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| Section | Objectives |
|---|---|
| Implement End-to-End Machine Learning Lifecycle | - Build, train, and evaluate machine learning models - Automate machine learning workflows and pipelines - Prepare and manage datasets - Save and manage models using Model Catalog - Deploy models and consume model endpoints |
| Use Related OCI Services | - Apply OCI services for data ingestion, storage, and processing - Integrate OCI Data and AI services - Design machine learning solutions for business use cases |
| Apply MLOps Practices | - Monitor and maintain machine learning models - Use best practices for operationalizing ML solutions - Implement model lifecycle management |
| Design and Set Up Data Science Workspace | - Create and configure Data Science projects - Use Accelerated Data Science SDK and open source tools - Manage notebook sessions and compute resources |
| OCI Data Science - Introduction and Configuration | - Configure and manage Data Science resources - Use OCI Data Science notebooks and sessions - Understand OCI Data Science service concepts and architecture |
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質問 # 95
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?
正解：C
解説：
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".
質問 # 96
You have an embarrassingly parallel or distributed batch job on a large amount of data that you consider running using Data Science Jobs. What would be the best approach to run the workload?
正解：C
解説：
Detailed Answer in Step-by-Step Solution:
Objective: Optimize embarrassingly parallel workload in OCI Jobs.
Evaluate Options:
A: Sequential runs—Inefficient for parallel tasks.
B: Simultaneous runs—Maximizes parallelism—correct.
C: False—Jobs support parallelism.
D: One job per run—Misstates capability, wasteful.
Reasoning: B leverages OCI’s parallel run support.
Conclusion: B is correct.
OCI documentation states: “For embarrassingly parallel tasks, create one Job and launch multiple simultaneous Job Runs (B) to process data efficiently.” A is slow, C is incorrect, and Dovercomplicates—B is the best approach.
1: Oracle Cloud Infrastructure Data Science Documentation, "Parallel Job Execution".
質問 # 97
You are a data scientist working inside a notebook session and you attempt to pip install a package from a public repository that is not included in your conda environment. After running this command, you get a network timeout error. What might be missing from your network configuration?
正解：D
解説：
Detailed Answer in Step-by-Step Solution:
Objective: Diagnose a network timeout during pip install in a notebook session.
Understand Notebook Networking: Sessions run in a VCN; internet access requires specific configs.
Analyze Timeout: Indicates failure to reach the public PyPI repository—likely no internet outbound route.
Evaluate Options:
A: NAT Gateway—Provides internet access for private subnets—correct fix.
B: Service Gateway—Accesses OCI services privately, not public internet.
C: FastConnect—Links to on-premises, not public internet.
D: VNIC—Essential but present by default; doesn’t solve internet access.
Reasoning: NAT Gateway enables outbound traffic to public repos like PyPI.
Conclusion: A is correct.
OCI documentation notes: “Notebook sessions in a private subnet require a NAT Gateway to access public internet resources, such as PyPI, for package installation via pip. Without it, network timeouts occur.” Service Gateway (B) is for OCI services, FastConnect (C) is irrelevant, and VNIC (D) is standard—only A resolves the issue.
1: Oracle Cloud Infrastructure Data Science Documentation, "Networking for Notebook Sessions".
質問 # 98
Which technique can be used for feature engineering in the machine learning lifecycle?
正解：B
解説：
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".
質問 # 99
Which Security Zone policy is NOT valid?
正解：B
解説：
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".
質問 # 100
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