1z0-1110-25 Exam Dumps - Oracle Cloud Infrastructure 2025 Data Science Professional

Detailed Answer in Step-by-Step Solution:

Objective: Identify the non-visualized AutoML stage with small data.

Understand AutoML Pipeline: Includes sampling, feature/algorithm selection, tuning.

Evaluate Options:

A: Feature selection—Visualized (e.g., feature importance).

B: Algorithm selection—Visualized (e.g., algorithm scores).

C: Adaptive sampling—Skipped/visualization absent for <1000 rows.

D: Hyperparameter tuning—Visualized (e.g., trial plots).

Reasoning: Adaptive sampling optimizes large datasets; small data skips it, omitting visuals.

Conclusion: C is correct.

OCI AutoML documentation notes: “Adaptive sampling is applied to large datasets (>1000 rows) to reduce size; for smaller datasets, it’s skipped, and no visualization is generated.” Other stages (A, B,D) produce visuals—only C is absent here.

Oracle Cloud Infrastructure AutoML Documentation, "Pipeline Stages".

Detailed Answer in Step-by-Step Solution:

Objective: Identify the OCI service for key and secret management.

Evaluate Options:

A: Data Safe—Database security, not key management.

B: Cloud Guard—Threat detection, not keys.

C: Data Guard—DB replication, not keys.

D: Vault—Key and secret management—correct.

Reasoning: Vault is OCI’s dedicated service for crypto keys and secrets.

Conclusion: D is correct.

OCI documentation states: “OCI Vault (D) centrally manages encryption keys and secrets, securing data and resource access.” A, B, and C serve other purposes—only D matches per OCI’s securityservices.

Oracle Cloud Infrastructure Vault Documentation, "Overview".

Detailed Answer in Step-by-Step Solution:

Objective: Define LIME’s core concept.

Understand LIME: Explains individual predictions with local surrogate models.

Evaluate Options:

A: Complex global, simple local—Correct LIME principle.

B: Agnosticism—True but not the key idea.

C: Global/local similarity—False.

D: Local vs. global agnosticism—Incorrect distinction.

Reasoning: A captures LIME’s local approximation focus.

Conclusion: A is correct.

OCI documentation notes: “LIME (A) explains predictions by approximating complex global models with simpler local surrogate models around specific instances.” B, C, and D misalign—only A reflects LIME’s foundational idea per OCI’s interpretability tools.

Oracle Cloud Infrastructure Data Science Documentation, "Model Interpretability - LIME".

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.

Oracle Cloud Infrastructure Security Zones Documentation, "Policy Restrictions".

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.

Oracle Cloud Infrastructure ADS SDK Documentation, "Correlation Visualization".

Detailed Answer in Step-by-Step Solution:

Objective: Configure conda env for a pipeline step.

Evaluate Options:

A: Shape—Infra, not env config.

B: Volume—Storage, not env.

C: Command-line—Step args, not env.

D: Env variables—Sets conda path—correct.

Reasoning: D specifies runtime env (e.g., CONDA_ENV_SLUG).

Conclusion: D is correct.

OCI documentation states: “Configure a pipeline step’s conda environment using environment variables (D), such as CONDA_ENV_SLUG, in the step definition.” A, B, and C address other aspects—only D fits env config.

Oracle Cloud Infrastructure Data Science Documentation, "Pipeline Step Configuration".

Detailed Answer in Step-by-Step Solution:

Objective: Identify a step in OCI’s AutoML pipeline.

Understand AutoML: Automates model building—includes preprocessing, selection, and tuning.

Evaluate Options:

A: Feature Extraction (e.g., PCA) isn’t explicitly part of OCI AutoML—too specific.

B: Saving to Model Catalog is post-AutoML, not a pipeline step.

C: Deployment is a separate action after AutoML—incorrect.

D: Feature Selection (e.g., choosing relevant features) is a core AutoML step—correct.

Reasoning: OCI AutoML automates feature selection, algorithm choice, and tuning—D fits.

Conclusion: D is correct.

OCI AutoML’s pipeline includes “feature selection, algorithm selection, adaptive sampling, and hyperparameter tuning,” per the documentation. Extraction (A) isn’t highlighted, while saving (B) and deployment (C) are post-process actions—only Feature Selection (D) is an integral automated step.

Oracle Cloud Infrastructure Data Science Documentation, "AutoML Pipeline".

Detailed Answer in Step-by-Step Solution:

Objective: Identify the key difference between supervised and unsupervised learning.

Define Types:

Supervised: Uses labeled data (e.g., input-output pairs) to predict outcomes.

Unsupervised: Uses unlabeled data to find patterns (e.g., clustering).

Evaluate Options:

A: Labeled vs. unlabeled—Core distinction, correct.

B: Monitoring—Misleading, not the primary difference.

C: Image recognition—False, supervised applies broadly.

D: Data Engineer—Irrelevant to learning type.

Reasoning: A captures the foundational data difference.

Conclusion: A is correct.

OCI documentation states: “Supervised learning uses labeled data to train models for prediction, while unsupervised learning analyzes unlabeled data to discover patterns.” B, C, and D misrepresent this—only A aligns with OCI’s ML definitions and industry standards.

Oracle Cloud Infrastructure Data Science Documentation, "Machine Learning Types".