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| Section | Weight | Objectives |
|---|---|---|
| Implement End-to-End Machine Learning Lifecycle | 45% | - Use AutoML and built-in algorithms - Model development, training, and evaluation - Deploy models and manage endpoints - Data preparation, exploration, and transformation - Model saving, cataloging, and versioning |
| Integrate Related OCI Services | 10% | - Use OCI AI and data services with Data Science - Integration with OCI Object Storage, Vault, and Networking |
| OCI Data Science - Introduction & Configuration | 10% | - Tenancy and environment configuration for Data Science - Overview and core concepts of OCI Data Science - Capabilities of the Accelerated Data Science (ADS) SDK |
| 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 |
| 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 # 98
What is the primary difference between a data scientist and a data engineer?
Answer: A
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 # 99
What is the minimum active storage duration for logs used by Logging Analytics to be archived?
Answer: D
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Determine minimum log storage duration before archiving in Logging Analytics.
Understand Logging Analytics: Logs are active before archival.
Evaluate Options:
A: 60 days—Too long for minimum.
B: 10 days—Too short.
C: 30 days—Standard minimum—correct.
D: 15 days—Not OCI’s default.
Reasoning: 30 days is OCI’s documented minimum active period.
Conclusion: C is correct.
OCI documentation states: “Logs in Logging Analytics remain active for a minimum of 30 days (C) before archiving, ensuring availability for analysis.” B and D are shorter, A is longer—only C matches OCI’s policy.
1: Oracle Cloud Infrastructure Logging Analytics Documentation, "Log Retention".
NEW QUESTION # 100
Which technique can be used for feature engineering in the machine learning lifecycle?
Answer: B
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Identify a feature engineering technique in ML.
Understand Feature Engineering: Transforms raw data into model-ready features.
Evaluate Options:
A . PCA: Reduces dimensionality—feature engineering—correct.
B . K-means: Clustering model—not feature engineering.
C . SVM: Classification model—not feature engineering.
D . Gradient boosting: Model training—not feature engineering.
Reasoning: PCA creates new features via transformation—fits definition.
Conclusion: A is correct.
OCI documentation states: “Feature engineering techniques like Principal Component Analysis (PCA) (A) transform data into new features to enhance model performance.” B, C, and D are modeling techniques—only A aligns with OCI’s feature engineering stage.
1: Oracle Cloud Infrastructure Data Science Documentation, "Feature Engineering Techniques".
NEW QUESTION # 101
Which type of file system does File Storage use?
Answer: D
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Identify the file system type for OCI File Storage.
Understand File Storage: Network-attached storage in OCI.
Evaluate Options:
A: NFSv3—Network File System, used by File Storage—correct.
B: iSCSI—Block storage protocol, not File Storage.
C: Paravirtualized—Virtualization mode, not file system.
D: NVMe SSD—Hardware, not file system.
Reasoning: NFSv3 is OCI File Storage’s protocol.
Conclusion: A is correct.
OCI documentation states: “File Storage uses NFSv3 (A) as its file system protocol, providing shared storage across instances.” B, C, and D are unrelated—only A aligns with OCI’s File Storage design.
1: Oracle Cloud Infrastructure File Storage Documentation, "File System Protocol".
NEW QUESTION # 102
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: D,E
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 # 103
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