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Oracle 1z0-1110-25 Exam Syllabus Topics:

Topic
Details

Topic 1

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 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

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 4

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 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 (Q93-Q98):

NEW QUESTION # 93
You are a data scientist building a pipeline in the Oracle Cloud Infrastructure (OCI) Data Science service for your machine learning project. You want to optimize the pipeline completion time by running some steps in parallel. Which statement is true about running pipeline steps in parallel?

A. All pipeline steps are always run in parallel
B. Pipeline steps can be run in sequence or in parallel, as long as they create a directed acyclic graph (DAG)
C. Parallel steps cannot be run if they are completely independent of each other
D. Steps in a pipeline can be run only sequentially

Answer: B

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Understand parallel execution in OCI Pipelines.
* Evaluate Options:
* A: False-Pipelines support parallelism.
* B: True-DAG allows sequential/parallel steps-correct.
* C: False-Not all steps must be parallel.
* D: False-Independence enables parallelism.
* Reasoning: B reflects OCI's DAG-based flexibility.
* Conclusion: B is correct.
OCI documentation states: "Pipeline steps can run sequentially or in parallel, defined by a directedacyclic graph (DAG) (B), allowing optimization of completion time." A, C, and D misrepresent this-only B aligns with OCI's pipeline design.
Oracle Cloud Infrastructure Data Science Documentation, "Pipeline Parallelism".

NEW QUESTION # 94
Which Oracle Cloud Infrastructure (OCI) Data Science policy is invalid?

A. Allow group DataScienceGroup to use data-science-model-sessions in compartment DataScience
B. Allow dynamic-group DataScienceDynamicGroup to manage data-science-projects in compartment DataScience
C. Allow dynamic-group DataScienceDynamicGroup to manage data-science-family in compartment DataScience
D. Allow group DataScienceGroup to use virtual-network-family in compartment DataScience

Answer: A

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Spot the invalid OCI IAM policy.
* Understand Policies: Use verbs (e.g., use, manage) and resource types.
* Evaluate Options:
* A: Valid-virtual-network-family is a resource type.
* B: Invalid-No data-science-model-sessions resource type; likely meant data-science-models.
* C: Valid-data-science-projects is correct.
* D: Valid-data-science-family aggregates Data Science resources.
* Reasoning: B uses a nonexistent resource type-error in syntax.
* Conclusion: B is incorrect.
OCI IAM documentation lists valid resource types: "data-science-models, data-science-projects, data-science- family-but not data-science-model-sessions (B)." A, C, D are correct-B is invalid due to this typo
/misunderstanding.
Oracle Cloud Infrastructure IAM Documentation, "Data Science Resource Types".

NEW QUESTION # 95
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. cramersv_plot()
B. correlation_ratio_plot()
C. corr()
D. pearson_plot()

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 # 96
You have trained three different models on your dataset using Oracle AutoML. You want to visualize the behavior of each of the models, including the baseline model, on the test set. Which class should be used from the Accelerated Data Science (ADS) SDK to visually compare the models?

A. ADSTuner
B. EvaluationMetrics
C. ADSEvaluator
D. ADSExplainer

Answer: C

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Identify the ADS SDK class for visualizing model performance comparison.
* Understand ADS Classes: Each serves a specific ML purpose-visualization requires evaluation tools.
* Evaluate Options:
* A. EvaluationMetrics: Likely a typo-meant EvaluationsMetrics? Not a standalone class for visualization.
* B. ADSEvaluator: Designed to evaluate and visualize model performance (e.g., ROC curves)- correct.
* C. ADSExplainer: Explains model predictions (e.g., SHAP), not comparative visualization.
* D. ADSTuner: Tunes hyperparameters, not for visualization.
* Reasoning: ADSEvaluator provides comparative plots (e.g., precision-recall) for multiple models, including baselines.
* Conclusion: B is correct.
OCI documentation states: "The ADSEvaluator class in ADS SDK (B) enables visualization of model performance metrics, such as ROC curves and confusion matrices, for multiple models on a test set, including baselines." EvaluationMetrics (A) isn't a class, ADSExplainer (C) focuses on interpretability, and ADSTuner (D) is for tuning-only B fits the visualization need per OCI's ADS toolkit.
Oracle Cloud Infrastructure ADS SDK Documentation, "ADSEvaluator Class".

NEW QUESTION # 97
How can you collaborate with team members in OCI Data Science Workspace?

A. By granting access to specific notebooks and files
B. By sharing the workspace instance with other users
C. By using version control systems integrated with the workspace
D. By enabling chat and video conferencing within the workspace

Answer: C

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Determine collaboration method in OCI Data Science (Notebook Sessions).
* Evaluate Options:
* A: Access control-Possible but not primary collaboration.
* B: Version control (e.g., Git)-Standard for code sharing-correct.
* C: Shared instance-Not supported; sessions are single-user.
* D: Chat/video-Not a feature of OCI Data Science.
* Reasoning: B leverages Git for team collaboration-OCI's recommended method.
* Conclusion: B is correct.
OCI documentation states: "Collaborate in Data Science by integrating version control systems like Git (B) with notebook sessions to share code and notebooks." A is limited, C isn't feasible, and D isn't available- only B matches OCI's collaboration approach.
Oracle Cloud Infrastructure Data Science Documentation, "Collaboration with Git".

NEW QUESTION # 98
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