycle | - Deploy models and consume model endpoints - Save and manage models using Model Catalog - Automate machine learning workflows and pipelines - Build, train, and evaluate machine learning models - Prepare and manage datasets |
| Topic 5: Design and Set Up Data Science Workspace | - Create and configure Data Science projects - Manage notebook sessions and compute resources - Use Accelerated Data Science SDK and open source tools |
>> 1Z0-1110-26 Fragen Und Antworten <<
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92. Frage
Which activity is NOT a part of the machine learning life cycle?
Antwort: C
Begründung:
Detailed Answer in Step-by-Step Solution:
Objective: Identify which activity isn’t part of the ML lifecycle.
Define ML Lifecycle: Includes data access, preparation, modeling, evaluation, deployment, and monitoring.
Evaluate Options:
A: Database Management (e.g., DBA tasks) is IT-related, not specific to ML workflows.
B: Model Deployment (e.g., serving predictions) is a key ML phase—correctly included.
C: Modeling (e.g., training) is the core of ML—correctly included.
D: Data Access (e.g., retrieving data) is the first ML step—correctly included.
Reasoning: Database management supports infrastructure, not the ML process directly.
Conclusion: A is the outlier.
The OCI Data Science lifecycle includes “data access, exploration, feature engineering, modeling, deployment, and monitoring,” per the documentation. Database Management (A) is a general ITtask (e.g., optimizing Oracle DB), not an ML-specific activity, unlike B, C, and D, which are integral to OCI’s ML pipeline.
1: Oracle Cloud Infrastructure Data Science Documentation, "Machine Learning Lifecycle Overview".
93. Frage
You are a data scientist trying to load data into your notebook session. You understand that Accelerated Data Science (ADS) SDK supports loading various data formats. Which of the following THREE are ADS-supported data formats?
Antwort: A,C,D
Begründung:
Detailed Answer in Step-by-Step Solution:
Objective: Identify three data formats supported by ADS SDK for loading data.
Understand ADS SDK: Facilitates data loading into notebook sessions via DatasetFactory.
Evaluate Options:
A . DOCX: Not natively supported—requires conversion (e.g., to text).
B . Pandas DataFrame: Supported—core format for data manipulation in ADS.
C . JSON: Supported—common structured data format.
D . Raw Images: Not directly supported—image data needs preprocessing (e.g., via Vision).
E . XML: Supported—parseable structured format.
Reasoning: ADS focuses on tabular/structured data—B, C, E align; A and D require external handling.
Conclusion: B, C, E are correct.
OCI documentation states: “ADS SDK’s DatasetFactory supports loading data from formats like Pandas DataFrames (B), JSON (C), and XML (E), enabling easy integration into notebook sessions.” DOCX (A) isn’t natively handled, and raw images (D) require preprocessing outside ADS—B, C, E match the supported list.
1: Oracle Cloud Infrastructure ADS SDK Documentation, "Supported Data Formats".
94. Frage
Which statement about Oracle Cloud Infrastructure Anomaly Detection is true?
Antwort: D
Begründung:
Detailed Answer in Step-by-Step Solution:
Objective: Find a true statement about OCI Anomaly Detection.
Understand Service: Detects anomalies in multivariate data (e.g., time series).
Evaluate Options:
A: False—Accepted types are CSV/JSON, not SQL/Python.
B: Partially true—Focuses on numerical data (e.g., sensors), not text broadly.
C: True—Used for fraud, intrusions, and sensor anomalies (key use cases).
D: False—Trained on customer data only, not general datasets.
Reasoning: C aligns with documented applications; others misalign.
Conclusion: C is correct.
OCI Anomaly Detection documentation states: “The service is designed to detect anomalies in time series data, making it valuable for fraud detection, network intrusion analysis, and sensor discrepancies.” A is incorrect (file formats), B overgeneralizes (numerical focus), and D misstates training data—only C matches the service’s purpose.
1: Oracle Cloud Infrastructure Anomaly Detection Documentation, "Use Cases".
95. 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: D,E
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".
96. Frage
Which OCI cloud service lets you centrally manage the encryption keys that protect your data and the secret credentials that you use to securely access resources?
Antwort: C
Begründung:
Detailed Answer in Step-by-Step Solution:
Objective: Identify the OCI service for key and secret management.
Evaluate Options:
A: Data Safe—Database security, not key management.
B: Cloud Guard—Threat detection, not keys.
C: Data Guard—DB replication, not keys.
D: Vault—Key and secret management—correct.
Reasoning: Vault is OCI’s dedicated service for crypto keys and secrets.
Conclusion: D is correct.
OCI documentation states: “OCI Vault (D) centrally manages encryption keys and secrets, securing data and resource access.” A, B, and C serve other purposes—only D matches per OCI’s securityservices.
1: Oracle Cloud Infrastructure Vault Documentation, "Overview".
97. Frage
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