Oracle Oracle Cloud Infrastructure 2025 Data Science Professional 1z0-1110-25 Exam Dumps: Updated Questions & Answers (October 2025)

Detailed Answer in Step-by-Step Solution:

Objective: Differentiate data scientist vs. data engineer roles.

Define Roles:

Data Engineer: Builds pipelines, prepares data.

Data Scientist: Analyzes data, builds models.

Evaluate Options:

A: Engineer preps, scientist analyzes—Correct division.

B: Reverses roles—Incorrect.

C: Overlaps roles—Scientist doesn’t typically build pipelines.

D: Misaligns—Analyst isn’t the focus.

Reasoning: A reflects standard role separation.

Conclusion: A is correct.

OCI documentation notes: “Data engineers focus on collecting and preparing data through pipelines, while data scientists analyze it to derive insights and build models.” A aligns, B inverts, C overcomplicates, and D shifts focus—only A is accurate.

Oracle Cloud Infrastructure Data Science Documentation, "Roles in Data Science".

Detailed Answer in Step-by-Step Solution:

Objective: Determine minimum log storage duration before archiving in Logging Analytics.

Understand Logging Analytics: Logs are active before archival.

Evaluate Options:

A: 60 days—Too long for minimum.

B: 10 days—Too short.

C: 30 days—Standard minimum—correct.

D: 15 days—Not OCI’s default.

Reasoning: 30 days is OCI’s documented minimum active period.

Conclusion: C is correct.

OCI documentation states: “Logs in Logging Analytics remain active for a minimum of 30 days (C) before archiving, ensuring availability for analysis.” B and D are shorter, A is longer—only C matches OCI’s policy.

Oracle Cloud Infrastructure Logging Analytics Documentation, "Log Retention".

Detailed Answer in Step-by-Step Solution:

Objective: Identify the mechanism for sharing and reloading ML models in OCI Data Science.

Evaluate Options:

A. Model provenance: Tracks model origin—informative but not a sharing mechanism.

B. Model taxonomy: Categorizes models (e.g., regression)—not for sharing/loading.

C. Model deployment: Makes models accessible as endpoints, not for notebook reloading.

D. Model catalog: Stores models and artifacts, enabling sharing and loading into sessions.

Reasoning: The Model Catalog is OCI’s centralized repository for saving, sharing, and retrieving models (e.g., via ADS SDK).

Conclusion: D is the correct tool.

The OCI Model Catalog “enables data scientists to save trained models and their artifacts, share them with team members, and load them back into notebook sessions for further use or evaluation.” Provenance (A) and taxonomy (B) are metadata, while deployment (C) serves inference, not notebook access. D is explicitly designed for this purpose.

Oracle Cloud Infrastructure Data Science Documentation, "Model Catalog Usage".

Detailed Answer in Step-by-Step Solution:

Objective: Locate score.py output in OCI model deployment.

Understand Deployment: Logs are centralized in OCI Logging.

Evaluate Options:

A: VM file—Not default; requires custom config—incorrect.

B: Predict log in OCI Logging—Standard destination—correct.

C: Cloud Shell—Separate tool, not logs—incorrect.

D: Console—UI, not raw logs—incorrect.

Reasoning: B aligns with OCI’s logging integration.

Conclusion: B is correct.

OCI documentation states: “score.py stdout and stderr are captured in the predict log within OCI Logging service (B), configured during deployment.” A isn’t standard, C and D don’t receive logs—only B fits OCI’s logging setup.

Oracle Cloud Infrastructure Data Science Documentation, "Model Deployment Logging".

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.

Oracle Cloud Infrastructure Data Science Documentation, "Feature Engineering Techniques".

Detailed Answer in Step-by-Step Solution:

Define OCI Secrets: Secrets are sensitive data (e.g., API keys, passwords) managed securely in OCI.

Evaluate Options:

A: Object Storage is for general data, not secure secret management.

B: Vault is OCI’s service for storing and managing secrets securely.

C: Autonomous Data Warehouse is for analytics, not secret storage.

D: Oracle Databases store data, not OCI-specific secrets.

Reasoning: Vault is purpose-built for secrets with encryption and access control.

Conclusion: B is correct.

OCI Vault “provides a secure, centralized service to store and manage secrets, such as passwords and keys, with encryption at rest and fine-grained access policies.” Object Storage (A), Autonomous Data Warehouse (C), and Oracle Databases (D) serve other purposes—only Vault (B) is designed for secrets per OCI’s security architecture.

Oracle Cloud Infrastructure Vault Documentation, "Secrets Management".

Detailed Answer in Step-by-Step Solution:

Objective: Identify three annotation methods in OCI Data Labeling for images.

Understand Data Labeling: Supports image annotations for ML.

Evaluate Options:

A: Semantic segmentation with boxes—Incorrect; segmentation is pixel-based, not boxes.

B: Single label (classification)—Supported—correct.

C: No bounding boxes—False; boxes are supported.

D: Object detection with boxes—Supported—correct.

E: Multiple labels (multi-label)—Supported—correct.

Reasoning: B (classification), D (detection), E (multi-label) match OCI capabilities.

Conclusion: B, D, E are correct.

OCI documentation states: “Data Labeling supports image annotations via single-label classification (B), object detection with bounding boxes (D), and multi-label classification (E).” A misdefines segmentation, C contradicts support—only B, D, E are valid per OCI’s Data Labeling features.

Oracle Cloud Infrastructure Data Labeling Documentation, "Image Annotation Types".

Detailed Answer in Step-by-Step Solution:

Objective: Identify supported annotation classes in OCI Data Labeling.

Understand Tool: Supports image/text annotations for ML.

Evaluate Options:

A: Object detection—Yes (bounding boxes).

B: Named entity—Text-specific, not primary for images.

C: Classification—Yes (labels for images/text).

D: Key-point—Not listed in OCI docs.

E: Polygonal—Not explicitly supported.

F: Semantic segmentation—Yes (pixel-level).

Reasoning: A, C, F match OCI’s image/text focus.

Conclusion: A, C, F are correct.

OCI Data Labeling supports “object detection (A), classification (C), and semantic segmentation (F) for images and text,” per documentation. B is text-specific, D and E aren’t highlighted—only A, C, F are core classes.

Oracle Cloud Infrastructure Data Labeling Documentation, "Annotation Types".

Detailed Answer in Step-by-Step Solution:

Objective: Identify export formats for OCI Data Labeling.

Understand Export: Annotated data is exported for ML use.

Evaluate Options:

A: CONLL V2003—Text-specific, not OCI-supported.

B: COCO—Standard for image annotations—correct.

C: Proprietary JSON—OCI’s default format—correct.

D: Spacy—NLP format, not supported.

Reasoning: B and C are image-compatible per OCI docs.

Conclusion: B and C are correct.

OCI documentation states: “Data Labeling exports annotations in COCO format (B) for image tasks and a proprietary JSON format (C) specific to the service.” CONLL (A) and Spacy (D) are text/NLP-focused—not supported for OCI’s image annotations.

Oracle Cloud Infrastructure Data Labeling Documentation, "Export Formats".

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.

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