Last Updated: Aug 10, 2026
No. of Questions: 160 Questions & Answers with Testing Engine
Download Limit: Unlimited
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| Section | Weight | Objectives |
|---|---|---|
| Implement End-to-End Machine Learning Lifecycle | 45% | - Deploy models and manage endpoints - Model development, training, and evaluation - Use AutoML and built-in algorithms - Model saving, cataloging, and versioning - Data preparation, exploration, and transformation |
| Apply MLOps Practices | 20% | - Governance, auditing, and compliance - ML pipelines, automation, and reproducibility - Model monitoring, drift detection, and performance tracking |
| OCI Data Science - Introduction & Configuration | 10% | - Capabilities of the Accelerated Data Science (ADS) SDK - Tenancy and environment configuration for Data Science - Overview and core concepts of OCI Data Science |
| Integrate Related OCI Services | 10% | - Integration with OCI Object Storage, Vault, and Networking - Use OCI AI and data services with 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 |
1. Which two statements are true about published conda environments?
A) Your notebook session acts as the source to share published conda environments with team members
B) You can only create a published conda environment by modifying a Data Science conda environment
C) In addition to service job run environment variables, conda environment variables can be used inData Science Jobs
D) They are curated by Oracle Cloud Infrastructure (OCI) Data Science
E) The odsc conda init command is used to configure the location of published conda environments
2. As a data scientist, you create models for cancer prediction based on mammographic images. The correct identification is very crucial in this case. After evaluating two models, you arrive at the following confusion matrix. Which model would you prefer and why?
Model 1 has Test accuracy is 80% and recall is 70%
Model 2 has Test accuracy is 75% and recall is 85%
A) Model 2, because recall has more impact on predictions in this use case
B) Model 2, because recall is high
C) Model 1, because recall has lesser impact on predictions in this use case
D) Model 1, because the test accuracy is high
3. You are building a model and need input that represents data as morning, afternoon, or evening. However, the data contains a timestamp. What part of the Data Science lifecycle would you be in when creating the new variable?
A) Model validation
B) Data access
C) Model type selection
D) Feature engineering
4. What is the correct definition of Git?
A) Git is a distributed version control system that allows you to track changes made to a set of files.
B) Git is a centralized version control system that allows data scientists and developers to track copious amounts of data.
C) Git is a centralized version control system that allows you to revert to previous versions of files as needed.
D) Git is a distributed version control system that protects teams from simultaneous repo contributions and merge requests.
5. You are preparing a configuration object necessary to create a Data Flow application. Which THREE parameter values should you provide?
A) The path to the archive.zip file
B) The local path to your PySpark script
C) The display name of the application
D) The compartment of the Data Flow application
E) The bucket used to read/write the PySpark script in Object Storage
Solutions:
| Question # 1 Answer: C,E | Question # 2 Answer: A | Question # 3 Answer: D | Question # 4 Answer: A | Question # 5 Answer: C,D,E |
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