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| Section | Weight | Objectives |
|---|---|---|
| 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 |
| Integrate Related OCI Services | 10% | - Use OCI AI and data services with Data Science - Integration with OCI Object Storage, Vault, and Networking |
| Apply MLOps Practices | 20% | - ML pipelines, automation, and reproducibility - Model monitoring, drift detection, and performance tracking - Governance, auditing, and compliance |
| Implement End-to-End Machine Learning Lifecycle | 45% | - Data preparation, exploration, and transformation - Model saving, cataloging, and versioning - Model development, training, and evaluation - Use AutoML and built-in algorithms - Deploy models and manage endpoints |
| OCI Data Science - Introduction & Configuration | 10% | - Capabilities of the Accelerated Data Science (ADS) SDK - Overview and core concepts of OCI Data Science - Tenancy and environment configuration for Data Science |
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NEW QUESTION # 109
Which of the following best describes the principal goal of data science?
Answer: D
Explanation:
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?".
NEW QUESTION # 110
You want to create a user group for a team of external data science consultants. The consultants should only have the ability to see Data Science resource details but not have the ability to create, delete, or update Data Science resources. What verb should you write in the policy?
Answer: D
Explanation:
Detailed Answer in Step-by-Step Solution:
Understand OCI IAM Policies: Policies control access using verbs like “inspect,” “read,” “use,” and “manage.” Define Requirement: Consultants need view-only access without modification rights.
Evaluate Verbs:
A . Use: Allows interaction (e.g., running jobs)—too permissive.
B . Inspect: Grants view-only access to resource details—matches requirement.
C . Manage: Full control (create, update, delete)—too permissive.
D . Read: Includes viewing content (e.g., data), not just metadata—slightly broader than needed.
Conclusion: “Inspect” (B) is the precise verb for view-only access to resource details.
In OCI Identity and Access Management (IAM), the “inspect” verb allows listing and viewing resource metadata without granting modification or data access rights, ideal for this scenario. This is confirmed in the IAM policy reference. (Oracle Cloud Infrastructure Documentation, "IAM Policy Verbs").
NEW QUESTION # 111
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 # 112
Which statement is true about standards?
Answer: A
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Identify a true statement about standards in an OCI context (likely governance/security).
Understand Standards: Rules or benchmarks, often compliance-related.
Evaluate Options:
A: Auditable—True; standards are checked for adherence.
B: Result of requirements—Partially true, but not always.
C: Methods/instructions—More procedural, not defining standards.
D: Foundation of governance—Broad, not specific to standards.
Reasoning: A is universally true—standards face audits (e.g., SOC, ISO).
Conclusion: A is correct.
OCI documentation notes: “Standards (e.g., security standards) may be audited (A) to ensure compliance with OCI policies or external regulations.” B is a source, C describes procedures, D is too vague—only A is consistently true per OCI’s compliance framework.
1: Oracle Cloud Infrastructure Security Documentation, "Compliance and Standards".
NEW QUESTION # 113
You are a data scientist; you use the Oracle Cloud Infrastructure (OCI) Language service to train custom models. Which types of custom models can be trained?
Answer: A
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Identify custom model types for OCI Language.
Understand OCI Language: Focuses on text analysis.
Evaluate Options:
A: Image classification—Not text-based, incorrect.
B: Text classification, NER—Both text tasks—correct.
C: Sentiment, NER—Sentiment is pretrained, not custom.
D: Object detection—Image-based, incorrect.
Reasoning: B aligns with OCI Language’s text custom models.
Conclusion: B is correct.
OCI Language documentation states: “Custom models can be trained for text classification and Named Entity Recognition (NER) using your data.” Image tasks (A, D) are for Vision, and sentiment (C) is pretrained—only B fits OCI Language’s scope.
1: Oracle Cloud Infrastructure Language Documentation, "Custom Model Training".
NEW QUESTION # 114
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