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Oracle certification 1z0-1110-25 the latest examination questions and answers come out

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Oracle 1z0-1110-25 Exam Syllabus Topics:

Topic
Details

Topic 1

OCI Data Science - Introduction & Configuration: This section of the exam measures the skills of Machine Learning Engineers and covers foundational concepts of Oracle Cloud Infrastructure (OCI) Data Science. It includes an overview of the platform, its architecture, and the capabilities offered by the Accelerated Data Science (ADS) SDK. It also addresses the initial configuration of tenancy and workspace setup to begin data science operations in OCI.

Topic 2

Use Related OCI Services: This final section measures the competence of Machine Learning Engineers in utilizing OCI-integrated services to enhance data science capabilities. It includes creating Spark applications through OCI Data Flow, utilizing the OCI Open Data Service, and integrating other tools to optimize data handling and model execution workflows.

Topic 3

Implement End-to-End Machine Learning Lifecycle: This section evaluates the abilities of Machine Learning Engineers and includes an end-to-end walkthrough of the ML lifecycle within OCI. It involves data acquisition from various sources, data preparation, visualization, profiling, model building with open-source libraries, Oracle AutoML, model evaluation, interpretability with global and local explanations, and deployment using the model catalog.

Topic 4

Create and Manage Projects and Notebook Sessions: This part assesses the skills of Cloud Data Scientists and focuses on setting up and managing projects and notebook sessions within OCI Data Science. It also covers managing Conda environments, integrating OCI Vault for credentials, using Git-based repositories for source code control, and organizing your development environment to support streamlined collaboration and reproducibility.

Topic 5

Apply MLOps Practices: This domain targets the skills of Cloud Data Scientists and focuses on applying MLOps within the OCI ecosystem. It covers the architecture of OCI MLOps, managing custom jobs, leveraging autoscaling for deployed models, monitoring, logging, and automating ML workflows using pipelines to ensure scalable and production-ready deployments.

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Oracle Cloud Infrastructure 2025 Data Science Professional Sample Questions (Q110-Q115):

NEW QUESTION # 110
You have configured the Management Agent on an Oracle Cloud Infrastructure (OCI) Linux instance for log ingestion purposes. Which is a required configuration for OCI Logging Analytics service to collect data from multiple logs of this instance?

A. Entity - Log Association
B. Source - Entity Association
C. Log Group - Source Association
D. Log - Log Group Association

Answer: B

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Identify the required configuration for OCI Logging Analytics to collect logs from an instance.
* Understand Logging Analytics: Collects and analyzes logs from OCI resources via Management Agents.
* Key Concepts:
* Entity: Represents the instance (e.g., Linux VM).
* Source: Defines log locations (e.g., file paths).
* Log Group: Organizes logs for analysis.
* Evaluate Options:
* A: Log-Log Group-Groups logs, not collection setup.
* B: Entity-Log-Links instance to logs, but not source-specific.
* C: Source-Entity-Maps log sources to the instance-correct.
* D: Log Group-Source-Post-collection organization, not ingestion.
* Reasoning: C establishes the link between the instance and its log sources-key for ingestion.
* Conclusion: C is correct.
OCI documentation states: "To collect logs using Logging Analytics, configure a Source-Entity Association (C) to link the Management Agent on the instance (entity) to specific log sources (e.g., file paths)." A and D organize logs post-collection, B is less specific-only C is required for ingestion per OCI's Logging Analytics setup.
Oracle Cloud Infrastructure Logging Analytics Documentation, "Configuring Log Collection".

NEW QUESTION # 111
You want to write a Python script to create a collection of different projects for your data science team. Which Oracle Cloud Infrastructure (OCI) Data Science interface would you use?

A. Mobile App
B. The OCI Software Development Kit (SDK)
C. Command Line Interface (CLI)
D. OCI Console

Answer: B

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Choose an interface for a Python script to manage projects.
* Evaluate Options:
* A: OCI SDK-Python-based, scriptable-correct.
* B: Console-GUI, not scriptable.
* C: CLI-Command-based, not Python-native.
* D: Mobile App-Not for scripting.
* Reasoning: A enables programmatic project creation.
* Conclusion: A is correct.
OCI documentation states: "Use the OCI Python SDK (A) to programmatically manage Data Science resources, like creating projects, via Python scripts." B, C, and D don't support Python scripting-only A fits.
Oracle Cloud Infrastructure SDK Documentation, "Data Science API".

NEW QUESTION # 112
As a data scientist, you require a pipeline to train ML models. When can a pipeline run be initiated?

A. Pipeline can be initiated once it is created.
B. Pipeline can be initiated after the active state.
C. Pipeline can be initiated during the pipeline run state.
D. Pipeline can be initiated before the active state.

Answer: A

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Determine when an OCI Data Science pipeline can start.
* Understand Pipelines: They're workflows with defined steps, executed on demand or scheduled.
* Evaluate Options:
* A: Once created, a pipeline can be run immediately-correct.
* B: "During run state" implies it's already running-illogical.
* C: "After active state" is unclear; pipelines run when triggered, not post-state.
* D: "Before active state" is vague-creation precedes running.
* Reasoning: Pipelines are executable post-creation via UI/CLI-simplest interpretation is A.
* Conclusion: A is correct.
OCI Data Science documentation states: "After a pipeline is created, you can initiate a pipeline run immediately or schedule it using the OCI Console, CLI, or SDK." B, C, and D misalign with this-running starts post-creation (A), not during/after ambiguous states.
Oracle Cloud Infrastructure Data Science Documentation, "Pipelines - Running a Pipeline".

NEW QUESTION # 113
You are a computer vision engineer building an image recognition model. You decide to use Oracle Data Labeling to annotate your image data. Which of the following THREE are possible ways to annotate an image in Data Labeling?

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

Answer: A,C,D

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

NEW QUESTION # 114
What happens when a notebook session is deactivated?

A. The block volume attached to the notebook is permanently deleted
B. The underlying compute instance stops
C. Compute cost increases due to frequent deactivation
D. The data on boot volume is preserved

Answer: B

Explanation:
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".

NEW QUESTION # 115
......

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Lily Wilson Lily Wilson

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