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| Section | Weight | Objectives |
|---|---|---|
| Implement End-to-End Machine Learning Lifecycle | 45% | - Model saving, cataloging, and versioning - Data preparation, exploration, and transformation - Use AutoML and built-in algorithms - Model development, training, and evaluation - Deploy models and manage endpoints |
| OCI Data Science - Introduction & Configuration | 10% | - Tenancy and environment configuration for Data Science - Capabilities of the Accelerated Data Science (ADS) SDK - Overview and core concepts of OCI Data Science |
| Integrate Related OCI Services | 10% | - Integration with OCI Object Storage, Vault, and Networking - Use OCI AI and data services with Data Science |
| Apply MLOps Practices | 20% | - Governance, auditing, and compliance - ML pipelines, automation, and reproducibility - Model monitoring, drift detection, and performance tracking |
| Design and Set Up Data Science Workspace | 15% | - Manage access control, security, and IAM integration - Configure compute shapes, storage, and networking - Create and manage projects and notebook sessions |
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NEW QUESTION # 20
You want to make your model more frugal to reduce the cost of collecting and processing dat a. You plan to do this by removing features that are highly correlated. You would like to create a heatmap that displays the correlation so that you can identify candidate features to remove. Which Accelerated Data Science (ADS) SDK method is appropriate to display the comparability between Continuous and Categorical features?
Answer: A
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Visualize correlation between continuous and categorical features.
Evaluate Options:
A: Pearson—Continuous vs. continuous—incorrect.
B: Cramer’s V—Categorical vs. categorical—incorrect.
C: Correlation ratio—Continuous vs. categorical—correct.
D: General correlation—Not specific to mixed types.
Reasoning: Correlation ratio handles mixed feature types for heatmaps.
Conclusion: C is correct.
OCI documentation states: “correlation_ratio_plot() (C) in ADS SDK visualizes correlations between continuous and categorical features, ideal for mixed-type heatmaps.” Pearson (A) and Cramer’s (B) are type-specific, corr() (D) is broad—only C fits per ADS capabilities.
1: Oracle Cloud Infrastructure ADS SDK Documentation, "Correlation Visualization".
NEW QUESTION # 21
Triggering a PagerDuty notification as part of Monitoring is an example of what in the OCI Console?
Answer: B
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Classify a PagerDuty notification in OCI Monitoring.
Understand OCI Monitoring: Involves events, rules, and actions for notifications.
Evaluate Options:
A: Action—executes a response (e.g., notify PagerDuty) when triggered—correct.
B: Rule—defines conditions for triggering actions—precedes the action.
C: Function—serverless code, not directly tied to notifications.
D: Event—state change triggering a rule, not the notification itself.
Reasoning: The notification is the action taken after an event/rule—fits A.
Conclusion: A is correct.
OCI documentation states: “Actions in the Monitoring service execute responses, such as sending notifications to PagerDuty, when a rule’s condition is met based on an event.” Rules (B) set conditions, Functions (C) are unrelated, and Events (D) are triggers—only Action (A) describes the notification step.
1: Oracle Cloud Infrastructure Monitoring Documentation, "Actions Overview".
NEW QUESTION # 22
Which encryption is used for Oracle Data Science?
Answer: E
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Identify encryption standard for OCI Data Science.
Understand OCI Encryption: Applies to data at rest and in transit.
Evaluate Options:
A: AES-256—Industry-standard, OCI default—correct.
B: DES—Outdated, weak—incorrect.
C: TDES—Older, less secure—incorrect.
D: Twofish—Not OCI standard—incorrect.
E: RSA—Asymmetric, not primary for data at rest—incorrect.
Reasoning: AES-256 is OCI’s go-to for Data Science resources.
Conclusion: A is correct.
OCI documentation states: “Data Science services encrypt data at rest using AES-256 (A), ensuring high security for notebooks, jobs, and models.” B, C, D, and E are either outdated or not used—only A matches OCI’s encryption policy.
1: Oracle Cloud Infrastructure Data Science Documentation, "Data Encryption".
NEW QUESTION # 23
Which OCI Data Science interaction method can function without the need of scripting?
Answer: D
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Identify the OCI Data Science interaction method that doesn’t require scripting.
Understand Interaction Methods: OCI provides multiple ways to interact with Data Science services—some are GUI-based, others script-based.
Evaluate Options:
A . OCI Console: A web-based graphical interface allowing users to manage resources (e.g., create notebook sessions, deploy models) via point-and-click—no scripting needed.
B . CLI: Command Line Interface requires writing commands (scripts) to execute tasks (e.g., oci data-science notebook-session create).
C . Language SDKs: Software Development Kits (e.g., Python SDK) require coding to interact programmatically (e.g., oci.data_science.DataScienceClient).
D . REST APIs: Application Programming Interfaces require scripted HTTP requests (e.g., using curl or a programming language).
Reasoning: Only the OCI Console (A) offers a no-code, user-friendly interface, while B, C, and D rely on scripting or programming.
Conclusion: A is the correct answer as it eliminates the need for scripting.
The OCI Console is described in the documentation as “a browser-based interface that allows users to manage OCI Data Science resources, such as creating notebook sessions or jobs, without writing code or scripts.” In contrast, the CLI (B) requires command-line scripts, SDKs (C) need programming (e.g., Python), and REST APIs (D) involve scripted API calls. The Console’s GUI distinguishes it as the only option functioning without scripting, aligning with Oracle’s design for accessibility to non-programmers.
1: Oracle Cloud Infrastructure Data Science Documentation, "Getting Started with OCI Console" section.
NEW QUESTION # 24
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: C
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 # 25
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