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Oracle 1Z0-1110-26 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| OCI Data Science - Introduction & Configuration | 10% | - Tenancy and environment configuration for Data Science - Capabilities of the Accelerated Data Science (ADS) SDK - Overview and core concepts of OCI Data Science |
| Design and Set Up Data Science Workspace | 15% | - Create and manage projects and notebook sessions - Manage access control, security, and IAM integration - Configure compute shapes, storage, and networking |
| Apply MLOps Practices | 20% | - Model monitoring, drift detection, and performance tracking - Governance, auditing, and compliance - ML pipelines, automation, and reproducibility |
| Implement End-to-End Machine Learning Lifecycle | 45% | - Use AutoML and built-in algorithms - Model development, training, and evaluation - Model saving, cataloging, and versioning - Data preparation, exploration, and transformation - Deploy models and manage endpoints |
| Integrate Related OCI Services | 10% | - Use OCI AI and data services with Data Science - Integration with OCI Object Storage, Vault, and Networking |
Oracle Cloud Infrastructure Data Science Professional Sample Questions:
Question #1
True or false? Bias is a common problem in data science applications.
A. True
B. False
Question #2
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. Retrain the model
B. Operational monitoring
C. Redeploy the model
D. Validate the model using recent data
E. Drift monitoring
Question #3
Arrange the following in the correct Git Repository workflow order:
Install, configure, and authenticate Git.
Configure SSH keys for the Git repository.
Create a local and remote Git repository.
Commit files to the local Git repository.
Push the commit to the remote Git repository.
A. 4, 2, 3, 1, 5
B. 2, 3, 1, 4, 5
C. 1, 2, 3, 4, 5
D. 3, 5, 1, 2, 4
Question #4
Which OCI service provides a scalable environment for developers and data scientists to run Apache Spark applications at scale?
A. Data Labeling
B. Anomaly Detection
C. Data Flow
D. Data Science
Question #5
Which stage in the machine learning life cycle helps in identifying the imbalance present in the data?
A. Data Access
B. Data Monitoring
C. Data Modeling
D. Data Exploration
Solutions:
| Question #1 Answer: A | Question #2 Answer: A,E | Question #3 Answer: C | Question #4 Answer: C | Question #5 Answer: D |
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