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| Section | Objectives |
|---|---|
| Implement End-to-End Machine Learning Lifecycle | - Deploy models and consume model endpoints - Save and manage models using Model Catalog - Prepare and manage datasets - Build, train, and evaluate machine learning models - Automate machine learning workflows and pipelines |
| Design and Set Up Data Science Workspace | - Use Accelerated Data Science SDK and open source tools - Create and configure Data Science projects - Manage notebook sessions and compute resources |
| Apply MLOps Practices | - Monitor and maintain machine learning models - Use best practices for operationalizing ML solutions - Implement model lifecycle management |
| OCI Data Science - Introduction and Configuration | - Understand OCI Data Science service concepts and architecture - Configure and manage Data Science resources - Use OCI Data Science notebooks and sessions |
| Use Related OCI Services | - Integrate OCI Data and AI services - Design machine learning solutions for business use cases - Apply OCI services for data ingestion, storage, and processing |
Question 1
Which step is unique to MLOps, as opposed to DevOps?
A. Continuous deployment
B. Continuous integration
C. Continuous training
D. Continuous delivery
Question 2
What does the Data Science Service template in Oracle Resource Manager (ORM) NOTautomatically create?
A. Required user groups
B. Individual Data Science users
C. Policies for a basic use case
D. Dynamic groups
Question 3
You are a data scientist working inside a notebook session and you attempt to pip install a package from a public repository that is not included in your conda environment. After running this command, you get a network timeout error. What might be missing from your networking configuration?
A. NAT Gateway with public internet access
B. Primary Virtual Network Interface Card (VNIC)
C. Service Gateway with private subnet access
D. FastConnect to an on-premises network
Question 4
You want to use ADSTuner to tune the hyperparameters of a supported model you recently trained. You have just started your search and want to reduce the computational cost as well as assess the quality of the model class that you are using. What is the most appropriate search space strategy to choose?
A. Pass a dictionary that defines a search space
B. Perfunctory
C. Detailed
D. ADSTuner doesn’t need a search space to tune the hyperparameters
Question 5
You have an embarrassingly parallel or distributed batch job on a large amount of data that you consider running using Data Science Jobs. What would be the best approach to run the workload?
A. Create the job in Data Science Jobs and then start the number of simultaneous job runs required for your workload
B. Create the job in Data Science Jobs and start a job run. When it is done, start a new job run until you achieve the number of runs required
C. Create a new job for every job run that you have to run in parallel, because the Data Science Jobs service can have only one job run per job
D. Reconfigure the job run because Data Science Jobs does not support embarrassingly parallel workloads
Solutions:
| Question 1 Answer: C | Question 2 Answer: B | Question 3 Answer: A | Question 4 Answer: B | Question 5 Answer: A |
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