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Oracle 1z0-1110-25 Exam Syllabus Topics:

Topic	Details
Topic 1	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 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	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 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	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.

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Oracle Cloud Infrastructure 2025 Data Science Professional Sample Questions (Q36-Q41):

NEW QUESTION # 36
You are a data scientist; you use the Oracle Cloud Infrastructure (OCI) Language service to train custom models. Which types of custom models can be trained?

A. Sentiment Analysis, Named Entity Recognition (NER)
B. Text classification, Named Entity Recognition (NER)
C. Image classification, Named Entity Recognition (NER)
D. Object detection, Text classification

Answer: B

Explanation:
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".

NEW QUESTION # 37
Which of the following programming languages are most widely used by data scientists?

A. Python, R, and SQL
B. Java and JavaScript
C. C and C++

Answer: A

Explanation:
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".

NEW QUESTION # 38
Which type of firewalls are designed to protect against web application attacks, such as SQL injection and cross-site scripting?

A. Incident firewall
B. Web Application Firewall
C. Stateful inspection firewall
D. Packet filtering firewall

Answer: B

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Identify the firewall type protecting against web app attacks like SQL injection and XSS.
* Understand Firewall Types:
* Stateful Inspection: Tracks connection states, not app-specific.
* Web Application Firewall (WAF): Targets web app vulnerabilities.
* Incident Firewall: Not a recognized term.
* Packet Filtering: Basic packet rules, not app-aware.
* Evaluate Options:
* A: Stateful-General network, not web-specific-incorrect.
* B: WAF-Designed for SQLi, XSS-correct.
* C: Incident-Non-existent-incorrect.
* D: Packet-Low-level, not app-focused-incorrect.
* Reasoning: WAF specializes in web app security-matches requirement.
* Conclusion: B is correct.
OCI documentation states: "Web Application Firewall (WAF) (B) protects against web application attacks like SQL injection and cross-site scripting by inspecting HTTP traffic." A and D handle network-level threats, C isn't real-only B aligns with OCI's WAF purpose.
Oracle Cloud Infrastructure WAF Documentation, "Overview".

NEW QUESTION # 39
You are a data scientist working for a manufacturing company. You have developed a forecasting model to predict the sales demand in the upcoming months. You created a model artifact that contained custom logic requiring third-party libraries. When you deployed the model, it failed to run because you did not include all the third-party dependencies in the model artifact. What file should be modified to include the missing libraries?

A. requirements.txt
B. runtime.yaml
C. model_artifact_validate.py
D. score.py

Answer: B

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Specify third-party libraries for model deployment.
* Understand Artifacts: runtime.yaml defines runtime; score.py handles logic.
* Evaluate Options:
* A: Not a standard file-incorrect.
* B: Inference code-not for dependencies.
* C: Defines conda env with dependencies-correct.
* D: Pip list-not used in OCI conda deployments.
* Reasoning: runtime.yaml points to a conda env with all libraries.
* Conclusion: C is correct.
OCI documentation states: "In runtime.yaml, specify the conda environment slug (e.g., ENVIRONMENT_SLUG: custom_env) containing all third-party libraries required by the model." score.py (B) is for logic, requirements.txt (D) isn't OCI-standard, and A doesn't exist-C fixes the issue.
Oracle Cloud Infrastructure Data Science Documentation, "Model Deployment - runtime.yaml".

NEW QUESTION # 40
You have a dataset with fewer than 1000 observations, and you are using Oracle AutoML to build a classifier.
While visualizing the results of each stage of the Oracle AutoML pipeline, you notice that no visualization has been generated for one of the stages. Which stage is not visualized?

A. Feature selection
B. Hyperparameter tuning
C. Adaptive sampling
D. Algorithm selection

Answer: C

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Identify the non-visualized AutoML stage with small data.
* Understand AutoML Pipeline: Includes sampling, feature/algorithm selection, tuning.
* Evaluate Options:
* A: Feature selection-Visualized (e.g., feature importance).
* B: Algorithm selection-Visualized (e.g., algorithm scores).
* C: Adaptive sampling-Skipped/visualization absent for <1000 rows.
* D: Hyperparameter tuning-Visualized (e.g., trial plots).
* Reasoning: Adaptive sampling optimizes large datasets; small data skips it, omitting visuals.
* Conclusion: C is correct.
OCI AutoML documentation notes: "Adaptive sampling is applied to large datasets (>1000 rows) to reduce size; for smaller datasets, it's skipped, and no visualization is generated." Other stages (A, B,D) produce visuals-only C is absent here.
Oracle Cloud Infrastructure AutoML Documentation, "Pipeline Stages".

NEW QUESTION # 41
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