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| Section | Weight | Objectives |
|---|---|---|
| Design and Set Up Data Science Workspace | 15% | - Configure compute shapes, storage, and networking - Manage access control, security, and IAM integration - Create and manage projects and notebook sessions |
| OCI Data Science - Introduction & Configuration | 10% | - Overview and core concepts of OCI Data Science - Tenancy and environment configuration for Data Science - Capabilities of the Accelerated Data Science (ADS) SDK |
| Integrate Related OCI Services | 10% | - Use OCI AI and data services with Data Science - Integration with OCI Object Storage, Vault, and Networking |
| Apply MLOps Practices | 20% | - ML pipelines, automation, and reproducibility - Model monitoring, drift detection, and performance tracking - Governance, auditing, and compliance |
| Implement End-to-End Machine Learning Lifecycle | 45% | - Deploy models and manage endpoints - Model development, training, and evaluation - Model saving, cataloging, and versioning - Use AutoML and built-in algorithms - Data preparation, exploration, and transformation |
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NEW QUESTION # 94
Which OCI cloud service lets you centrally manage the encryption keys that protect your data and the secret credentials that you use to securely access resources?
Answer: D
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Identify the OCI service for key and secret management.
Evaluate Options:
A: Data Safe—Database security, not key management.
B: Cloud Guard—Threat detection, not keys.
C: Data Guard—DB replication, not keys.
D: Vault—Key and secret management—correct.
Reasoning: Vault is OCI’s dedicated service for crypto keys and secrets.
Conclusion: D is correct.
OCI documentation states: “OCI Vault (D) centrally manages encryption keys and secrets, securing data and resource access.” A, B, and C serve other purposes—only D matches per OCI’s securityservices.
1: Oracle Cloud Infrastructure Vault Documentation, "Overview".
NEW QUESTION # 95
What is a common maxim about data scientists?
Answer: C
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Identify a widely accepted maxim about data scientists’ time allocation.
Understand Data Science Workflow: Involves data collection, preparation, and analysis—time distribution is key.
Evaluate Options:
A: 80% on finding/preparing, 20% analyzing—Reflects the data wrangling challenge.
B: 80% analyzing, 20% finding/preparing—Inverts the common perception.
C: 80% on failed projects, 20% useful—Pessimistic, not a standard maxim.
Reasoning: Industry consensus (e.g., “80/20 rule”) emphasizes data prep as the bulk of effort due to messy real-world data.
Conclusion: A is correct.
OCI Data Science documentation aligns with industry norms: “Data scientists typically spend 80% of their time finding, cleaning, and preparing data, and 20% on analysis and modeling, due to the complexity of raw data.” B reverses this, and C isn’t supported—only A reflects this widely cited maxim from sources like Forbes and OCI’s practical guidance.
1: Oracle Cloud Infrastructure Data Science Documentation, "Data Science WorkflowOverview".
NEW QUESTION # 96
How can you convert a fixed load balancer to a flexible load balancer?
Answer: B
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Convert fixed to flexible load balancer in OCI.
Understand Load Balancers: Fixed (e.g., 10 Mbps) vs. flexible (dynamic shapes).
Evaluate Options:
A: False—Conversion possible via recreation.
B: Update Shape—For flexible only, not conversion.
C: Delete and recreate—Standard method—correct.
D: Edit Listener—Configures rules, not type.
Reasoning: OCI requires new creation for type change.
Conclusion: C is correct.
OCI documentation states: “To change from a fixed to a flexible load balancer, delete the existing fixed load balancer and create a new flexible one (C)—direct conversion isn’t supported.” A is too absolute, B and D don’t apply—only C matches OCI’s process.
1: Oracle Cloud Infrastructure Load Balancing Documentation, "Changing Load Balancer Type".
NEW QUESTION # 97
You are a data scientist using Oracle AutoML to produce a model and you are evaluating the score metric for the model. Which TWO of the following prevailing metrics would you use for evaluating a multiclass classification model?
Answer: B,C
Explanation:
Detailed Answer in Step-by-Step Solution:
Understand Multiclass Classification: Metrics evaluate how well the model predicts multiple classes.
Evaluate Metrics:
A . Mean squared error: Used for regression, not classification.
B . Explained variance score: Regression metric, not suitable.
C . Recall: Measures true positive rate per class—key for classification.
D . F1-score: Balances precision and recall—widely used in multiclass.
E . R-squared: Regression metric, not applicable.
Select Two: Recall (C) and F1-score (D) are standard for multiclass classification.
Oracle AutoML supports metrics like recall and F1-score for multiclass classification, as they assess per-class performance and overall precision-recall balance, respectively. Regression metrics (A, B,E) are irrelevant here. (Oracle Cloud Infrastructure Data Science Documentation, "AutoML Metrics").
NEW QUESTION # 98
Which activity is NOT a part of the machine learning life cycle?
Answer: A
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Identify which activity isn’t part of the ML lifecycle.
Define ML Lifecycle: Includes data access, preparation, modeling, evaluation, deployment, and monitoring.
Evaluate Options:
A: Database Management (e.g., DBA tasks) is IT-related, not specific to ML workflows.
B: Model Deployment (e.g., serving predictions) is a key ML phase—correctly included.
C: Modeling (e.g., training) is the core of ML—correctly included.
D: Data Access (e.g., retrieving data) is the first ML step—correctly included.
Reasoning: Database management supports infrastructure, not the ML process directly.
Conclusion: A is the outlier.
The OCI Data Science lifecycle includes “data access, exploration, feature engineering, modeling, deployment, and monitoring,” per the documentation. Database Management (A) is a general ITtask (e.g., optimizing Oracle DB), not an ML-specific activity, unlike B, C, and D, which are integral to OCI’s ML pipeline.
1: Oracle Cloud Infrastructure Data Science Documentation, "Machine Learning Lifecycle Overview".
NEW QUESTION # 99
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