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

Detailed Answer in Step-by-Step Solution:

Objective: Identify top languages for data science.

Evaluate Options:

A: C/C++—Low-level, less common for data tasks.

B: Python (ML, libraries), R (stats), SQL (data)—Industry standards.

C: Java (enterprise), JavaScript (web)—Not data-focused.

Reasoning: B aligns with data science tools (e.g., pandas, ggplot).

Conclusion: B is correct.

OCI documentation highlights “Python, R, and SQL as the most widely used languages in Data Science for modeling, analysis, and data querying.” C/C++ (A) and Java/JS (C) are less prevalent—B matches OCI’s notebook support and industry trends.

Oracle Cloud Infrastructure Data Science Documentation, "Supported Languages".

Detailed Answer in Step-by-Step Solution:

Objective: Identify three annotation methods in OCI Data Labeling for images.

Understand Data Labeling: Supports image annotations for ML.

Evaluate Options:

A: Semantic segmentation with boxes—Incorrect; segmentation is pixel-based, not boxes.

B: Single label (classification)—Supported—correct.

C: No bounding boxes—False; boxes are supported.

D: Object detection with boxes—Supported—correct.

E: Multiple labels (multi-label)—Supported—correct.

Reasoning: B (classification), D (detection), E (multi-label) match OCI capabilities.

Conclusion: B, D, E are correct.

OCI documentation states: “Data Labeling supports image annotations via single-label classification (B), object detection with bounding boxes (D), and multi-label classification (E).” A misdefines segmentation, C contradicts support—only B, D, E are valid per OCI’s Data Labeling features.

Oracle Cloud Infrastructure Data Labeling Documentation, "Image Annotation Types".

Detailed Answer in Step-by-Step Solution:

Objective: Determine minimum log storage duration before archiving in Logging Analytics.

Understand Logging Analytics: Logs are active before archival.

Evaluate Options:

A: 60 days—Too long for minimum.

B: 10 days—Too short.

C: 30 days—Standard minimum—correct.

D: 15 days—Not OCI’s default.

Reasoning: 30 days is OCI’s documented minimum active period.

Conclusion: C is correct.

OCI documentation states: “Logs in Logging Analytics remain active for a minimum of 30 days (C) before archiving, ensuring availability for analysis.” B and D are shorter, A is longer—only C matches OCI’s policy.

Oracle Cloud Infrastructure Logging Analytics Documentation, "Log Retention".

Detailed Answer in Step-by-Step Solution:

Understand Notebook Sessions: These are OCI compute instances running JupyterLab.

Deactivation Impact: Deactivating stops the session to save costs.

Evaluate Options:

A: False—Costs decrease as compute stops.

B: False—Boot volume data isn’t preserved; block volume data is.

C: True—The compute instance shuts down, halting billing.

D: False—Block volume persists unless explicitly deleted.

Reasoning: Deactivation stops the instance (C), preserving block volume data separately.

Conclusion: C is correct.

The OCI documentation states: “When a notebook session is deactivated, the underlying compute instance stops, and billing for compute resources ceases. Data on the attached block volume is preserved, but the boot volume is not.” A is backwards, B misattributes preservation, and D overstates deletion—only C aligns with the process.

Oracle Cloud Infrastructure Data Science Documentation, "Notebook Session Lifecycle".

Detailed Answer in Step-by-Step Solution:

Objective: Classify a PagerDuty notification in OCI Monitoring.

Understand OCI Monitoring: Involves events, rules, and actions for notifications.

Evaluate Options:

A: Action—executes a response (e.g., notify PagerDuty) when triggered—correct.

B: Rule—defines conditions for triggering actions—precedes the action.

C: Function—serverless code, not directly tied to notifications.

D: Event—state change triggering a rule, not the notification itself.

Reasoning: The notification is the action taken after an event/rule—fits A.

Conclusion: A is correct.

OCI documentation states: “Actions in the Monitoring service execute responses, such as sending notifications to PagerDuty, when a rule’s condition is met based on an event.” Rules (B) set conditions, Functions (C) are unrelated, and Events (D) are triggers—only Action (A) describes the notification step.

Oracle Cloud Infrastructure Monitoring Documentation, "Actions Overview".

Detailed Answer in Step-by-Step Solution:

Objective: Find the stage where data imbalance (e.g., skewed classes) is identified.

Understand Stages:

Data Modeling: Training models—assumes data is prepared.

Data Monitoring: Post-deployment tracking—not for initial analysis.

Data Exploration: Analyzing data properties (e.g., distributions)—key for imbalance.

Data Access: Retrieving data—no analysis yet.

Evaluate Options:

A: Modeling uses data, doesn’t detect imbalance—incorrect.

B: Monitoring tracks performance, not initial data issues—incorrect.

C: Exploration (e.g., via pandas) reveals imbalances—correct.

D: Access is just retrieval—incorrect.

Reasoning: Imbalance is assessed during exploration (e.g., class counts).

Conclusion: C is correct.

OCI documentation notes: “Data Exploration involves analyzing the dataset to understand its characteristics, such as identifying class imbalances or missing values, using tools like ADS SDK or Jupyter notebooks.” Modeling (A) and Monitoring (B) occur later, while Access (D) is pre-analysis—only Exploration (C) fits this role.

Oracle Cloud Infrastructure Data Science Documentation, "Data Exploration Stage".

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".