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| Section | Weight | Objectives |
|---|---|---|
| Implement End-to-End Machine Learning Lifecycle | 45% | - Use AutoML and built-in algorithms - Deploy models and manage endpoints - Model development, training, and evaluation - Data preparation, exploration, and transformation - Model saving, cataloging, and versioning |
| OCI Data Science - Introduction & Configuration | 10% | - Capabilities of the Accelerated Data Science (ADS) SDK - Tenancy and environment configuration for Data Science - Overview and core concepts of OCI Data Science |
| Apply MLOps Practices | 20% | - Model monitoring, drift detection, and performance tracking - Governance, auditing, and compliance - ML pipelines, automation, and reproducibility |
| 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 |
| Integrate Related OCI Services | 10% | - Integration with OCI Object Storage, Vault, and Networking - Use OCI AI and data services with Data Science |
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NEW QUESTION # 106
As a data scientist, you are trying to automate a machine learning (ML) workflow and have decided to use Oracle Cloud Infrastructure (OCI) AutoML Pipeline. Which THREE are part of the AutoML Pipeline?
Answer: C,D,E
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Identify three stages in OCI AutoML Pipeline.
Understand Pipeline: Automates ML steps from data to model training.
Evaluate Options:
A: Feature Selection—Selects relevant features—correct.
B: Adaptive Sampling—Reduces data size—correct.
C: Model Deployment—Post-pipeline step—incorrect.
D: Feature Extraction—Not explicit in OCI AutoML—incorrect.
E: Algorithm Selection—Chooses best model—correct.
Reasoning: A, B, E are core automated stages; C and D are separate.
Conclusion: A, B, E are correct.
OCI documentation lists “AutoML Pipeline stages as adaptive sampling (B), feature selection (A), algorithm selection (E), and hyperparameter tuning.” Deployment (C) is post-pipeline, and extraction (D) isn’t highlighted—only A, B, E are included per OCI’s design.
1: Oracle Cloud Infrastructure AutoML Documentation, "Pipeline Components".
NEW QUESTION # 107
Which OCI service provides a scalable environment for developers and data scientists to run Apache Spark applications at scale?
Answer: B
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 # 108
You want to make your model more parsimonious 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 would be appropriate to display the correlation between Continuous and Categorical features?
Answer: D
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Visualize correlation between continuous and categorical features using ADS SDK.
Understand Correlation Types:
Continuous vs. Continuous: Pearson correlation.
Categorical vs. Categorical: Cramer’s V.
Continuous vs. Categorical: Correlation ratio (eta).
Evaluate Options:
A . corr(): General correlation (Pearson), not suited for mixed types—incorrect.
B . correlation_ratio_plot(): Plots correlation ratio for continuous-categorical—correct.
C . pearson_plot(): Not an ADS method; Pearson is continuous-only—incorrect.
D . cramersv_plot(): Cramer’s V for categorical-categorical—incorrect.
Reasoning: Correlation ratio measures association between continuous and categorical variables—ideal for heatmap in this mixed scenario.
Conclusion: B is correct.
OCI documentation states: “The correlation_ratio_plot() method (B) in ADS SDK generates a heatmap displaying the correlation ratio between continuous and categorical features, suitable for identifying highly correlated features for removal.” corr() (A) defaults to Pearson, pearson_plot() (C) isn’t real, and cramersv_plot() (D) is for categorical pairs—only B aligns with OCI’s ADS capabilities for this use case.
1: Oracle Cloud Infrastructure ADS SDK Documentation, "Correlation Visualization Methods".
NEW QUESTION # 109
Which statement accurately describes an aspect of machine learning models?
Answer: D
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Find a true statement about ML models.
Evaluate Options:
A: True—Data drift (changes in data distribution) degrades performance over time.
B: False—Static predictions don’t improve without retraining.
C: False—Models need updates as data changes, unlike static software.
D: False—Even high-quality models require retraining with new data.
Reasoning: A reflects the reality of data drift, a common ML challenge.
Conclusion: A is correct.
OCI documentation notes: “Model performance can degrade over time due to data drift, where the underlying data distribution changes, necessitating monitoring and retraining.” B, C, and D contradict this—static predictions don’t improve (B), models aren’t static (C), and retraining is needed (D). A is the accurate aspect.
1: Oracle Cloud Infrastructure Data Science Documentation, "Model Monitoring and Drift".
NEW QUESTION # 110
Which Security Zone policy is NOT valid?
Answer: B
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Spot the invalid Security Zone policy.
Understand Security Zones: Enforce strict security—resources can’t leave easily.
Evaluate Options:
A: Boot volume move—Not allowed; violates zone rules—invalid.
B: Compute no-move—True, enforces security.
C: No public access—True, standard policy.
D: Auto-backup—True, required for compliance.
Reasoning: A contradicts Security Zone immutability.
Conclusion: A is incorrect.
OCI documentation states: “Resources like boot volumes (A) cannot be moved from a Security Zone to a standard compartment—B, C, and D are enforced policies.” A violates OCI’s Security Zone restrictions—only A is not valid.
1: Oracle Cloud Infrastructure Security Zones Documentation, "Policy Restrictions".
NEW QUESTION # 111
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