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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: 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 |
| 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: Design and Set Up Data Science Workspace | 15% | - Create and manage projects and notebook sessions - Manage access control, security, and IAM integration - Configure compute shapes, storage, and networking |
| Topic 4: Implement End-to-End Machine Learning Lifecycle | 45% | - Use AutoML and built-in algorithms - Deploy models and manage endpoints - Data preparation, exploration, and transformation - Model development, training, and evaluation - Model saving, cataloging, and versioning |
| Topic 5: Apply MLOps Practices | 20% | - ML pipelines, automation, and reproducibility - Governance, auditing, and compliance - Model monitoring, drift detection, and performance tracking |
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NEW QUESTION # 30
Which of the following programming languages are most widely used by data scientists?
Answer: B
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Identify top languages for data science.
Evaluate Options:
A: C/C++—Low-level, less common for data tasks.
B: Python (ML, libraries), R (stats), SQL (data)—Industry standards.
C: Java (enterprise), JavaScript (web)—Not data-focused.
Reasoning: B aligns with data science tools (e.g., pandas, ggplot).
Conclusion: B is correct.
OCI documentation highlights “Python, R, and SQL as the most widely used languages in Data Science for modeling, analysis, and data querying.” C/C++ (A) and Java/JS (C) are less prevalent—B matches OCI’s notebook support and industry trends.
1: Oracle Cloud Infrastructure Data Science Documentation, "Supported Languages".
NEW QUESTION # 31
Which cache rules criterion matches if the concatenation of the requested URL path and query are identical to the contents of the value field?
Answer: D
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Match a cache rule criterion for exact URL path and query.
Understand Cache Rules: Used in OCI (e.g., WAF, CDN) to cache content.
Evaluate Options:
A: Contains—Partial match, not exact.
B: Is—Exact match of full URL (path + query)—correct.
C: Ends with—Matches end, not full URL.
D: Starts with—Matches start, not full URL.
Reasoning: “URL_IS” checks exact equality—fits requirement.
Conclusion: B is correct.
OCI documentation states: “The URL_IS (B) criterion in cache rules matches when the full URL (path and query) exactly equals the specified value.” A, C, and D are partial matches—only B ensures identical concatenation per OCI’s caching config.
1: Oracle Cloud Infrastructure WAF Documentation, "Cache Rules Criteria".
NEW QUESTION # 32
Which OCI cloud service lets you centrally manage the encryption keys that protect your data and the secret credentials that you use to securely access resources?
Answer: C
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Identify the OCI service for key and secret management.
Evaluate Options:
A: Data Safe—Database security, not key management.
B: Cloud Guard—Threat detection, not keys.
C: Data Guard—DB replication, not keys.
D: Vault—Key and secret management—correct.
Reasoning: Vault is OCI’s dedicated service for crypto keys and secrets.
Conclusion: D is correct.
OCI documentation states: “OCI Vault (D) centrally manages encryption keys and secrets, securing data and resource access.” A, B, and C serve other purposes—only D matches per OCI’s securityservices.
1: Oracle Cloud Infrastructure Vault Documentation, "Overview".
NEW QUESTION # 33
Why is data sampling useful for data scientists?
Answer: C
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Determine the primary benefit of data sampling.
Define Sampling: Selecting a subset of data to represent the whole—used in ML/statistics.
Evaluate Options:
A: Small batches reduce resources—True but not the main purpose.
B: Reduces storage—Incidental, not the goal.
C: Representative subset for faster, accurate models—Core purpose of sampling.
Reasoning: Sampling speeds up analysis while maintaining accuracy (e.g., training on 10% of data).
Conclusion: C is correct.
OCI documentation states: “Data sampling allows data scientists to use a representative subset of a large dataset to build accurate models more quickly, especially when processing full datasets is impractical.” A focuses on resources (secondary), B on storage (not primary)—only C captures the analytical intent per OCI’s AutoML sampling approach.
1: Oracle Cloud Infrastructure Data Science Documentation, "Data Sampling Techniques".
NEW QUESTION # 34
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 # 35
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