The DumpsFree offers three formats of study materials for the Oracle Cloud Infrastructure Data Science Professional (1Z0-1110-26) certification exam preparation. Our product is designed by experts in their respective fields, ensuring that our customers receive the most up-to-date and accurate Oracle 1Z0-1110-26 Exam Questions.
| Section | Weight | Objectives |
|---|---|---|
| Machine Learning Fundamentals | 20% | - Supervised learning
|
| Model Development and Deployment | 30% | - Model training
|
| OCI Data Science Service | 30% | - Model catalog
|
| MLOps and OCI Integration | 20% | - Automation and pipelines
|
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NEW QUESTION # 127
You loaded data into Oracle Cloud Infrastructure (OCI) Data Science. To transform the data, you want to use the Accelerated Data Science (ADS) SDK. When you applied the get_recommendations() tool to the ADSDataset object, it showed you user-detected issues with all the recommended changes to apply to the dataset. Which option should you use to apply all the recommended transformations at once?
Answer: A
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Apply all recommended transformations from get_recommendations() in ADS.
Understand ADS Tools: get_recommendations() suggests fixes (e.g., missing values).
Evaluate Options:
A: Returns transformed data—Not for applying—incorrect.
B: Sklearn-style, not ADS-specific—incorrect.
C: auto_transform()—Applies all recommendations—correct.
D: Visualizes, doesn’t apply—incorrect.
Reasoning: auto_transform() executes the fixes suggested by get_recommendations().
Conclusion: C is correct.
OCI documentation states: “After get_recommendations() identifies issues, use auto_transform() (C) on the ADSDataset to apply all recommended transformations at once.” A retrieves, B is external, D visualizes—only C aligns with OCI’s ADS transformation workflow.
1: Oracle Cloud Infrastructure ADS SDK Documentation, "Data Transformation Methods".
NEW QUESTION # 128
How are datasets exported in the OCI Data Labeling service?
Answer: B
Explanation:
Detailed Answer in Step-by-Step Solution:
Understand OCI Data Labeling Export: After annotation, datasets are exported for ML use.
Check Supported Formats: OCI Data Labeling exports annotations in a structured, machine-readable format.
Evaluate Options:
A: Binary isn’t a standard export format for annotations.
B: XML isn’t used; JSON is preferred for flexibility.
C: Line-delimited JSON is the correct format, aligning with ML workflows.
D: CSV is common but not the default for OCI Data Labeling.
Conclusion: C matches the official export format.
OCI Data Labeling exports annotated datasets as line-delimited JSON files, which store each annotation as a separate JSON object per line, suitable for ML pipelines. This is explicitly stated in the documentation. (Oracle Cloud Infrastructure Data Labeling Service Documentation, "Exporting Datasets").
NEW QUESTION # 129
What is the correct definition of Git?
Answer: D
Explanation:
Detailed Answer in Step-by-Step Solution:
Define Git: Git is a version control system—centralized vs. distributed is key.
Evaluate Options:
A: Incorrect—Git is distributed, not centralized (e.g., SVN is centralized).
B: Correct—Distributed, tracks file changes across local and remote repos.
C: Incorrect—Git allows simultaneous contributions; it manages, not prevents, merges.
D: Incorrect—Centralized is wrong, and “copious data” is vague.
Reasoning: Git’s distributed nature (each user has a full repo copy) and change-tracking are core traits.
Conclusion: B is accurate.
OCI documentation aligns with Git’s official definition: “Git is a distributed version control system that tracks changes to files, enabling collaboration and version history management.” A and D misclassify it as centralized, while C misrepresents merge handling—B captures Git’s essence as used in OCI Data Science.
1: Oracle Cloud Infrastructure Code Repository Documentation, "Git Overview".
NEW QUESTION # 130
You are a computer vision engineer building an image recognition model. You decide to use Oracle Data Labeling to annotate your image dat a. Which of the following THREE are possible ways to annotate an image in Data Labeling?
Answer: B,C,E
Explanation:
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.
1: Oracle Cloud Infrastructure Data Labeling Documentation, "Image Annotation Types".
NEW QUESTION # 131
Which statement about Oracle Cloud Infrastructure Anomaly Detection is true?
Answer: C
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Find a true statement about OCI Anomaly Detection.
Understand Service: Detects anomalies in multivariate data (e.g., time series).
Evaluate Options:
A: False—Accepted types are CSV/JSON, not SQL/Python.
B: Partially true—Focuses on numerical data (e.g., sensors), not text broadly.
C: True—Used for fraud, intrusions, and sensor anomalies (key use cases).
D: False—Trained on customer data only, not general datasets.
Reasoning: C aligns with documented applications; others misalign.
Conclusion: C is correct.
OCI Anomaly Detection documentation states: “The service is designed to detect anomalies in time series data, making it valuable for fraud detection, network intrusion analysis, and sensor discrepancies.” A is incorrect (file formats), B overgeneralizes (numerical focus), and D misstates training data—only C matches the service’s purpose.
1: Oracle Cloud Infrastructure Anomaly Detection Documentation, "Use Cases".
NEW QUESTION # 132
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