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1z0-1110-25 Reliable Test Sims - Latest 1z0-1110-25 Learning Material

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Oracle 1z0-1110-25 Exam Syllabus Topics:

Topic
Details

Topic 1

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 2

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.

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

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 5

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.

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Oracle Cloud Infrastructure 2025 Data Science Professional Sample Questions (Q58-Q63):

NEW QUESTION # 58
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 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 labels to an image using semantic segmentation, by drawing multiple bounding boxes to an image
E. Adding multiple labels to an image

Answer: A,B,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.
Oracle Cloud Infrastructure Data Labeling Documentation, "Image Annotation Types".

NEW QUESTION # 59
Which statement is true about origin management in Web Application Firewall (WAF)?

A. Both the statements are false
B. Both the statements are true
C. Only statement A is true
D. Multiple origins can be defined
E. Only statement B is true
F. Only a single origin can be active for a WAF

Answer: B

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Determine truth about WAF origin management.
* Understand WAF: Protects apps by routing traffic via origins.
* Evaluate Statements:
* A: Multiple origins-True; WAF supports this.
* B: Single active origin-True; only one is active per policy.
* Evaluate Options:
* C: B only-False; A is true.
* D: Both false-Incorrect.
* E: Both true-Correct per OCI WAF.
* F: A only-False; B is true.
* Conclusion: E is correct.
OCI documentation states: "WAF allows defining multiple origins (A), but only one origin is active per WAF policy at a time (B)-both are true (E)." C, D, and F misalign-E matches OCI's WAF origin management.
Oracle Cloud Infrastructure WAF Documentation, "Origin Management".

NEW QUESTION # 60
As a data scientist, you are trying to automate a machine learning (ML) workflow and have decided to use Oracle Cloud Infrastructure (OCI) AutoML Pipeline. Which THREE are part of the AutoML Pipeline?

A. Feature Extraction
B. Algorithm Selection
C. Model Deployment
D. Feature Selection
E. Adaptive Sampling

Answer: B,D,E

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Identify three stages in OCI AutoML Pipeline.
* Understand Pipeline: Automates ML steps from data to model training.
* Evaluate Options:
* A: Feature Selection-Selects relevant features-correct.
* B: Adaptive Sampling-Reduces data size-correct.
* C: Model Deployment-Post-pipeline step-incorrect.
* D: Feature Extraction-Not explicit in OCI AutoML-incorrect.
* E: Algorithm Selection-Chooses best model-correct.
* Reasoning: A, B, E are core automated stages; C and D are separate.
* Conclusion: A, B, E are correct.
OCI documentation lists "AutoML Pipeline stages as adaptive sampling (B), feature selection (A), algorithm selection (E), and hyperparameter tuning." Deployment (C) is post-pipeline, and extraction (D) isn't highlighted-only A, B, E are included per OCI's design.
Oracle Cloud Infrastructure AutoML Documentation, "Pipeline Components".

NEW QUESTION # 61
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 auditing
B. Security assessment
C. Data masking
D. Data discovery
E. Data encryption

Answer: C

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 # 62
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.
Oracle Cloud Infrastructure Open Data Documentation, "Overview".

NEW QUESTION # 63
......

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