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Oracle Cloud Infrastructure 2025 Data Science Professional Sample Questions (Q111-Q116):

NEW QUESTION # 111
You are running a pipeline in the OCI Data Science service and want to override some of the pipeline's default settings. Which of the following statements about overriding pipeline defaults is true?

A. Pipeline defaults cannot be overridden once the pipeline has been created.
B. Pipeline defaults can be overridden only during pipeline creation.
C. Pipeline defaults can be overridden only by the Administrator.
D. Pipeline defaults can be overridden before starting the pipeline execution.

Answer: D

Explanation:
Detailed Answer in Step-by-Step Solution:
* Understand OCI Data Science Pipelines: Pipelines automate ML workflows with configurable steps.
* Check Override Mechanism: Defaults (e.g., compute shape, storage) can be modified before execution via the OCI Console, SDK, or CLI.
* Evaluate Options:
* A: False-Overrides can occur post-creation, before running.
* B: False-Any authorized user, not just admins, can override defaults.
* C: True-Settings can be adjusted before execution starts.
* D: False-Defaults can be changed post-creation, pre-execution.
* Conclusion: C is correct as it reflects the flexibility of pipeline configuration.
OCI Data Science Pipelines allow users to override default settings (e.g., compute resources, environment variables) before execution, as noted in the official documentation. This can be done via the UI or programmatically, offering flexibility beyond creation time (A) and without admin-only restrictions (B).
(Reference: Oracle Cloud Infrastructure Data Science Pipelines Documentation, "Configuring Pipelines").

NEW QUESTION # 112
The feature type TechJob has the following registered validators:
* TechJob.validator.register(name='is_tech_job', handler=is_tech_job_default_handler)
* TechJob.validator.register(name='is_tech_job', handler=is_tech_job_open_handler, condition= ('job_family',))
* TechJob.validator.register(name='is_tech_job', handler=is_tech_job_closed_handler, condition= ('job_family': 'IT'))When you run is_tech_job(job_family='Engineering'), what does the feature type validator system do?

A. Execute the is_tech_job_open_handler handler
B. Execute the is_tech_job_default_handler handler
C. Throw an error because the system cannot determine which handler to run
D. Execute the is_tech_job_closed_handler handler

Answer: A

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Determine which validator handler runs for is_tech_job(job_family='Engineering').
* Understand Validator System: Likely ADS SDK-executes handlers based on conditions.
* Analyze Validators:
* Default: is_tech_job_default_handler-No condition, fallback.
* Open: is_tech_job_open_handler-Condition ('job_family',)-requires job_family arg.
* Closed: is_tech_job_closed_handler-Condition ('job_family': 'IT')-requires job_family='IT'.
* Evaluate Call: job_family='Engineering'-Matches job_family presence, not IT.
* Reasoning:
* Open handler applies (tuple condition means arg exists).
* Closed fails (Engineering # IT).
* Default is overridden by specific matches.
* Conclusion: D is correct.
OCI ADS documentation states: "Validators execute the most specific handler matching the condition; for is_tech_job(job_family='Engineering'), is_tech_job_open_handler (D) runs as it matches job_family presence, while is_tech_job_closed_handler (C) requires IT-default (A) is bypassed, no error (B)." Only D fits per ADS validator logic.
Oracle Cloud Infrastructure ADS SDK Documentation, "Feature Type Validators".

NEW QUESTION # 113
You are a data scientist; you use the Oracle Cloud Infrastructure (OCI) Language service to train custom models. Which types of custom models can be trained?

A. Image classification, Named Entity Recognition (NER)
B. Text classification, Named Entity Recognition (NER)
C. Object detection, Text classification
D. Sentiment Analysis, Named Entity Recognition (NER)

Answer: B

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Identify custom model types for OCI Language.
* Understand OCI Language: Focuses on text analysis.
* Evaluate Options:
* A: Image classification-Not text-based, incorrect.
* B: Text classification, NER-Both text tasks-correct.
* C: Sentiment, NER-Sentiment is pretrained, not custom.
* D: Object detection-Image-based, incorrect.
* Reasoning: B aligns with OCI Language's text custom models.
* Conclusion: B is correct.
OCI Language documentation states: "Custom models can be trained for text classification and Named Entity Recognition (NER) using your data." Image tasks (A, D) are for Vision, and sentiment (C) is pretrained- only B fits OCI Language's scope.
Oracle Cloud Infrastructure Language Documentation, "Custom Model Training".

NEW QUESTION # 114
Which Oracle Data Safe feature minimizes the amount of personal data and allows internal test, development, and analytics teams to operate with reduced risk?

A. Data discovery
B. Security assessment
C. Data encryption
D. Data auditing
E. Data masking

Answer: E

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Identify the Data Safe feature that reduces personal data exposure.
* Understand Data Safe: Secures sensitive data in OCI databases.
* Evaluate Options:
* A: Encryption-Protects data, doesn't minimize it.
* B: Assessment-Identifies risks, doesn't alter data.
* C: Masking-Obfuscates personal data (e.g., SSNs)-correct.
* D: Discovery-Locates sensitive data, doesn't reduce it.
* E: Auditing-Tracks access, doesn't minimize data.
* Reasoning: Masking replaces sensitive data, reducing risk for teams-fits goal.
* Conclusion: C is correct.
OCI documentation states: "Data masking (C) in Data Safe transforms sensitive data into anonymized versions, minimizing exposure for test, dev, and analytics use." A protects, B assesses, D finds, E audits- only C reduces data per OCI's Data Safe features.
Oracle Cloud Infrastructure Data Safe Documentation, "Data Masking Overview".

NEW QUESTION # 115
You are a data scientist with a set of text and image files that need annotation, and you want to use Oracle Cloud Infrastructure (OCI) Data Labeling. Which of the following THREE annotation classes are supported by the tool?

A. Object detection
B. Polygonal segmentation
C. Key-point and landmark
D. Semantic segmentation
E. Named entity extraction
F. Classification (single/multi-label)

Answer: A,D,F

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Identify supported annotation classes in OCI Data Labeling.
* Understand Tool: Supports image/text annotations for ML.
* Evaluate Options:
* A: Object detection-Yes (bounding boxes).
* B: Named entity-Text-specific, not primary for images.
* C: Classification-Yes (labels for images/text).
* D: Key-point-Not listed in OCI docs.
* E: Polygonal-Not explicitly supported.
* F: Semantic segmentation-Yes (pixel-level).
* Reasoning: A, C, F match OCI's image/text focus.
* Conclusion: A, C, F are correct.
OCI Data Labeling supports "object detection (A), classification (C), and semantic segmentation (F) for images and text," per documentation. B is text-specific, D and E aren't highlighted-only A, C, F are core classes.
Oracle Cloud Infrastructure Data Labeling Documentation, "Annotation Types".

NEW QUESTION # 116
......

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