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| Section | Objectives |
|---|---|
| Topic 1: OCI Data Science - Introduction and Configuration | - Configure and manage Data Science resources - Use OCI Data Science notebooks and sessions - Understand OCI Data Science service concepts and architecture |
| Topic 2: Implement End-to-End Machine Learning Lifecycle | - Build, train, and evaluate machine learning models - Save and manage models using Model Catalog - Automate machine learning workflows and pipelines - Prepare and manage datasets - Deploy models and consume model endpoints |
| Topic 3: Apply MLOps Practices | - Use best practices for operationalizing ML solutions - Monitor and maintain machine learning models - Implement model lifecycle management |
| Topic 4: Use Related OCI Services | - Apply OCI services for data ingestion, storage, and processing - Design machine learning solutions for business use cases - Integrate OCI Data and AI services |
| Topic 5: 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 |
1. When preparing your model artifact to save it to the Oracle Cloud Infrastructure (OCI) DataScience model catalog, you create a score.py file. What is the purpose of the score.py file?
A) Execute the inference logic code
B) Define the inference server dependencies
C) Configure the deployment infrastructure
D) Define the compute scaling strategy
2. You are working in your notebook session and find that your notebook session does not have enough compute CPU and memory for your workload. How would you scale up your notebook session without losing your work?
A) Ensure your files and environments are written to the block volume storage under the /home/datascience directory, deactivate the notebook session, and activate the notebook with a larger compute shape selected
B) Create a temporary bucket in Object Storage, write all your files and data to Object Storage, delete the notebook session, provision a new notebook session on a larger compute shape, and copy your files and data from your temporary bucket to your new notebook session
C) Download your files and data to your local machine, delete your notebook session, provision a new notebook session on a larger compute shape, and upload your files from your local machine to the new notebook session
D) Deactivate your notebook session, provision a new notebook session on a larger compute shape, and recreate all your file changes
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) Primary Virtual Network Interface Card (VNIC)
B) NAT Gateway with public internet access
C) Service Gateway with private subnet access
D) FastConnect to an on-premises network
4. Which of these options allow the sharing and loading back of ML models into a notebook session?
A) Model catalog
B) Model provenance
C) Model deployment
D) Model taxonomy
5. How can you convert a fixed load balancer to a flexible load balancer?
A) Using the Edit Listener option
B) Use Update Shape workflows
C) There is no way to convert the load balancer
D) Delete the fixed load balancer and create a new one
Solutions:
| Question # 1 Answer: A | Question # 2 Answer: A | Question # 3 Answer: B | Question # 4 Answer: A | Question # 5 Answer: D |
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