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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	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 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	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 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 (Q51-Q56):

NEW QUESTION # 51
You want to make your model more frugal to reduce the cost of collecting and processing data. You plan to do this by removing features that are highly correlated. You would like to create a heatmap that displays the correlation so that you can identify candidate features to remove. Which Accelerated Data Science (ADS) SDK method is appropriate to display the comparability between Continuous and Categorical features?

A. pearson_plot()
B. correlation_ratio_plot()
C. cramersv_plot()
D. corr()

Answer: B

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Visualize correlation between continuous and categorical features.
* Evaluate Options:
* A: Pearson-Continuous vs. continuous-incorrect.
* B: Cramer's V-Categorical vs. categorical-incorrect.
* C: Correlation ratio-Continuous vs. categorical-correct.
* D: General correlation-Not specific to mixed types.
* Reasoning: Correlation ratio handles mixed feature types for heatmaps.
* Conclusion: C is correct.
OCI documentation states: "correlation_ratio_plot() (C) in ADS SDK visualizes correlations between continuous and categorical features, ideal for mixed-type heatmaps." Pearson (A) and Cramer's (B) are type- specific, corr() (D) is broad-only C fits per ADS capabilities.
Oracle Cloud Infrastructure ADS SDK Documentation, "Correlation Visualization".

NEW QUESTION # 52
Why is data sampling useful for data scientists?

A. It enables them to use a representative subset of data to build accurate analytical models more quickly.
B. It reduces the amount of data storage space that's required for data science applications.
C. It lets them analyze datasets in small batches to reduce their use of system resources.

Answer: A

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Determine the primary benefit of data sampling.
* Define Sampling: Selecting a subset of data to represent the whole-used in ML/statistics.
* Evaluate Options:
* A: Small batches reduce resources-True but not the main purpose.
* B: Reduces storage-Incidental, not the goal.
* C: Representative subset for faster, accurate models-Core purpose of sampling.
* Reasoning: Sampling speeds up analysis while maintaining accuracy (e.g., training on 10% of data).
* Conclusion: C is correct.
OCI documentation states: "Data sampling allows data scientists to use a representative subset of a large dataset to build accurate models more quickly, especially when processing full datasets is impractical." A focuses on resources (secondary), B on storage (not primary)-only C captures the analytical intent per OCI's AutoML sampling approach.
Oracle Cloud Infrastructure Data Science Documentation, "Data Sampling Techniques".

NEW QUESTION # 53
What do you use the score.py file for?

A. Execute the inference logic code
B. Configure the deployment infrastructure
C. Define the scaling strategy
D. Define the required conda environment

Answer: A

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Determine the purpose of score.py in OCI Data Science model deployment.
* Understand Model Deployment: When deploying a model in OCI, artifacts include score.py, runtime.
yaml, etc.
* Evaluate Options:
* A: Infrastructure configuration (e.g., compute shape) is handled by deployment settings, not score.
py.
* B: score.py contains the inference logic (e.g., load_model(), predict())-correct.
* C: Conda environment is defined in runtime.yaml or a requirements file-not score.py.
* D: Scaling (e.g., instance count) is set in deployment configuration-not score.py.
* Reasoning: score.py is the script executed by the deployment endpoint to load the model and make predictions.
* Conclusion: B is the correct purpose.
The OCI Data Science documentation states: "The score.py file is a required artifact for model deployment, containing the inference logic-functions like load_model() to load the model and predict() to generate predictions from input data." Infrastructure (A) and scaling (D) are managed via the OCI Console or SDK, while the environment (C) is specified in runtime.yaml. B is the precise role of score.py in OCI's deployment workflow.
Oracle Cloud Infrastructure Data Science Documentation, "Model Deployment - score.py".

NEW QUESTION # 54
Six months ago, you created and deployed a model that predicts customer churn for a call centre. Initially, it was yielding quality predictions. However, over the last two months, users are questioning the credibility of the predictions. Which TWO methods would you employ to verify the accuracy of the model?

A. Redeploy the model
B. Validate the model using recent data
C. Retrain the model
D. Drift monitoring
E. Operational monitoring

Answer: C,D

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Address declining prediction accuracy and verify model performance.
* Analyze Problem: Degradation over time suggests data drift or model staleness-common ML issues.
* Evaluate Options:
* A. Retrain the model: Uses new data to update the model-fixes accuracy-correct.
* B. Validate with recent data: Tests performance but doesn't fix-diagnostic only.
* C. Drift monitoring: Detects data distribution shifts-verifies cause-correct.
* D. Redeploy the model: Repeats deployment, doesn't address root cause.
* E. Operational monitoring: Tracks infra (e.g., latency), not prediction accuracy.
* Reasoning: C identifies drift (why accuracy dropped), A corrects it-best pair for verification and improvement.
* Conclusion: A and C are correct.
OCI documentation states: "Drift monitoring (C) detects changes in data distribution that impact accuracy, while retraining (A) with new data restores model performance." Validation (B) checks but doesn't fix, redeployment (D) is redundant, and operational monitoring (E) is infra-focused-only A and C align with OCI's model maintenance strategy.
Oracle Cloud Infrastructure Data Science Documentation, "Model Monitoring and Retraining".

NEW QUESTION # 55
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. Predict, deploy, save, verify, prepare model
B. Verify, prepare model, deploy, save, predict
C. Prepare model, verify, save, deploy, predict
D. Prepare model, deploy, verify, save, predict

Answer: C

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 # 56
......

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