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| Section | Objectives |
|---|---|
| Topic 1: 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 |
| Topic 2: Design and Set Up Data Science Workspace | - Manage notebook sessions and compute resources - Use Accelerated Data Science SDK and open source tools - Create and configure Data Science projects |
| Topic 3: Use Related OCI Services | - Apply OCI services for data ingestion, storage, and processing - Design machine learning solutions for business use cases - Integrate OCI Data and AI services |
| Topic 4: Apply MLOps Practices | - Monitor and maintain machine learning models - Implement model lifecycle management - Use best practices for operationalizing ML solutions |
| Topic 5: Implement End-to-End Machine Learning Lifecycle | - Automate machine learning workflows and pipelines - Deploy models and consume model endpoints - Save and manage models using Model Catalog - Prepare and manage datasets - Build, train, and evaluate machine learning models |
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NEW QUESTION # 129
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?
Answer: D,E
Explanation:
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".
NEW QUESTION # 130
You have received machine learning model training code, without clear information about the optimal shape to run the training. How would you proceed to identify the optimal compute shape for your model training that provides a balanced cost and processing time?
Answer: D
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Optimize compute shape for cost and time.
Evaluate Options:
A: Tuning params—Focuses on model, not shape.
B: Strongest shape—Costly, unbalanced.
C: Scale up when utilized—Balances cost/time—correct.
D: Random start—Unsystematic.
Reasoning: C iteratively optimizes based on utilization.
Conclusion: C is correct.
OCI documentation advises: “Start with a small shape, monitor utilization and time (C); scale up if fully utilized until performance stabilizes—optimizes cost and speed.” A misfocuses, B overspends, D lacks method—only C aligns.
1: Oracle Cloud Infrastructure Data Science Documentation, "Compute Shape Optimization".
NEW QUESTION # 131
As a data scientist for a hardware company, you have been asked to predict the revenue demand for the upcoming quarter. You develop a time series forecasting model to analyze the dat a. Select the correct sequence of steps to predict the revenue demand values for the upcoming quarter.
Answer: D
Explanation:
Detailed Answer in Step-by-Step Solution:
Prepare Model: Build and train the time series model using historical data.
Verify: Validate the model’s accuracy (e.g., using metrics like MAE or RMSE).
Save: Store the trained model (e.g., in the OCI Model Catalog).
Deploy: Make the model available for predictions (e.g., via OCI Model Deployment).
Predict: Generate revenue forecasts for the upcoming quarter.
Evaluate Options: D follows this logical flow; others (e.g., A starts with “verify” before preparation) don’t.
In OCI Data Science, the workflow for time series forecasting involves preparing the model (training), verifying its performance, saving it to the catalog, deploying it, and then predicting. This sequence is standard for ML deployment in OCI, as per the documentation. (Oracle Cloud Infrastructure Data Science Documentation, "Time Series Forecasting Workflow").
NEW QUESTION # 132
You have configured the Management Agent on an Oracle Cloud Infrastructure (OCI) Linux instance for log ingestion purposes. Which is a required configuration for OCI Logging Analytics service to collect data from multiple logs of this instance?
Answer: B
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Identify the required configuration for OCI Logging Analytics to collect logs from an instance.
Understand Logging Analytics: Collects and analyzes logs from OCI resources via Management Agents.
Key Concepts:
Entity: Represents the instance (e.g., Linux VM).
Source: Defines log locations (e.g., file paths).
Log Group: Organizes logs for analysis.
Evaluate Options:
A: Log-Log Group—Groups logs, not collection setup.
B: Entity-Log—Links instance to logs, but not source-specific.
C: Source-Entity—Maps log sources to the instance—correct.
D: Log Group-Source—Post-collection organization, not ingestion.
Reasoning: C establishes the link between the instance and its log sources—key for ingestion.
Conclusion: C is correct.
OCI documentation states: “To collect logs using Logging Analytics, configure a Source-Entity Association (C) to link the Management Agent on the instance (entity) to specific log sources (e.g., file paths).” A and D organize logs post-collection, B is less specific—only C is required for ingestion per OCI’s Logging Analytics setup.
1: Oracle Cloud Infrastructure Logging Analytics Documentation, "Configuring Log Collection".
NEW QUESTION # 133
Which model has an open-source, open model format that allows you to run machine learning models on different platforms?
Answer: A
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Identify an open model format for cross-platform ML model execution.
Evaluate Options:
A . PySpark: A big data framework, not a model format.
B . PyTorch: An ML framework with its own format, not inherently cross-platform without conversion.
C . TensorFlow: An ML framework with its SavedModel format, not universally open across platforms.
D . ONNX: Open Neural Network Exchange, an open-source format for model interoperability across frameworks.
Reasoning: ONNX is designed for portability (e.g., convert PyTorch to ONNX, run in TensorFlow), unlike framework-specific options.
Conclusion: D is the correct choice.
ONNX (D) is “an open-source model format that enables interoperability between ML frameworks like PyTorch and TensorFlow,” per OCI documentation. PySpark (A) is a processing tool, while PyTorch (B) and TensorFlow (C) are frameworks with native formats—only ONNX ensures cross-platform compatibility.
1: Oracle Cloud Infrastructure Data Science Documentation, "Supported Model Formats".
NEW QUESTION # 134
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