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

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: Minimize false alarms in OCI Anomaly Detection.

Understand FAP: False Alarm Probability—lower FAP means fewer false positives.

Evaluate Options:

A: High FAP—Increases false alarms—incorrect.

B: Low FAP—Reduces false alarms—correct.

C: Zero FAP—Unrealistic; risks missing true anomalies.

D: Function—Vague, not a direct setting.

Reasoning: Low FAP balances sensitivity and false positives— aligns with goal.

Conclusion: B is correct.

OCI Anomaly Detection documentation states: “Set a low False Alarm Probability (FAP) to minimize false positives, though too low (e.g., zero) may miss anomalies.” B fits the goal—high (A) increases errors, zero (C) is impractical, and function (D) isn’t specified.

Oracle Cloud Infrastructure Anomaly Detection Documentation, "Configuring FAP".

Detailed Answer in Step-by-Step Solution:

Objective: Spot the invalid OCI IAM policy.

Understand Policies: Use verbs (e.g., use, manage) and resource types.

Evaluate Options:

A: Valid—virtual-network-family is a resource type.

B: Invalid—No data-science-model-sessions resource type; likely meant data-science-models.

C: Valid—data-science-projects is correct.

D: Valid—data-science-family aggregates Data Science resources.

Reasoning: B uses a nonexistent resource type—error in syntax.

Conclusion: B is incorrect.

OCI IAM documentation lists valid resource types: “data-science-models, data-science-projects, data-science-family—but not data-science-model-sessions (B).” A, C, D are correct—B is invalid due to this typo/misunderstanding.

Oracle Cloud Infrastructure IAM Documentation, "Data Science Resource Types".

Detailed Answer in Step-by-Step Solution:

Analyze Git Operations: Identify which depend on internet speed.

Evaluate Options:

A. Staging (git add): Local operation—adds files to the index; no network involved.

B. Updating local repo (git pull): Downloads remote changes—requires internet, slowed by poor connectivity.

C. Pushing changes (git push): Uploads local commits to remote—network-dependent, delayed by slow speed.

D. Committing (git commit): Local snapshot—no network needed.

E. Converting to Git repo (git init): Local initialization—no internet required.

Reasoning: Only B and C involve network transfers, directly impacted by slow internet.

Conclusion: B and C are the correct choices.

Git operations like git pull (B) and git push (C) rely on network communication with a remote repository, such as OCI Code Repository, and are documented as “bandwidth-sensitive” in OCI’s guides. Local actions like staging (A), committing (D), and initializing (E) occur on the user’s machine, unaffected by internet speed. This matches standard Git behavior and OCI’s implementation.

Oracle Cloud Infrastructure Data Science Documentation, "Using Git in Notebook Sessions".

Detailed Answer in Step-by-Step Solution:

Objective: Specify third-party libraries for model deployment.

Understand Artifacts: runtime.yaml defines runtime; score.py handles logic.

Evaluate Options:

A: Not a standard file—incorrect.

B: Inference code—not for dependencies.

C: Defines conda env with dependencies—correct.

D: Pip list—not used in OCI conda deployments.

Reasoning: runtime.yaml points to a conda env with all libraries.

Conclusion: C is correct.

OCI documentation states: “In runtime.yaml, specify the conda environment slug (e.g., ENVIRONMENT_SLUG: custom_env) containing all third-party libraries required by the model.” score.py (B) is for logic, requirements.txt (D) isn’t OCI-standard, and A doesn’t exist—C fixes the issue.

Oracle Cloud Infrastructure Data Science Documentation, "Model Deployment - runtime.yaml".