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| Section | Weight | Objectives |
|---|---|---|
| Apply MLOps Practices | 20% | - ML pipelines, automation, and reproducibility - Model monitoring, drift detection, and performance tracking - Governance, auditing, and compliance |
| 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 |
| 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 |
| Implement End-to-End Machine Learning Lifecycle | 45% | - Deploy models and manage endpoints - Model development, training, and evaluation - Model saving, cataloging, and versioning - Data preparation, exploration, and transformation - Use AutoML and built-in algorithms |
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NEW QUESTION # 113
You want to evaluate the relationship between feature values and target variables. You have a large number of observations having a near uniform distribution and the features are highly correlated. Which model explanation technique should you choose?
Answer: A
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Select an explanation technique for feature-target relationships with correlated features.
Evaluate Options:
A: Permutation—Breaks with high correlation.
B: LIME—Local, not global relationships.
C: Dependence—Not a standard term; vague.
D: ALE—Handles correlation, shows feature effects—correct.
Reasoning: ALE is robust to correlated features, ideal here.
Conclusion: D is correct.
OCI documentation states: “Accumulated Local Effects (ALE) (D) evaluates feature-target relationships, accounting for correlations, unlike permutation importance (A) which falters with high correlation.” B is local, C isn’t defined—only D fits per OCI’s explanation tools.
1: Oracle Cloud Infrastructure Data Science Documentation, "Model Explanation Techniques".
NEW QUESTION # 114
Which feature of Oracle Cloud Infrastructure Data Science provides an interactive coding environment for building and training machine learning models?
Answer: D
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Identify the interactive coding environment in OCI Data Science.
Evaluate Options:
A: Model Catalog stores models—not for coding.
B: Jobs run predefined tasks—not interactive.
C: Notebook Sessions provide JupyterLab for coding and training—interactive.
D: Projects organize work—not a coding environment.
Reasoning: Notebook Sessions are OCI’s Jupyter-based tool for interactive ML development.
Conclusion: C is correct.
OCI Data Science Notebook Sessions “provide an interactive JupyterLab environment where data scientists can write code, explore data, and train machine learning models.” Model Catalog (A) is for storage, Jobs (B) for automation, and Projects (D) for organization—only C offers interactivity.
1: Oracle Cloud Infrastructure Data Science Documentation, "Notebook Sessions Overview".
NEW QUESTION # 115
You have been given a collection of digital files required for a business audit. They consist of several different formats that you would like to annotate using Oracle Cloud Infrastructure (OCI) Data Labeling. Which THREE types of files could this tool annotate?
Answer: B,D,E
Explanation:
Detailed Answer in Step-by-Step Solution:
Understand OCI Data Labeling Capabilities: OCI Data Labeling is designed to annotate data for machine learning, supporting specific file types like images, text documents, and videos.
Evaluate Options:
A . Video footage: Supported for tasks like object detection or action recognition.
B . Images: Supported for image classification, object detection, etc.
C . Typewritten document: Supported as text data for tasks like entity extraction or classification.
D . Purchase orders: While potentially text-based, this is ambiguous without format clarification (e.g., PDF, image). OCI supports text annotation, but “purchase orders” isn’t a specific file type—it’s assumed as text here.
E . Audio recording: Not supported, as OCI Data Labeling focuses on visual and textual data, not audio.
Select Three: A (video), B (images), and C (text documents) are explicitly supported file types.
OCI Data Labeling supports annotating datasets of images, text, and videos, as per the official documentation. Video footage (A) can be annotated for tasks like object tracking, images (B) for classification or detection, and typewritten documents (C) for text-based annotations (e.g., named entity recognition). Audio files (E) are not supported, and while purchase orders (D) could be text, the question specifies “typewritten document” as a clearer match. (Oracle Cloud Infrastructure Data Labeling Service Documentation, "Supported Data Types").
NEW QUESTION # 116
As you are working in your notebook session, you find that your notebook session does not have enough compute CPU and memory for your workload. How would you scale up your notebook session without losing your work?
Answer: D
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Scale up notebook without losing work.
Evaluate Options:
A: Object Storage—Extra steps, inefficient.
B: Block volume—Persists data, seamless scale—correct.
C: Local machine—Risky, cumbersome.
D: Recreate—Loses work, impractical.
Reasoning: B uses OCI’s built-in persistence.
Conclusion: B is correct.
OCI documentation states: “Files in /home/datascience (B) persist on block volume; deactivate, then reactivate with a larger shape to scale up without data loss.” A, C, and D add complexity or risk—only B is optimal per OCI’s design.
1: Oracle Cloud Infrastructure Data Science Documentation, "Scaling Notebook Sessions".
NEW QUESTION # 117
Which of these protects customer data at rest and in transit in a way that allows customers to meet their security and compliance requirements for cryptographic algorithms and key management?
Answer: A
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Identify protection for data at rest/transit with customer control.
Evaluate Options:
A: Controls—Broad, not specific to encryption.
B: Isolation—Separates tenants, not crypto-focused.
C: Encryption—Secures data, allows key management—correct.
D: Federation—Auth sharing, not data protection.
Reasoning: C provides crypto control (e.g., Vault keys).
Conclusion: C is correct.
OCI documentation states: “Data encryption (C) protects data at rest and in transit, with customer-managed keys in OCI Vault meeting compliance needs.” A and B are broader, D is unrelated—only C fits per OCI’s security model.
1: Oracle Cloud Infrastructure Security Documentation, "Data Encryption".
NEW QUESTION # 118
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