Last Updated: Jul 30, 2026
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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Implement End-to-End Machine Learning Lifecycle | 45% | - Deploy models and manage endpoints - Use AutoML and built-in algorithms - Model development, training, and evaluation - Data preparation, exploration, and transformation - Model saving, cataloging, and versioning |
| Topic 2: OCI Data Science - Introduction & Configuration | 10% | - Overview and core concepts of OCI Data Science - Capabilities of the Accelerated Data Science (ADS) SDK - Tenancy and environment configuration for Data Science |
| Topic 3: Apply MLOps Practices | 20% | - ML pipelines, automation, and reproducibility - Model monitoring, drift detection, and performance tracking - Governance, auditing, and compliance |
| Topic 4: Design and Set Up Data Science Workspace | 15% | - Manage access control, security, and IAM integration - Configure compute shapes, storage, and networking - Create and manage projects and notebook sessions |
| Topic 5: Integrate Related OCI Services | 10% | - Integration with OCI Object Storage, Vault, and Networking - Use OCI AI and data services with Data Science |
1. You want to make API calls against other OCI services from your instance without configuring user credentials. How would you achieve this?
A) No configuration is required for making API calls
B) Create a dynamic group and add your instance
C) Create a group and add a policy
D) Create a dynamic group and add a policy
2. Which of the following analytical and statistical techniques do data scientists commonly use?
A) All of the above
B) Regression
C) Clustering
D) Classification
3. You have just received a new dataset from a colleague. You want to quickly find out summary information about the dataset, such as the types of features, the total number of observations, and distributions of the dat a. Which Accelerated Data Science (ADS) SDK method from the ADSDataset class would you use?
A) compute()
B) to_xgb()
C) show_corr()
D) show_in_notebook()
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 Science
D) Data Flow
5. Which model has an open-source, open model format that allows you to run machine learning models on different platforms?
A) PyTorch
B) TensorFlow
C) PySpark
D) ONNX
Solutions:
| Question # 1 Answer: D | Question # 2 Answer: A | Question # 3 Answer: D | Question # 4 Answer: D | Question # 5 Answer: D |
Herman
Kerwin
Mick
Prescott
Tab
Wright
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