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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

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 3

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 4

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 5

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.

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Oracle Cloud Infrastructure 2025 Data Science Professional Sample Questions (Q70-Q75):

NEW QUESTION # 70
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. Adaptive Sampling
B. Algorithm Selection
C. Feature Selection
D. Model Deployment
E. Feature Extraction

Answer: A,B,C

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 # 71
As a data scientist for a hardware company, you have been asked to predict the revenue demand for the upcoming quarter. You develop a time series forecasting model to analyze the data. Select the correct sequence of steps to predict the revenue demand values for the upcoming quarter.

A. Prepare model, deploy, verify, save, predict
B. Prepare model, verify, save, deploy, predict
C. Verify, prepare model, deploy, save, predict
D. Predict, deploy, save, verify, prepare model

Answer: B

Explanation:
Detailed Answer in Step-by-Step Solution:
* Prepare Model: Build and train the time series model using historical data.
* Verify: Validate the model's accuracy (e.g., using metrics like MAE or RMSE).
* Save: Store the trained model (e.g., in the OCI Model Catalog).
* Deploy: Make the model available for predictions (e.g., via OCI Model Deployment).
* Predict: Generate revenue forecasts for the upcoming quarter.
* Evaluate Options: D follows this logical flow; others (e.g., A starts with "verify" before preparation) don't.
In OCI Data Science, the workflow for time series forecasting involves preparing the model (training), verifying its performance, saving it to the catalog, deploying it, and then predicting. This sequence is standard for ML deployment in OCI, as per the documentation. (Reference: Oracle Cloud Infrastructure Data Science Documentation, "Time Series Forecasting Workflow").

NEW QUESTION # 72
Which components are a part of the OCI Identity and Access Management service?

A. Compute instances
B. Policies
C. VCN
D. Regional subnets

Answer: B

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Identify IAM components in OCI.
* Understand IAM: Manages access via policies, groups, etc.
* Evaluate Options:
* A: Policies-Core IAM component-correct.
* B: Subnets-Networking, not IAM.
* C: Instances-Compute, not IAM.
* D: VCN-Networking, not IAM.
* Reasoning: Only A is an IAM element-others are resources.
* Conclusion: A is correct.
OCI documentation states: "IAM includes components like policies (A), groups, and compartments to control resource access." B, C, and D are infrastructure, not IAM-only A fits per OCI's IAM framework.
Oracle Cloud Infrastructure IAM Documentation, "IAM Components".

NEW QUESTION # 73
Which is NOT a valid OCI Data Science notebook session approach?

A. Run the process directly in the terminal and use Python logging to get updates on the progress of your job
B. Authenticate using your notebook session's resource principal to access other OCI resources. Resource principals provide a more secure way to authenticate to resources compared to the OCI configuration and API approach
C. Avoid having multiple users in the same notebook session due to the possibility of resource contention and write conflicts
D. Ensure you don't execute long-running Python processes in a notebook cell
E. While connecting to data in OCI Object Storage from your notebook session, the best practice is to make a local copy on the device and then upload it to your notebook session block volume

Answer: E

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Identify an invalid notebook session practice.
* Evaluate Options:
* A: Valid-Long runs should use Jobs, not notebooks.
* B: Valid-Terminal runs with logging are supported.
* C: Valid-Multi-user conflicts are a concern.
* D: Invalid-Copying from Object Storage to block volume is unnecessary; direct access is best.
* E: Valid-Resource principals are secure and recommended.
* Reasoning: D contradicts OCI's direct-access efficiency.
* Conclusion: D is incorrect.
OCI documentation advises: "Access data in Object Storage directly from notebook sessions using SDKs or resource principals-avoid local copies (D) unless necessary." A, B, C, and E are best practices-D is inefficient and not standard.
Oracle Cloud Infrastructure Data Science Documentation, "Notebook Session Best Practices".

NEW QUESTION # 74
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. Text classification, Named Entity Recognition (NER)
B. Sentiment Analysis, Named Entity Recognition (NER)
C. Image classification, Named Entity Recognition (NER)
D. Object detection, Text classification

Answer: A

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 # 75
......

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