Exam Oracle 1Z0-1110-26 Blueprint - 1Z0-1110-26 Practice Tests

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Oracle 1Z0-1110-26 Exam Syllabus Topics:

Section	Objectives
Implement End-to-End Machine Learning Lifecycle	- Prepare and manage datasets - Automate machine learning workflows and pipelines - Build, train, and evaluate machine learning models - Deploy models and consume model endpoints - Save and manage models using Model Catalog
Apply MLOps Practices	- Use best practices for operationalizing ML solutions - Implement model lifecycle management - Monitor and maintain machine learning models
OCI Data Science - Introduction and Configuration	- Use OCI Data Science notebooks and sessions - Configure and manage Data Science resources - Understand OCI Data Science service concepts and architecture
Design and Set Up Data Science Workspace	- Manage notebook sessions and compute resources - Use Accelerated Data Science SDK and open source tools - Create and configure Data Science projects
Use Related OCI Services	- Design machine learning solutions for business use cases - Apply OCI services for data ingestion, storage, and processing - Integrate OCI Data and AI services

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Oracle Cloud Infrastructure Data Science Professional Sample Questions (Q112-Q117):

NEW QUESTION # 112
You are a researcher who requires access to large datasets. Which OCI service would you use?

A. Oracle Open Data
B. ADW (Autonomous Data Warehouse)
C. OCI Data Science
D. Oracle Databases

Answer: A

Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Find the OCI service for accessing large public datasets.
Evaluate Options:
A: Oracle Databases—General-purpose, not dataset-focused.
B: ADW—Analytics warehouse, not a dataset repository.
C: OCI Data Science—ML platform, not a dataset provider.
D: Oracle Open Data—Free, public datasets (e.g., geospatial).
Reasoning: Open Data provides pre-existing large datasets for research.
Conclusion: D is correct.
OCI documentation states: “Oracle Open Data provides free access to large, curated datasets, such as geospatial data, ideal for researchers.” Databases (A) and ADW (B) are for storage/analytics, Data Science (C) is for ML—not datasets—only Open Data (D) fits.
1: Oracle Cloud Infrastructure Open Data Documentation, "Overview".

NEW QUESTION # 113
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 Permutation Importance Explanations
C. Local Interpretable Model-Agnostic Explanations
D. Feature Dependence 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.
1: Oracle Cloud Infrastructure Data Science Documentation, "Model Explanation Techniques".

NEW QUESTION # 114
Select two reasons why it is important to rotate encryption keys when using Oracle Cloud Infrastructure (OCI) Vault to store credentials or other secrets.

A. Key rotation improves encryption efficiency
B. Periodically rotating keys makes it easier to reuse keys
C. Key rotation allows you to encrypt no more than five keys at a time
D. Periodically rotating keys limits the amount of data encrypted by one key version
E. Key rotation reduces risk if a key is ever compromised

Answer: D,E

NEW QUESTION # 115
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?

A. Adding labels to an image using semantic segmentation, by drawing multiple bounding boxes to an image
B. Adding a single label to an image
C. Adding labels to an image by drawing a bounding box to an image is not supported by Data Labeling
D. Adding multiple labels to an image
E. Adding labels to an image using object detection, by drawing bounding boxes to an image

Answer: B,D,E

NEW QUESTION # 116
How are datasets exported in the OCI Data Labeling service?

A. As a binary file
B. As a line-delimited JSON file
C. As a CSV file
D. As an XML file

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 # 117
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