Oracle 1Z0-1110-25 Dumps - Pass Oracle Cloud Infrastructure 2025 Data Science Professional Exam in First Attempt 2026

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 OCI service for ML model lifecycle.

Evaluate Options:

A: Data Catalog---Metadata management, not ML.

B: Data Integration---ETL, not ML.

C: Data Science---Full ML lifecycle---correct.

D: Data Flow---Spark processing, not full ML.

Reasoning: C supports building, training, deploying models.

Conclusion: C is correct.

OCI documentation states: ''OCI Data Science (C) provides tools to build, train, and deploy machine learning models in the cloud, including notebooks and model catalog.'' A, B, and D serve other purposes---only C fits the ML lifecycle per OCI's offerings.

: Oracle Cloud Infrastructure Data Science Documentation, 'Service Overview'.

Detailed Answer in Step-by-Step Solution:

Objective: Troubleshoot a 404 authentication error when saving a model using ADS SDK with resource principal.

Understand Resource Principal: Allows notebook sessions to act as principals via dynamic groups and policies---no user credentials needed.

Analyze 404 Error: Indicates an authorization failure---likely missing permissions or misconfigured resource principal.

Evaluate Options:

A: True---Dynamic group must include notebook sessions (e.g., resource.type = 'datasciencenotebooksession') to authenticate.

B: False---Block volume stores artifacts locally, but saving to the catalog is a permission issue, not storage.

C: True---Policy must grant manage data-science-models to the dynamic group for catalog access.

D: False---Service gateway ensures network access, but 404 is auth-related, not connectivity.

E: False---Resource principal uses dynamic group policies, not user group policies.

Reasoning: A (group inclusion) and C (policy permission) are critical for resource principal auth---others are tangential.

Conclusion: A and C are correct.

OCI documentation states: ''To use resource principal with ADS SDK for model catalog operations, ensure (1) a dynamic group includes the notebook session with a matching rule (e.g., all {resource.type = 'datasciencenotebooksession'}) and (2) a policy grants the dynamic group manage data-science-models permissions in the compartment.'' B is unrelated (storage location), D is network-focused, and E applies to user auth---not resource principal. A 404 error flags missing auth, fixed by A and C.

: Oracle Cloud Infrastructure Data Science Documentation, 'Using Resource Principals with ADS SDK'.

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:

Data Access: The first step in any machine learning workflow is accessing the raw data. This involves retrieving the user commute data collected over the past 3 years from the bike-sharing platform's storage system.

Data Exploration: Once data is accessed, it's explored to understand its structure, quality, and patterns (e.g., missing values, distributions). This step helps identify what preprocessing is needed.

Feature Engineering: After understanding the data, features are created or transformed (e.g., commute duration, time of day) to improve model performance. This step precedes feature exploration because you need engineered features to analyze further.

Feature Exploration: This involves analyzing the engineered features (e.g., correlation analysis, importance ranking) to refine them or select the most relevant ones for modeling.

Modeling: Finally, the prepared data and features are used to train and evaluate a machine learning model.

Option C (Data Access, Data Exploration, Feature Engineering, Feature Exploration, Modeling) follows this logical sequence, aligning with standard ML workflows.

The correct order reflects the machine learning lifecycle as outlined in Oracle's OCI Data Science documentation. Data Access is the initial step to retrieve data, followed by Data Exploration to assess it (e.g., using OCI Data Science Notebook Sessions with tools like pandas). Feature Engineering transforms raw data into meaningful inputs, followed by Feature Exploration to analyze feature importance (e.g., using ADS SDK's correlation tools). Modeling is the final step where the model is built and trained. This sequence is consistent with Oracle's recommended practices for building ML models in OCI Data Science (Reference: Oracle Cloud Infrastructure Data Science Service Documentation, 'Machine Learning Lifecycle').