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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Implement End-to-End Machine Learning Lifecycle | 45% | - Model development, training, and evaluation - Data preparation, exploration, and transformation - Use AutoML and built-in algorithms - Deploy models and manage endpoints - Model saving, cataloging, and versioning |
| 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: 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 4: Apply MLOps Practices | 20% | - ML pipelines, automation, and reproducibility - Governance, auditing, and compliance - Model monitoring, drift detection, and performance tracking |
| Topic 5: Design and Set Up Data Science Workspace | 15% | - Manage access control, security, and IAM integration - Create and manage projects and notebook sessions - Configure compute shapes, storage, and networking |
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NEW QUESTION # 75
You are attempting to save a model from a notebook session to the model catalog by using ADS SDK, with resource principal as the authentication signer, and you get a 404 authentication error. Which TWO should you look for to ensure permissions are set up correctly?
Answer: A,B
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Troubleshoot a 404 authentication error when saving a model using ADS SDK with resource principal.
Understand Resource Principal: Allows notebook sessions to act as principals via dynamic groups and policies—no user credentials needed.
Analyze 404 Error: Indicates an authorization failure—likely missing permissions or misconfigured resource principal.
Evaluate Options:
A: True—Dynamic group must include notebook sessions (e.g., resource.type = 'datasciencenotebooksession') to authenticate.
B: False—Block volume stores artifacts locally, but saving to the catalog is a permission issue, not storage.
C: True—Policy must grant manage data-science-models to the dynamic group for catalog access.
D: False—Service gateway ensures network access, but 404 is auth-related, not connectivity.
E: False—Resource principal uses dynamic group policies, not user group policies.
Reasoning: A (group inclusion) and C (policy permission) are critical for resource principal auth—others are tangential.
Conclusion: A and C are correct.
OCI documentation states: “To use resource principal with ADS SDK for model catalog operations, ensure (1) a dynamic group includes the notebook session with a matching rule (e.g., all {resource.type = 'datasciencenotebooksession'}) and (2) a policy grants the dynamic group manage data-science-models permissions in the compartment.” B is unrelated (storage location), D is network-focused, and E applies to user auth—not resource principal. A 404 error flags missing auth, fixed by A and C.
1: Oracle Cloud Infrastructure Data Science Documentation, "Using Resource Principals with ADS SDK".
NEW QUESTION # 76
Which OCI service provides a scalable environment for developers and data scientists to run Apache Spark applications at scale?
Answer: D
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Identify the OCI service for scalable Spark applications.
Evaluate Options:
A: Data Science—ML platform, not Spark-focused.
B: Anomaly Detection—Specific ML service, not general Spark.
C: Data Labeling—Annotation tool, not Spark-related.
D: Data Flow—Managed Spark service for big data.
Reasoning: Data Flow is OCI’s Spark execution engine.
Conclusion: D is correct.
OCI Data Flow “provides a fully managed environment to run Apache Spark applications at scale, ideal for data processing and ML tasks.” Data Science (A) supports Spark in notebooks, but Data Flow (D) is the dedicated, scalable solution—B and C are unrelated.
1: Oracle Cloud Infrastructure Data Flow Documentation, "Overview".
NEW QUESTION # 77
Which type of firewalls are designed to protect against web application attacks, such as SQL injection and cross-site scripting?
Answer: B
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Identify the firewall type protecting against web app attacks like SQL injection and XSS.
Understand Firewall Types:
Stateful Inspection: Tracks connection states, not app-specific.
Web Application Firewall (WAF): Targets web app vulnerabilities.
Incident Firewall: Not a recognized term.
Packet Filtering: Basic packet rules, not app-aware.
Evaluate Options:
A: Stateful—General network, not web-specific—incorrect.
B: WAF—Designed for SQLi, XSS—correct.
C: Incident—Non-existent—incorrect.
D: Packet—Low-level, not app-focused—incorrect.
Reasoning: WAF specializes in web app security—matches requirement.
Conclusion: B is correct.
OCI documentation states: “Web Application Firewall (WAF) (B) protects against web application attacks like SQL injection and cross-site scripting by inspecting HTTP traffic.” A and D handle network-level threats, C isn’t real—only B aligns with OCI’s WAF purpose.
1: Oracle Cloud Infrastructure WAF Documentation, "Overview".
NEW QUESTION # 78
You are a data scientist using Oracle AutoML to produce a model and you are evaluating the score metric for the model. Which TWO of the following prevailing metrics would you use for evaluating a multiclass classification model?
Answer: C,D
Explanation:
Detailed Answer in Step-by-Step Solution:
Understand Multiclass Classification: Metrics evaluate how well the model predicts multiple classes.
Evaluate Metrics:
A . Mean squared error: Used for regression, not classification.
B . Explained variance score: Regression metric, not suitable.
C . Recall: Measures true positive rate per class—key for classification.
D . F1-score: Balances precision and recall—widely used in multiclass.
E . R-squared: Regression metric, not applicable.
Select Two: Recall (C) and F1-score (D) are standard for multiclass classification.
Oracle AutoML supports metrics like recall and F1-score for multiclass classification, as they assess per-class performance and overall precision-recall balance, respectively. Regression metrics (A, B,E) are irrelevant here. (Oracle Cloud Infrastructure Data Science Documentation, "AutoML Metrics").
NEW QUESTION # 79
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 # 80
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