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

Detailed Answer in Step-by-Step Solution:

Objective: Sequence OCI AutoML pipeline stages.

Stages:

Adaptive sampling: Reduces data size if large.

Feature selection: Picks relevant features.

Algorithm selection: Chooses best model type.

Hyperparameter tuning: Optimizes model params.

Evaluate: C (sampling, features, algorithms, tuning) matches logical flow—data first, then model.

Reasoning: Sampling precedes feature work—standard in OCI.

Conclusion: C is correct.

OCI documentation states: “AutoML pipeline runs 1) adaptive sampling, 2) feature selection, 3) algorithm selection, 4) hyperparameter tuning (C).” Sampling reduces data first, then features and models are optimized—other orders (A, B, D) misalign with OCI’s sequence.

Oracle Cloud Infrastructure AutoML Documentation, "Pipeline Sequence".

Detailed Answer in Step-by-Step Solution:

Objective: Prevent deployment failure due to utilization limits.

Understand Utilization: High load requires capacity or throttling.

Evaluate Options:

A: More instances—Scales horizontally—correct.

B: Delete—Stops service, not a fix—incorrect.

C: Fewer instances—Worsens issue—incorrect.

D: Larger VM—Scales vertically—correct.

E: Reduce bandwidth—Controls load—correct.

Reasoning: A and D increase capacity, E manages demand—effective trio.

Conclusion: A, D, E are correct.

OCI documentation states: “To avoid utilization limits, increase instances (A), use a larger compute shape (D), or reduce load balancer bandwidth (E) to manage request rates.” B stops service, C reduces capacity—only A, D, E align with OCI’s deployment scaling options.

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

Detailed Answer in Step-by-Step Solution:

Objective: Choose an interface for a Python script to manage projects.

Evaluate Options:

A: OCI SDK—Python-based, scriptable—correct.

B: Console—GUI, not scriptable.

C: CLI—Command-based, not Python-native.

D: Mobile App—Not for scripting.

Reasoning: A enables programmatic project creation.

Conclusion: A is correct.

OCI documentation states: “Use the OCI Python SDK (A) to programmatically manage Data Science resources, like creating projects, via Python scripts.” B, C, and D don’t support Python scripting—only A fits.

Oracle Cloud Infrastructure SDK Documentation, "Data Science API".

Detailed Answer in Step-by-Step Solution:

Objective: Share a custom conda environment in OCI Data Science.

Understand Commands: OCI provides odsc CLI for environment management.

Evaluate Options:

A: clone duplicates an environment locally—not for sharing.

B: publish uploads the environment to Object Storage for team access—correct.

C: modify doesn’t exist as a standard command.

D: install sets up an environment locally—not for sharing.

Reasoning: Sharing requires publishing to a shared location (Object Storage), which publish achieves.

Conclusion: B is the correct command.

The OCI Data Science CLI documentation states: “Use odsc conda publish to package and upload a custom conda environment to an Object Storage Bucket, making it accessible to other users.” clone (A) is for local duplication, modify (C) isn’t valid, and install (D) is for local setup—not sharing. B is the designated sharing mechanism.

Oracle Cloud Infrastructure Data Science CLI Reference, "odsc conda publish".

Detailed Answer in Step-by-Step Solution:

Objective: Identify three data formats supported by ADS SDK for loading data.

Understand ADS SDK: Facilitates data loading into notebook sessions via DatasetFactory.

Evaluate Options:

A. DOCX: Not natively supported—requires conversion (e.g., to text).

B. Pandas DataFrame: Supported—core format for data manipulation in ADS.

C. JSON: Supported—common structured data format.

D. Raw Images: Not directly supported—image data needs preprocessing (e.g., via Vision).

E. XML: Supported—parseable structured format.

Reasoning: ADS focuses on tabular/structured data—B, C, E align; A and D require external handling.

Conclusion: B, C, E are correct.

OCI documentation states: “ADS SDK’s DatasetFactory supports loading data from formats like Pandas DataFrames (B), JSON (C), and XML (E), enabling easy integration into notebook sessions.” DOCX (A) isn’t natively handled, and raw images (D) require preprocessing outside ADS—B, C, E match the supported list.

Oracle Cloud Infrastructure ADS SDK Documentation, "Supported Data Formats".

Detailed Answer in Step-by-Step Solution:

Define Feature Engineering: It’s the process of creating or modifying features to improve model performance.

Evaluate Options:

A: Parameter tuning adjusts model hyperparameters (e.g., learning rate), not features.

B: Model interpretation (e.g., SHAP values) explains predictions, not feature creation.

C: Transforming features (e.g., normalizing, encoding) is the core of feature engineering—correct.

D: Understanding features occurs during exploration, not engineering.

Reasoning: Feature engineering directly manipulates data inputs (e.g., converting timestamps to day-of-week), distinct from tuning or interpretation.

Conclusion: C is the precise definition.

OCI Data Science documentation defines feature engineering as “the process of transforming raw data into features that better represent the underlying problem to the predictive models, resulting in improved model accuracy.” Examples include scaling or creating interaction terms, aligning with C. Other options (A, B, D) relate to different ML stages.

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

Detailed Answer in Step-by-Step Solution:

Objective: Identify correct statements about installing Python libraries in OCI Data Science.

Understand Notebook Sessions: Run in a managed environment with specific permissions.

Evaluate Options:

A: False—No root privileges; users operate as datascience with limited sudo.

B: True—pip install from PyPI works with internet access (e.g., NAT Gateway).

C: False—Yum isn’t available; pip is the primary tool as a normal user.

D: False—Misstated; youcaninstall non-preinstalled libraries—likely a typo (intended opposite).

E: True—Custom repos are supported with proper network config.

Correct Interpretation: Assuming D’s intent was “Youcaninstall…” (common exam error), B, D (corrected), E are true.

Conclusion: B, D (corrected), E are correct.

OCI documentation states: “In notebook sessions, you can install Python libraries from PyPI (B) or private repositories (E) using pip, but root privileges (A) are not granted—users operate asdatascience.” Yum (C) isn’t supported, and D’s phrasing contradicts capability—corrected, it’s true you can install beyond preinstalled. B, D (adjusted), E align with OCI’s flexibility.

Oracle Cloud Infrastructure Data Science Documentation, "Installing Libraries in Notebook Sessions".

Detailed Answer in Step-by-Step Solution:

Objective: Select an OCI AI service to sort images by language.

Evaluate Options:

A: Digital Assistant—Chatbots, not image/language processing.

B: Vision—Image analysis (e.g., object detection), not language sorting.

C: Speech—Audio-to-text, not image-based.

D: Language—Text analysis (e.g., language detection) after OCR—correct.

Reasoning: Images need OCR (Vision) then language detection (Language)—D fits the sorting task.

Conclusion: D is correct.

OCI Language “detects and classifies languages in text,” often paired with OCI Vision’s OCR to process document images. Vision (B) extracts text, but Language (D) sorts by language—Digital Assistant (A) and Speech (C) don’t apply. Documentation supports this workflow.

Oracle Cloud Infrastructure Language Documentation, "Language Detection".