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

Detailed Answer in Step-by-Step Solution:

Objective: Identify the initial data science step.

Define Process: Starts with problem definition, then data and modeling.

Evaluate Options:

A: Data collection—Second step after problem definition.

B: Modeling—Later stage.

C: Hypothesis—Sets the goal, first step—correct.

D: Data owners—Collaboration, not the start.

Reasoning: Hypothesis drives the process (e.g., “Can we predict churn?”).

Conclusion: C is correct.

OCI documentation states: “The data science process begins with defining an analytical hypothesis to address a business problem, followed by data collection and analysis.” C precedes A, B, and D—aligning with OCI’s structured approach.

Oracle Cloud Infrastructure Data Science Documentation, "Data Science Process".

Detailed Answer in Step-by-Step Solution:

Objective: Define the role of score.py in OCI model artifacts.

Understand Artifacts: score.py is key for deployment runtime.

Evaluate Options:

A: Infra config—Handled by OCI settings, not score.py.

B: Inference logic—Correct; runs load_model(), predict().

C: Scaling—Set in deployment, not score.py.

D: Dependencies—In runtime.yaml, not score.py.

Reasoning: B aligns with score.py’s execution role.

Conclusion: B is correct.

OCI documentation states: “score.py (B) contains the inference logic, including functions to load the model and predict outputs, executed by the deployment endpoint.” A, C, and D are managed elsewhere—only B matches OCI’s design.

Oracle Cloud Infrastructure Data Science Documentation, "Model Artifact - score.py".

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: 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: Scale up notebook without losing work.

Evaluate Options:

A: Object Storage—Extra steps, inefficient.

B: Block volume—Persists data, seamless scale—correct.

C: Local machine—Risky, cumbersome.

D: Recreate—Loses work, impractical.

Reasoning: B uses OCI’s built-in persistence.

Conclusion: B is correct.

OCI documentation states: “Files in /home/datascience (B) persist on block volume; deactivate, then reactivate with a larger shape to scale up without data loss.” A, C, and D add complexity or risk—only B is optimal per OCI’s design.

Oracle Cloud Infrastructure Data Science Documentation, "Scaling Notebook Sessions".

Detailed Answer in Step-by-Step Solution:

Objective: Locate sources for ADS SDK in OCI.

Understand ADS SDK: A Python library for Data Science tasks (e.g., EDA).

Evaluate Options:

A: Big Data Service—Spark-focused, not ADS source.

B: Machine Learning—Separate service, not ADS-related.

C: Conda in OCI Data Science—Preinstalled ADS in notebook sessions.

D: PyPI—Public source to install ADS (pip install oracle-ads).

E: ADW—Database, not an SDK source.

Reasoning: C (preinstalled) and D (installable) are practical access points.

Conclusion: C and D are correct.

OCI documentation states: “The ADS SDK is available in OCI Data Science notebook sessions via preinstalled conda environments (C) and can be installed from PyPI (D) using pip install oracle-ads.” Big Data (A), Machine Learning (B), and ADW (E) don’t host ADS—only C and D apply.

Oracle Cloud Infrastructure Data Science Documentation, "ADS SDK Installation".

Detailed Answer in Step-by-Step Solution:

Objective: Identify two OCI Language AI capabilities.

Understand OCI Language: Focuses on text analysis tasks.

Evaluate Options:

A: Table extraction—Vision, not Language—incorrect.

B: Punctuation correction—Not offered—incorrect.

C: Sentence diagramming—Not supported—incorrect.

D: Topic classification—Supported (custom/pretrained)—correct.

E: Sentiment analysis—Supported (pretrained)—correct.

Reasoning: D and E are core text analysis features of OCI Language.

Conclusion: D and E are correct.

OCI documentation states: “OCI Language offers topic classification (D) and sentiment analysis (E) for text analysis, among other features.” A belongs to Vision, B and C aren’t available—only D and E match OCI Language’s capabilities.

Oracle Cloud Infrastructure Language Documentation, "Text Analysis Features".

Detailed Answer in Step-by-Step Solution:

Define Resource Principals: They allow OCI resources (e.g., notebook sessions) to authenticate to other OCI services without user credentials.

Evaluate Options:

A: False—Resource principals eliminate manual credential management.

B: False—They’re secure, leveraging IAM policies, not less secure than API keys.

C: False—Data Science supports resource principals for accessing resources (e.g., Object Storage).

D: True—Resource principals are an IAM feature authorizing resources as actors.

Reasoning: D captures the essence of resource principals as an IAM mechanism.

Conclusion: D is correct.

OCI documentation states: “A resource principal is an IAM feature that enables OCI resources, such as compute instances or notebook sessions, to act as principal actors and authenticate to other OCI services using policies.” This refutes A (no credentials needed), B (secure method), and C (supported in Data Science), making D the accurate statement.

Oracle Cloud Infrastructure IAM Documentation, "Resource Principals".

Detailed Answer in Step-by-Step Solution:

Objective: Identify true statements about published conda environments in OCI.

Understand Published Conda: Custom envs shared via Object Storage.

Evaluate Options:

A: False—Users create them, not OCI-curated.

B: True—odsc conda init sets up access to published envs.

C: False—Object Storage, not notebook, is the source.

D: False—Can create from scratch, not just modifications.

E: True—Conda env vars extend job configs.

Reasoning: B configures access, E enhances flexibility—match OCI behavior.

Conclusion: B and E are correct.

OCI documentation states: “odsc conda init (B) configures the bucket for published conda environments, and Jobs can use conda environment variables (E) alongside service vars.” A misattributes curation, C misplaces source, and D limits creation—only B and E are true per OCI’s conda management.

Oracle Cloud Infrastructure Data Science Documentation, "Published Conda Environments".

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".