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| Section | Objectives |
|---|---|
| Topic 1: Design and Set Up Data Science Workspace | - Manage notebook sessions and compute resources - Create and configure Data Science projects - Use Accelerated Data Science SDK and open source tools |
| Topic 2: Apply MLOps Practices | - Use best practices for operationalizing ML solutions - Monitor and maintain machine learning models - Implement model lifecycle management |
| Topic 3: OCI Data Science - Introduction and Configuration | - Understand OCI Data Science service concepts and architecture - Use OCI Data Science notebooks and sessions - Configure and manage Data Science resources |
| Topic 4: 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 5: Implement End-to-End Machine Learning Lifecycle | - Automate machine learning workflows and pipelines - Prepare and manage datasets - Build, train, and evaluate machine learning models - Save and manage models using Model Catalog - Deploy models and consume model endpoints |
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NEW QUESTION # 133
What is the correct definition of Git?
Answer: B
Explanation:
Detailed Answer in Step-by-Step Solution:
Define Git: Git is a version control system—centralized vs. distributed is key.
Evaluate Options:
A: Incorrect—Git is distributed, not centralized (e.g., SVN is centralized).
B: Correct—Distributed, tracks file changes across local and remote repos.
C: Incorrect—Git allows simultaneous contributions; it manages, not prevents, merges.
D: Incorrect—Centralized is wrong, and “copious data” is vague.
Reasoning: Git’s distributed nature (each user has a full repo copy) and change-tracking are core traits.
Conclusion: B is accurate.
OCI documentation aligns with Git’s official definition: “Git is a distributed version control system that tracks changes to files, enabling collaboration and version history management.” A and D misclassify it as centralized, while C misrepresents merge handling—B captures Git’s essence as used in OCI Data Science.
1: Oracle Cloud Infrastructure Code Repository Documentation, "Git Overview".
NEW QUESTION # 134
What is the primary difference between a data scientist and a data engineer?
Answer: C
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Differentiate data scientist vs. data engineer roles.
Define Roles:
Data Engineer: Builds pipelines, prepares data.
Data Scientist: Analyzes data, builds models.
Evaluate Options:
A: Engineer preps, scientist analyzes—Correct division.
B: Reverses roles—Incorrect.
C: Overlaps roles—Scientist doesn’t typically build pipelines.
D: Misaligns—Analyst isn’t the focus.
Reasoning: A reflects standard role separation.
Conclusion: A is correct.
OCI documentation notes: “Data engineers focus on collecting and preparing data through pipelines, while data scientists analyze it to derive insights and build models.” A aligns, B inverts, C overcomplicates, and D shifts focus—only A is accurate.
1: Oracle Cloud Infrastructure Data Science Documentation, "Roles in Data Science".
NEW QUESTION # 135
Which architecture is based on the principle of “never trust, always verify”?
Answer: B
Explanation:
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".
NEW QUESTION # 136
True or false? Data scientists typically need a combination of technical skills, nontechnical ones, and suitable personality traits to be successful.
Answer: A
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Assess required skills for data scientists.
Analyze Skills:
Technical: Coding, stats, ML.
Nontechnical: Communication, business acumen.
Traits: Curiosity, problem-solving.
Reasoning: Success requires this mix—e.g., explaining models to stakeholders.
Conclusion: A (True) is correct.
OCI documentation states: “Effective data scientists combine technical skills (e.g., Python), nontechnical skills (e.g., storytelling), and traits like analytical thinking.” This holistic requirement is true (A), not false (B).
1: Oracle Cloud Infrastructure Data Science Documentation, "Data Scientist Skills".
NEW QUESTION # 137
You are a data scientist using Oracle AutoML to produce a model and you are evaluating the score metric for the model. Which of the following TWO prevailing metrics would you use for evaluating a multiclass classification model?
Answer: B,C
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Select two metrics for multiclass classification in AutoML.
Understand Multiclass Metrics: Focus on class-specific performance—classification, not regression.
Evaluate Options:
A . Recall: Measures true positives per class—key for multiclass—correct.
B . Mean squared error: Regression metric—incorrect.
C . F1 Score: Balances precision and recall—standard for multiclass—correct.
D . R-Squared: Regression fit—incorrect.
E . Explained variance: Regression metric—incorrect.
Reasoning: A and C assess classification accuracy across multiple classes—fit AutoML’s evaluation.
Conclusion: A and C are correct.
OCI AutoML documentation states: “For multiclass classification, common evaluation metrics include recall (A) for per-class sensitivity and F1 Score (C) for balanced performance.” B, D, and E are regression-focused—only A and C are supported and relevant per OCI’s AutoML metrics suite.
1: Oracle Cloud Infrastructure AutoML Documentation, "Evaluation Metrics for Classification".
NEW QUESTION # 138
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