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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 |
| Apply MLOps Practices | - Monitor and maintain machine learning models - Implement model lifecycle management - Use best practices for operationalizing ML solutions |
| OCI Data Science - Introduction and Configuration | - Configure and manage Data Science resources - Understand OCI Data Science service concepts and architecture - Use OCI Data Science notebooks and sessions |
| Implement End-to-End Machine Learning Lifecycle | - Deploy models and consume model endpoints - Automate machine learning workflows and pipelines - Build, train, and evaluate machine learning models - Save and manage models using Model Catalog - Prepare and manage datasets |
| 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 |
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NEW QUESTION # 116
You are preparing a configuration object necessary to create a Data Flow application. Which THREE parameter values should you provide?
Answer: A,C,E
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Identify three required params for an OCI Data Flow app config.
Understand Data Flow: Runs Spark apps; needs compartment, storage, and identity.
Evaluate Options:
A: Archive path—Optional if script is in Object Storage—incorrect.
B: Local script path—Not needed; script is uploaded—incorrect.
C: Compartment—Required for resource scope—correct.
D: Bucket—Required for script storage/access—correct.
E: Display name—Required for app identification—correct.
Reasoning: C, D, E are mandatory metadata for Data Flow creation—script location is specified via bucket.
Conclusion: C, D, E are correct.
OCI documentation states: “To create a Data Flow application, configure the compartment OCID (C), Object Storage bucket for the PySpark script (D), and a display name (E) in the application object.” Local paths (B) or archives (A) are optional or handled separately—only C, D, E are required per OCI’s Data Flow API spec.
1: Oracle Cloud Infrastructure Data Flow Documentation, "Creating Applications".
NEW QUESTION # 117
In machine learning, what is the primary difference between supervised and unsupervised learning?
Answer: A
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Identify the key difference between supervised and unsupervised learning.
Define Types:
Supervised: Uses labeled data (e.g., input-output pairs) to predict outcomes.
Unsupervised: Uses unlabeled data to find patterns (e.g., clustering).
Evaluate Options:
A: Labeled vs. unlabeled—Core distinction, correct.
B: Monitoring—Misleading, not the primary difference.
C: Image recognition—False, supervised applies broadly.
D: Data Engineer—Irrelevant to learning type.
Reasoning: A captures the foundational data difference.
Conclusion: A is correct.
OCI documentation states: “Supervised learning uses labeled data to train models for prediction, while unsupervised learning analyzes unlabeled data to discover patterns.” B, C, and D misrepresent this—only A aligns with OCI’s ML definitions and industry standards.
1: Oracle Cloud Infrastructure Data Science Documentation, "Machine Learning Types".
NEW QUESTION # 118
How can you collaborate with team members in OCI Data Science Workspace?
Answer: D
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Determine collaboration method in OCI Data Science (Notebook Sessions).
Evaluate Options:
A: Access control—Possible but not primary collaboration.
B: Version control (e.g., Git)—Standard for code sharing—correct.
C: Shared instance—Not supported; sessions are single-user.
D: Chat/video—Not a feature of OCI Data Science.
Reasoning: B leverages Git for team collaboration—OCI’s recommended method.
Conclusion: B is correct.
OCI documentation states: “Collaborate in Data Science by integrating version control systems like Git (B) with notebook sessions to share code and notebooks.” A is limited, C isn’t feasible, and D isn’t available—only B matches OCI’s collaboration approach.
1: Oracle Cloud Infrastructure Data Science Documentation, "Collaboration with Git".
NEW QUESTION # 119
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: B,C,D
NEW QUESTION # 120
As a data scientist, you use the Oracle Cloud Infrastructure (OCI) Language service to train custommodels. Which types of custom models can be trained?
Answer: C
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Identify custom model types for OCI Language service.
Understand OCI Language: Focuses on text analysis, not images.
Evaluate Options:
A: Image classification—Incorrect; Language is text-based.
B: Text classification, NER—Both text tasks—correct.
C: Sentiment—Pretrained, not custom; NER ok—incorrect.
D: Object detection—Image-based, incorrect.
Reasoning: B aligns with OCI Language’s custom text capabilities.
Conclusion: B is correct.
OCI documentation states: “OCI Language supports training custom models for text classification and Named Entity Recognition (NER) (B) using user data.” A and D involve images, C includes pretrained sentiment—only B matches OCI Language’s custom model scope.
1: Oracle Cloud Infrastructure Language Documentation, "Custom Model Types".
NEW QUESTION # 121
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