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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	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	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	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.

Oracle Cloud Infrastructure 2025 Data Science Professional Sample Questions (Q101-Q106):

NEW QUESTION # 101
You are preparing a configuration object necessary to create a Data Flow application. Which THREE parameter values should you provide?

A. The compartment of the Data Flow application
B. The bucket used to read/write the PySpark script in Object Storage
C. The display name of the application
D. The local path to your PySpark script
E. The path to the archive.zip file

Answer: A,B,C

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Identify three required params for an OCI Data Flow app config.
* Understand Data Flow: Runs Spark apps; needs compartment, storage, and identity.
* Evaluate Options:
* A: Archive path-Optional if script is in Object Storage-incorrect.
* B: Local script path-Not needed; script is uploaded-incorrect.
* C: Compartment-Required for resource scope-correct.
* D: Bucket-Required for script storage/access-correct.
* E: Display name-Required for app identification-correct.
* Reasoning: C, D, E are mandatory metadata for Data Flow creation-script location is specified via bucket.
* Conclusion: C, D, E are correct.
OCI documentation states: "To create a Data Flow application, configure the compartment OCID (C), Object Storage bucket for the PySpark script (D), and a display name (E) in the application object." Local paths (B) or archives (A) are optional or handled separately-only C, D, E are required per OCI's Data Flow API spec.
Oracle Cloud Infrastructure Data Flow Documentation, "Creating Applications".

NEW QUESTION # 102
Which technique can be used for feature engineering in the machine learning lifecycle?

A. K-means clustering
B. Principal Component Analysis (PCA)
C. Support Vector Machines (SVM)
D. Gradient boosting

Answer: B

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Identify a feature engineering technique in ML.
* Understand Feature Engineering: Transforms raw data into model-ready features.
* Evaluate Options:
* A. PCA: Reduces dimensionality-feature engineering-correct.
* B. K-means: Clustering model-not feature engineering.
* C. SVM: Classification model-not feature engineering.
* D. Gradient boosting: Model training-not feature engineering.
* Reasoning: PCA creates new features via transformation-fits definition.
* Conclusion: A is correct.
OCI documentation states: "Feature engineering techniques like Principal Component Analysis (PCA) (A) transform data into new features to enhance model performance." B, C, and D are modeling techniques-only A aligns with OCI's feature engineering stage.
Oracle Cloud Infrastructure Data Science Documentation, "Feature Engineering Techniques".

NEW QUESTION # 103
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. Operational monitoring
B. Redeploy the model
C. Validate the model using recent data
D. Retrain the model
E. Drift monitoring

Answer: D,E

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 # 104
Which OCI service provides a managed Kubernetes service for deploying, scaling, and managing containerized applications?

A. Oracle Cloud Infrastructure Container Registry
B. Oracle Cloud Infrastructure Container Engine for Kubernetes
C. Oracle Cloud Infrastructure Streaming
D. Oracle Cloud Infrastructure Load Balancing

Answer: B

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Identify OCI's managed Kubernetes service.
* Evaluate Options:
* A: Container Registry-Stores images, not Kubernetes.
* B: Load Balancing-Network service, not Kubernetes.
* C: Container Engine (OKE)-Managed Kubernetes-correct.
* D: Streaming-Data streaming, not containers.
* Reasoning: C is OCI's Kubernetes offering-OKE.
* Conclusion: C is correct.
OCI documentation states: "OCI Container Engine for Kubernetes (OKE) (C) provides a managed service to deploy, scale, and manage containerized applications using Kubernetes." A, B, and D serve other purposes- only C matches per OCI's container services.
Oracle Cloud Infrastructure OKE Documentation, "Overview".

NEW QUESTION # 105
Which statement about resource principals is true?

A. A resource principal is a feature of IAM that enables resources to be authorized principal actors.
B. A resource principal is not a secure way to authenticate to resources, compared to the OCI configuration and API key approach.
C. The Data Science service does not provide authentication via a notebook session's or job run's resource principal to access other OCI resources.
D. When you authenticate using a resource principal, you need to create and manage credentials to access OCI resources.

Answer: A

Explanation:
Detailed Answer in Step-by-Step Solution:
* Define Resource Principals: They allow OCI resources (e.g., notebook sessions) to authenticate to other OCI services without user credentials.
* Evaluate Options:
* A: False-Resource principals eliminate manual credential management.
* B: False-They're secure, leveraging IAM policies, not less secure than API keys.
* C: False-Data Science supports resource principals for accessing resources (e.g., Object Storage).
* D: True-Resource principals are an IAM feature authorizing resources as actors.
* Reasoning: D captures the essence of resource principals as an IAM mechanism.
* Conclusion: D is correct.
OCI documentation states: "A resource principal is an IAM feature that enables OCI resources, such as compute instances or notebook sessions, to act as principal actors and authenticate to other OCI services using policies." This refutes A (no credentials needed), B (secure method), and C (supported in Data Science), making D the accurate statement.
Oracle Cloud Infrastructure IAM Documentation, "Resource Principals".

NEW QUESTION # 106
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