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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Design and Set Up Data Science Workspace | 15% | - Configure compute shapes, storage, and networking - Create and manage projects and notebook sessions - Manage access control, security, and IAM integration |
| Topic 2: Integrate Related OCI Services | 10% | - Integration with OCI Object Storage, Vault, and Networking - Use OCI AI and data services with Data Science |
| Topic 3: Implement End-to-End Machine Learning Lifecycle | 45% | - Deploy models and manage endpoints - Use AutoML and built-in algorithms - Model saving, cataloging, and versioning - Model development, training, and evaluation - Data preparation, exploration, and transformation |
| Topic 4: Apply MLOps Practices | 20% | - ML pipelines, automation, and reproducibility - Model monitoring, drift detection, and performance tracking - Governance, auditing, and compliance |
| Topic 5: OCI Data Science - Introduction & Configuration | 10% | - Tenancy and environment configuration for Data Science - Capabilities of the Accelerated Data Science (ADS) SDK - Overview and core concepts of OCI Data Science |
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NEW QUESTION # 75
As a data scientist, you create models for cancer prediction based on mammographic images. The correct identification is very crucial in this case. After evaluating two models, you arrive at the following confusion matrix. Which model would you prefer and why?
Model 1 has Test accuracy is 80% and recall is 70%
Model 2 has Test accuracy is 75% and recall is 85%
Answer: D
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Choose the better model for cancer prediction based on metrics.
Understand Metrics:
Accuracy: Overall correct predictions.
Recall: True positives / (True positives + False negatives)—crucial for cancer (minimizing misses).
Context: Cancer prediction prioritizes recall—false negatives (missed cancers) are critical.
Evaluate Models:
Model 1: 80% accuracy, 70% recall—Misses more cancers.
Model 2: 75% accuracy, 85% recall—Misses fewer cancers.
Evaluate Options:
A: High recall—True, but lacks context.
B: High accuracy—Misses recall’s importance.
C: Recall’s impact—Correct for cancer use case—best.
D: Lesser recall impact—Incorrect for this priority.
Reasoning: C emphasizes recall’s critical role—aligns with medical needs.
Conclusion: C is correct.
OCI documentation advises: “For critical predictions like cancer detection, prioritize recall (e.g., Model 2 at 85%) over accuracy (Model 1 at 80%) to minimize false negatives, as missing cases has severe consequences (C).” A is partial, B overlooks context, D reverses priority—only C fits OCI’s ML evaluation guidance for this scenario.
1: Oracle Cloud Infrastructure Data Science Documentation, "Evaluating Classification Models".
NEW QUESTION # 76
What is the first step in the data science process?
Answer: C
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Identify the initial data science step.
Define Process: Starts with problem definition, then data and modeling.
Evaluate Options:
A: Data collection—Second step after problem definition.
B: Modeling—Later stage.
C: Hypothesis—Sets the goal, first step—correct.
D: Data owners—Collaboration, not the start.
Reasoning: Hypothesis drives the process (e.g., “Can we predict churn?”).
Conclusion: C is correct.
OCI documentation states: “The data science process begins with defining an analytical hypothesis to address a business problem, followed by data collection and analysis.” C precedes A, B, and D—aligning with OCI’s structured approach.
1: Oracle Cloud Infrastructure Data Science Documentation, "Data Science Process".
NEW QUESTION # 77
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?
Answer: A,D
Explanation:
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".
NEW QUESTION # 78
Which statement best describes Oracle Cloud Infrastructure Data Science Jobs?
Answer: B
Explanation:
Detailed Answer in Step-by-Step Solution:
Understand OCI Data Science Jobs: This service automates ML tasks (e.g., training, evaluation) with configurable, repeatable executions.
Key Characteristics: Jobs run on OCI’s infrastructure, managed by Oracle, not the customer or third parties, and are specific to Data Science, not general DevOps.
Evaluate Options:
A: Correct—Jobs are defined by users (e.g., via scripts) and executed on OCI’s fully managed compute resources.
B: Incorrect—Infrastructure is managed by OCI, not the customer.
C: Incorrect—No third-party cloud integration; it’s OCI-specific.
D: Incorrect—Jobs are for Data Science tasks (e.g., ML training), not all DevOps workloads (e.g., CI/CD pipelines).
Reasoning: “Fully managed” means OCI handles provisioning and scaling, aligning with A.
Conclusion: A accurately reflects the service’s purpose and operation.
OCI Data Science Jobs “allow users to define and execute repeatable machine learning tasks, such as model training or batch processing, on fully managed OCI infrastructure.” This eliminates customer management (B), third-party clouds (C), or broad DevOps scope (D). The documentation emphasizes automation and management by OCI, making A the precise description.
1: Oracle Cloud Infrastructure Data Science Documentation, "Overview of Jobs" section.
NEW QUESTION # 79
Six months ago you created and deployed a model that predicts customer churn for a call center. Initially, it was yielding quality predictions. However, over the last two months, users have been questioning the credibility of the predictions. Which TWO methods would you employ to verify accuracy and lower customer churn?
Answer: A,C
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Address declining model performance and improve churn prediction.
Analyze Issue: Poor predictions suggest data drift or model staleness—common ML challenges.
Evaluate Options:
A . Drift monitoring: Tracks changes in data distribution—identifies root cause of accuracy drop—correct.
B . Redeploy the model: Repeats deployment without fixing the issue—ineffective alone.
C . Operational monitoring: Tracks system health (e.g., latency), not prediction quality.
D . Retrain the model: Updates model with new data—directly improves accuracy—correct.
E . Validate with recent data: Checks performance but doesn’t fix—diagnostic, not corrective.
Reasoning: A diagnoses drift (cause), D retrains to adapt (solution)—best combo to verify and lower churn.
Conclusion: A and D are correct.
OCI documentation advises: “Drift monitoring (A) detects shifts in data distribution that degrade model performance, while retraining (D) with fresh data restores accuracy.” Redeployment (B) doesn’t address drift, operational monitoring (C) focuses on infra, and validation (E) is a check—not a fix. A and D align with OCI’s model maintenance strategy.
1: Oracle Cloud Infrastructure Data Science Documentation, "Model Monitoring and Retraining".
NEW QUESTION # 80
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