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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	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.

Oracle Cloud Infrastructure 2025 Data Science Professional Sample Questions (Q118-Q123):

NEW QUESTION # 118
In machine learning, what is the primary difference between supervised and unsupervised learning?

A. Supervised learning is created and managed by the Data Engineer.
B. Supervised learning involves data that has been labeled and classified, while unsupervised learning data is unlabeled and unclassified.
C. Supervised learning is only used for image recognition, while unsupervised learning can be used for various analytics applications.
D. Supervised learning is monitored closely by data scientists, while they don't play a role inunsupervised learning.

Answer: B

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Identify the key difference between supervised and unsupervised learning.
* Define Types:
* Supervised: Uses labeled data (e.g., input-output pairs) to predict outcomes.
* Unsupervised: Uses unlabeled data to find patterns (e.g., clustering).
* Evaluate Options:
* A: Labeled vs. unlabeled-Core distinction, correct.
* B: Monitoring-Misleading, not the primary difference.
* C: Image recognition-False, supervised applies broadly.
* D: Data Engineer-Irrelevant to learning type.
* Reasoning: A captures the foundational data difference.
* Conclusion: A is correct.
OCI documentation states: "Supervised learning uses labeled data to train models for prediction, while unsupervised learning analyzes unlabeled data to discover patterns." B, C, and D misrepresent this-only A aligns with OCI's ML definitions and industry standards.
Oracle Cloud Infrastructure Data Science Documentation, "Machine Learning Types".

NEW QUESTION # 119
You loaded data into Oracle Cloud Infrastructure (OCI) Data Science. To transform the data, you want to use the Accelerated Data Science (ADS) SDK. When you applied the get_recommendations() tool to the ADSDataset object, it showed you user-detected issues with all the recommended changes to apply to the dataset. Which option should you use to apply all the recommended transformations at once?

A. visualize_transforms()
B. get_transformed_dataset()
C. auto_transform()
D. fit_transform()

Answer: C

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Apply all recommended transformations from get_recommendations() in ADS.
* Understand ADS Tools: get_recommendations() suggests fixes (e.g., missing values).
* Evaluate Options:
* A: Returns transformed data-Not for applying-incorrect.
* B: Sklearn-style, not ADS-specific-incorrect.
* C: auto_transform()-Applies all recommendations-correct.
* D: Visualizes, doesn't apply-incorrect.
* Reasoning: auto_transform() executes the fixes suggested by get_recommendations().
* Conclusion: C is correct.
OCI documentation states: "After get_recommendations() identifies issues, use auto_transform() (C) on the ADSDataset to apply all recommended transformations at once." A retrieves, B is external, D visualizes- only C aligns with OCI's ADS transformation workflow.
Oracle Cloud Infrastructure ADS SDK Documentation, "Data Transformation Methods".

NEW QUESTION # 120
What is feature engineering in machine learning used for?

A. To interpret ML models
B. To transform existing features into new ones
C. To perform parameter tuning
D. To help understand the dataset features

Answer: B

Explanation:
Detailed Answer in Step-by-Step Solution:
* Define Feature Engineering: It's the process of creating or modifying features to improve model performance.
* Evaluate Options:
* A: Parameter tuning adjusts model hyperparameters (e.g., learning rate), not features.
* B: Model interpretation (e.g., SHAP values) explains predictions, not feature creation.
* C: Transforming features (e.g., normalizing, encoding) is the core of feature engineering-correct.
* D: Understanding features occurs during exploration, not engineering.
* Reasoning: Feature engineering directly manipulates data inputs (e.g., converting timestamps to day-of- week), distinct from tuning or interpretation.
* Conclusion: C is the precise definition.
OCI Data Science documentation defines feature engineering as "the process of transforming raw data into features that better represent the underlying problem to the predictive models, resulting in improved model accuracy." Examples include scaling or creating interaction terms, aligning with C. Other options (A, B, D) relate to different ML stages.
Oracle Cloud Infrastructure Data Science Documentation, "Feature Engineering Overview".

NEW QUESTION # 121
Where do calls to stdout and stderr from score.py go in a model deployment?

A. The OCI Cloud Shell, which can be accessed from the console
B. The file that was defined for them on the Virtual Machine (VM)
C. The OCI console
D. The predict log in the Oracle Cloud Infrastructure (OCI) Logging service as defined in the deployment

Answer: D

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Locate score.py output in OCI model deployment.
* Understand Deployment: Logs are centralized in OCI Logging.
* Evaluate Options:
* A: VM file-Not default; requires custom config-incorrect.
* B: Predict log in OCI Logging-Standard destination-correct.
* C: Cloud Shell-Separate tool, not logs-incorrect.
* D: Console-UI, not raw logs-incorrect.
* Reasoning: B aligns with OCI's logging integration.
* Conclusion: B is correct.
OCI documentation states: "score.py stdout and stderr are captured in the predict log within OCI Logging service (B), configured during deployment." A isn't standard, C and D don't receive logs-only B fits OCI's logging setup.
Oracle Cloud Infrastructure Data Science Documentation, "Model Deployment Logging".

NEW QUESTION # 122
You are a data scientist using Oracle AutoML to produce a model and you are evaluating the score metric for the model. Which TWO of the following prevailing metrics would you use for evaluating a multiclass classification model?

A. R-squared
B. F1-score
C. Recall
D. Explained variance score
E. Mean squared error

Answer: B,C

Explanation:
Detailed Answer in Step-by-Step Solution:
* Understand Multiclass Classification: Metrics evaluate how well the model predicts multiple classes.
* Evaluate Metrics:
* A. Mean squared error: Used for regression, not classification.
* B. Explained variance score: Regression metric, not suitable.
* C. Recall: Measures true positive rate per class-key for classification.
* D. F1-score: Balances precision and recall-widely used in multiclass.
* E. R-squared: Regression metric, not applicable.
* Select Two: Recall (C) and F1-score (D) are standard for multiclass classification.
Oracle AutoML supports metrics like recall and F1-score for multiclass classification, as they assess per-class performance and overall precision-recall balance, respectively. Regression metrics (A, B,E) are irrelevant here. (Reference: Oracle Cloud Infrastructure Data Science Documentation, "AutoML Metrics").

NEW QUESTION # 123
......

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1z0-1110-25 Exam Dump | 1z0-1110-25 Related Certifications

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Oracle 1z0-1110-25 Exam Syllabus Topics:

Oracle Cloud Infrastructure 2025 Data Science Professional Sample Questions (Q118-Q123):

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