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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: OCI Data Science - Introduction & Configuration | 10% | - Capabilities of the Accelerated Data Science (ADS) SDK - Tenancy and environment configuration for Data Science - Overview and core concepts of OCI Data Science |
| Topic 2: Integrate Related OCI Services | 10% | - Use OCI AI and data services with Data Science - Integration with OCI Object Storage, Vault, and Networking |
| Topic 3: 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 4: Apply MLOps Practices | 20% | - Governance, auditing, and compliance - Model monitoring, drift detection, and performance tracking - ML pipelines, automation, and reproducibility |
| Topic 5: Implement End-to-End Machine Learning Lifecycle | 45% | - Deploy models and manage endpoints - Model saving, cataloging, and versioning - Model development, training, and evaluation - Use AutoML and built-in algorithms - Data preparation, exploration, and transformation |
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질문 # 20
Which architecture is based on the principle of “never trust, always verify”?
정답：A
설명：
Detailed Answer in Step-by-Step Solution:
Objective: Identify the architecture with “never trust, always verify.” Evaluate Options:
A: Federated identity—Shares auth, not verification-focused.
B: Zero trust—Explicitly “never trust, always verify”—correct.
C: Fluid perimeter—Adaptive, not the core principle.
D: Defense in depth—Layered, not verification-centric.
Reasoning: Zero trust matches the stated principle exactly.
Conclusion: B is correct.
OCI documentation states: “Zero trust (B) architecture operates on ‘never trust, always verify,’ requiring continuous authentication and authorization.” A, C, and D have different focuses—only B aligns with OCI’s security philosophy.
1: Oracle Cloud Infrastructure Security Documentation, "Zero Trust Architecture".
질문 # 21
On which option do you set Oracle Cloud Infrastructure Budget?
정답：A
설명：
Detailed Answer in Step-by-Step Solution:
Objective: Determine where OCI budgets are set.
Understand Budgets: Track spending across OCI resources.
Evaluate Options:
A: Compartments—Scoped within tenancy, not budget root.
B: Instances—Specific resources, not budget scope.
C: Tags—Filter costs, not budget setting.
D: Tenancy—Top-level scope for budgets—correct.
Reasoning: Budgets apply at tenancy, optionally filtered (e.g., by compartment).
Conclusion: D is correct.
OCI documentation states: “Budgets are set at the tenancy level (D), with optional filters like compartments or tags to monitor spending.” A, B, and C are sub-elements—only D is the primary scope per OCI’s cost management.
1: Oracle Cloud Infrastructure Cost Management Documentation, "Setting Budgets".
질문 # 22
You are building a model and need input that represents data as morning, afternoon, or evening. However, the data contains a timestamp. What part of the Data Science lifecycle would you be in when creating the new variable?
정답：D
설명：
Detailed Answer in Step-by-Step Solution:
Objective: Locate the lifecycle stage for transforming timestamps into categories.
Understand Lifecycle:
Model Type Selection: Choosing algorithms—post-data prep.
Model Validation: Evaluating performance—post-training.
Data Access: Retrieving raw data—pre-transformation.
Feature Engineering: Creating new features—correct.
Evaluate Options:
A: Too late—incorrect.
B: Post-model—incorrect.
C: Pre-transformation—incorrect.
D: Feature creation—correct.
Reasoning: Converting timestamps to time periods is feature engineering.
Conclusion: D is correct.
OCI documentation states: “Feature engineering (D) transforms raw data, such as timestamps into categorical variables (e.g., morning/afternoon), to enhance model inputs.” A, B, and C occur at different stages—only D fits OCI’s lifecycle for this task.
1: Oracle Cloud Infrastructure Data Science Documentation, "Feature Engineering Stage".
질문 # 23
Which of the following best describes the principal goal of data science?
정답：D
설명：
Detailed Answer in Step-by-Step Solution:
Objective: Define data science’s main goal.
Evaluate Options:
A: Archiving—Not the focus; too narrow.
B: Analyze for insights/business value—Core purpose—correct.
C: Prep for analytics—Means, not the end goal.
D: Output-focused—Vague, incomplete.
Reasoning: B captures the actionable insight generation central to data science.
Conclusion: B is correct.
OCI documentation defines data science as “mining and analyzing large datasets to uncoveractionable insights for operational improvements and business value.” A is storage-focused, C is preparatory, and D is unclear—only B reflects the principal goal per OCI’s mission.
1: Oracle Cloud Infrastructure Data Science Documentation, "What is Data Science?".
질문 # 24
What is a common maxim about data scientists?
정답：A
설명：
Detailed Answer in Step-by-Step Solution:
Objective: Identify a widely accepted maxim about data scientists’ time allocation.
Understand Data Science Workflow: Involves data collection, preparation, and analysis—time distribution is key.
Evaluate Options:
A: 80% on finding/preparing, 20% analyzing—Reflects the data wrangling challenge.
B: 80% analyzing, 20% finding/preparing—Inverts the common perception.
C: 80% on failed projects, 20% useful—Pessimistic, not a standard maxim.
Reasoning: Industry consensus (e.g., “80/20 rule”) emphasizes data prep as the bulk of effort due to messy real-world data.
Conclusion: A is correct.
OCI Data Science documentation aligns with industry norms: “Data scientists typically spend 80% of their time finding, cleaning, and preparing data, and 20% on analysis and modeling, due to the complexity of raw data.” B reverses this, and C isn’t supported—only A reflects this widely cited maxim from sources like Forbes and OCI’s practical guidance.
1: Oracle Cloud Infrastructure Data Science Documentation, "Data Science WorkflowOverview".
질문 # 25
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