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| Section | Weight | Objectives |
|---|---|---|
| Implement End-to-End Machine Learning Lifecycle | 45% | - Model saving, cataloging, and versioning - Data preparation, exploration, and transformation - Use AutoML and built-in algorithms - Model development, training, and evaluation - Deploy models and manage endpoints |
| Integrate Related OCI Services | 10% | - Integration with OCI Object Storage, Vault, and Networking - Use OCI AI and data services with Data Science |
| Design and Set Up Data Science Workspace | 15% | - Manage access control, security, and IAM integration - Create and manage projects and notebook sessions - Configure compute shapes, storage, and networking |
| Apply MLOps Practices | 20% | - Governance, auditing, and compliance - ML pipelines, automation, and reproducibility - Model monitoring, drift detection, and performance tracking |
| OCI Data Science - Introduction & Configuration | 10% | - Overview and core concepts of OCI Data Science - Tenancy and environment configuration for Data Science - Capabilities of the Accelerated Data Science (ADS) SDK |
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질문 # 106
What is the first step in the data science process?
정답：B
설명：
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".
질문 # 107
You want to write a Python script to create a collection of different projects for your data science team. Which Oracle Cloud Infrastructure (OCI) Data Science interface would you use?
정답：D
설명：
Detailed Answer in Step-by-Step Solution:
Objective: Choose an interface for a Python script to manage projects.
Evaluate Options:
A: OCI SDK—Python-based, scriptable—correct.
B: Console—GUI, not scriptable.
C: CLI—Command-based, not Python-native.
D: Mobile App—Not for scripting.
Reasoning: A enables programmatic project creation.
Conclusion: A is correct.
OCI documentation states: “Use the OCI Python SDK (A) to programmatically manage Data Science resources, like creating projects, via Python scripts.” B, C, and D don’t support Python scripting—only A fits.
1: Oracle Cloud Infrastructure SDK Documentation, "Data Science API".
질문 # 108
Which statement is true about origin management in Web Application Firewall (WAF)?
정답：F
설명：
Detailed Answer in Step-by-Step Solution:
Objective: Determine truth about WAF origin management.
Understand WAF: Protects apps by routing traffic via origins.
Evaluate Statements:
A: Multiple origins—True; WAF supports this.
B: Single active origin—True; only one is active per policy.
Evaluate Options:
C: B only—False; A is true.
D: Both false—Incorrect.
E: Both true—Correct per OCI WAF.
F: A only—False; B is true.
Conclusion: E is correct.
OCI documentation states: “WAF allows defining multiple origins (A), but only one origin is active per WAF policy at a time (B)—both are true (E).” C, D, and F misalign—E matches OCI’s WAF origin management.
1: Oracle Cloud Infrastructure WAF Documentation, "Origin Management".
질문 # 109
You want to make your model more parsimonious to reduce the cost of collecting and processing dat a. You plan to do this by removing features that are highly correlated. You would like to create a heatmap that displays the correlation so that you can identify candidate features to remove. Which Accelerated Data Science (ADS) SDK method would be appropriate to display the correlation between Continuous and Categorical features?
정답：B
설명：
Detailed Answer in Step-by-Step Solution:
Objective: Visualize correlation between continuous and categorical features using ADS SDK.
Understand Correlation Types:
Continuous vs. Continuous: Pearson correlation.
Categorical vs. Categorical: Cramer’s V.
Continuous vs. Categorical: Correlation ratio (eta).
Evaluate Options:
A . corr(): General correlation (Pearson), not suited for mixed types—incorrect.
B . correlation_ratio_plot(): Plots correlation ratio for continuous-categorical—correct.
C . pearson_plot(): Not an ADS method; Pearson is continuous-only—incorrect.
D . cramersv_plot(): Cramer’s V for categorical-categorical—incorrect.
Reasoning: Correlation ratio measures association between continuous and categorical variables—ideal for heatmap in this mixed scenario.
Conclusion: B is correct.
OCI documentation states: “The correlation_ratio_plot() method (B) in ADS SDK generates a heatmap displaying the correlation ratio between continuous and categorical features, suitable for identifying highly correlated features for removal.” corr() (A) defaults to Pearson, pearson_plot() (C) isn’t real, and cramersv_plot() (D) is for categorical pairs—only B aligns with OCI’s ADS capabilities for this use case.
1: Oracle Cloud Infrastructure ADS SDK Documentation, "Correlation Visualization Methods".
질문 # 110
Select two reasons why it is important to rotate encryption keys when using Oracle Cloud Infrastructure (OCI) Vault to store credentials or other secrets.
정답：B,D
질문 # 111
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