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Oracle Cloud Infrastructure 2025 Data Science Professional Sample Questions (Q108-Q113):

NEW QUESTION # 108
Which statement is true about standards?

A. They may be audited
B. They are the result of a regulation or contractual requirement or an industry requirement
C. They are methods and instructions on how to maintain or accomplish the directives of the policy
D. They are the foundation of corporate governance

Answer: A

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Identify a true statement about standards in an OCI context (likely governance/security).
* Understand Standards: Rules or benchmarks, often compliance-related.
* Evaluate Options:
* A: Auditable-True; standards are checked for adherence.
* B: Result of requirements-Partially true, but not always.
* C: Methods/instructions-More procedural, not defining standards.
* D: Foundation of governance-Broad, not specific to standards.
* Reasoning: A is universally true-standards face audits (e.g., SOC, ISO).
* Conclusion: A is correct.
OCI documentation notes: "Standards (e.g., security standards) may be audited (A) to ensure compliance with OCI policies or external regulations." B is a source, C describes procedures, D is too vague-only A is consistently true per OCI's compliance framework.
Oracle Cloud Infrastructure Security Documentation, "Compliance and Standards".

NEW QUESTION # 109
As a data scientist, you are tasked with creating a model training job that is expected to take different hyperparameter values on every run. What is the most efficient way to set those parameters with Oracle Data Science Jobs?

A. Create your code to expect different parameters as command-line arguments and create a new job every time you run the code
B. Create a new job by setting the required parameters in your code and create a new job for every code change
C. Create your code to expect different parameters either as environment variables or as command-line arguments, which are set on every job run with different values
D. Create a new job every time you need to run your code and pass the parameters as environment variables

Answer: C

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Efficiently vary hyperparameters in OCI Jobs.
* Evaluate Options:
* A: New job per run-Wastes setup time.
* B: Code changes per job-Inefficient, error-prone.
* C: Flexible params per run-Efficient, reusable-correct.
* D: New job per run-Redundant effort.
* Reasoning: C minimizes job creation, maximizes flexibility.
* Conclusion: C is correct.
OCI documentation states: "For varying hyperparameters, configure a single Job with code accepting environment variables or command-line arguments (C), set per run-most efficient." A and D over-create jobs, B ties params to code-only C optimizes.
Oracle Cloud Infrastructure Data Science Documentation, "Job Parameterization".

NEW QUESTION # 110
What is the primary difference between a data scientist and a data engineer?

A. A data engineer analyzes data after a data scientist collects and prepares it.
B. A data engineer collects and prepares data, and a data scientist then analyzes it.
C. A data engineer creates data flows to be used as templates by the data analyst.
D. A data engineer builds data pipelines and helps prepare data, while a data scientist is responsible for data collection, preparation, and analysis.

Answer: B

Explanation:
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".

NEW QUESTION # 111
You want to evaluate the relationship between feature values and target variables. You have a large number of observations having a near uniform distribution and the features are highly correlated. Which model explanation technique should you choose?

A. Accumulated Local Effects
B. Feature Dependence Explanations
C. Local Interpretable Model-Agnostic Explanations
D. Feature Permutation Importance Explanations

Answer: A

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Select an explanation technique for feature-target relationships with correlated features.
* Evaluate Options:
* A: Permutation-Breaks with high correlation.
* B: LIME-Local, not global relationships.
* C: Dependence-Not a standard term; vague.
* D: ALE-Handles correlation, shows feature effects-correct.
* Reasoning: ALE is robust to correlated features, ideal here.
* Conclusion: D is correct.
OCI documentation states: "Accumulated Local Effects (ALE) (D) evaluates feature-target relationships, accounting for correlations, unlike permutation importance (A) which falters with high correlation." B is local, C isn't defined-only D fits per OCI's explanation tools.
Oracle Cloud Infrastructure Data Science Documentation, "Model Explanation Techniques".

NEW QUESTION # 112
You are a data scientist working for a manufacturing company. You have developed a forecasting model to predict the sales demand in the upcoming months. You created a model artifact that contained custom logic requiring third-party libraries. When you deployed the model, it failed to run because you did not include all the third-party dependencies in the model artifact. What file should be modified to include the missing libraries?

A. score.py
B. model_artifact_validate.py
C. requirements.txt
D. runtime.yaml

Answer: D

Explanation:
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".

NEW QUESTION # 113
......

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