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| Section | Objectives |
|---|---|
| Design and Set Up Data Science Workspace | - Manage notebook sessions and compute resources - Use Accelerated Data Science SDK and open source tools - Create and configure Data Science projects |
| Use Related OCI Services | - Design machine learning solutions for business use cases - Apply OCI services for data ingestion, storage, and processing - Integrate OCI Data and AI services |
| 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 |
| Apply MLOps Practices | - Implement model lifecycle management - Monitor and maintain machine learning models - Use best practices for operationalizing ML solutions |
| Implement End-to-End Machine Learning Lifecycle | - Automate machine learning workflows and pipelines - Save and manage models using Model Catalog - Prepare and manage datasets - Build, train, and evaluate machine learning models - Deploy models and consume model endpoints |
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NEW QUESTION # 98
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?
Answer: B
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Select an ADSTuner strategy to minimize cost and assess model quality.
Understand ADSTuner: Optimizes hyperparameters with configurable search spaces.
Evaluate Options:
A: Detailed—Exhaustive, high cost—incorrect.
B: No search space—False; tuning requires a space.
C: Perfunctory—Quick, low-cost assessment—correct.
D: Dictionary—Defines space but not a strategy.
Reasoning: Perfunctory balances cost and initial quality check.
Conclusion: C is correct.
OCI documentation states: “ADSTuner’s perfunctory strategy (C) performs a quick, low-cost search to assess model quality, ideal for initial tuning.” Detailed (A) is costly, B misstates requirements, and D is a method, not a strategy—only C fits the goal.
1: Oracle Cloud Infrastructure ADS SDK Documentation, "ADSTuner Search Strategies".
NEW QUESTION # 99
You are a computer vision engineer building an image recognition model. You decide to use Oracle Data Labeling to annotate your image dat a. Which of the following THREE are possible ways to annotate an image in Data Labeling?
Answer: A,B,C
NEW QUESTION # 100
Which type of file system does File Storage use?
Answer: D
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Identify the file system type for OCI File Storage.
Understand File Storage: Network-attached storage in OCI.
Evaluate Options:
A: NFSv3—Network File System, used by File Storage—correct.
B: iSCSI—Block storage protocol, not File Storage.
C: Paravirtualized—Virtualization mode, not file system.
D: NVMe SSD—Hardware, not file system.
Reasoning: NFSv3 is OCI File Storage’s protocol.
Conclusion: A is correct.
OCI documentation states: “File Storage uses NFSv3 (A) as its file system protocol, providing shared storage across instances.” B, C, and D are unrelated—only A aligns with OCI’s File Storage design.
1: Oracle Cloud Infrastructure File Storage Documentation, "File System Protocol".
NEW QUESTION # 101
Which CLI command allows the customized conda environment to be shared with co-workers?
Answer: B
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Share a custom conda environment in OCI Data Science.
Understand Commands: OCI provides odsc CLI for environment management.
Evaluate Options:
A: clone duplicates an environment locally—not for sharing.
B: publish uploads the environment to Object Storage for team access—correct.
C: modify doesn’t exist as a standard command.
D: install sets up an environment locally—not for sharing.
Reasoning: Sharing requires publishing to a shared location (Object Storage), which publish achieves.
Conclusion: B is the correct command.
The OCI Data Science CLI documentation states: “Use odsc conda publish to package and upload a custom conda environment to an Object Storage Bucket, making it accessible to other users.” clone (A) is for local duplication, modify (C) isn’t valid, and install (D) is for local setup—not sharing. B is the designated sharing mechanism.
1: Oracle Cloud Infrastructure Data Science CLI Reference, "odsc conda publish".
NEW QUESTION # 102
During a job run, you receive an error message that no space is left on your disk device. To solve the problem, you must increase the size of the job storage. What would be the most efficient way to do this with Data Science Jobs?
Answer: A
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Efficiently increase storage for an OCI Job.
Understand Jobs: Storage (block volume) is set at job creation, not dynamically adjustable.
Evaluate Options:
A: False—Jobs can’t edit storage post-creation; it’s fixed.
B: False—No environment variable adjusts storage size.
C: True—Create a new job with larger storage (e.g., 200 GB) and run it.
D: False—Refactoring code is inefficient compared to increasing storage.
Reasoning: C is the standard OCI process for adjusting resources.
Conclusion: C is correct.
OCI documentation states: “Storage size for a Data Science Job is specified during job creation (e.g., block volume size). To increase it, create a new job with a larger storage configuration and initiate a new run.” Editing (A) isn’t supported, variables (B) don’t apply, and refactoring (D) avoids the issue—only C is efficient.
1: Oracle Cloud Infrastructure Data Science Documentation, "Jobs - Storage Configuration".
NEW QUESTION # 103
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