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| Section | Weight | Objectives |
|---|---|---|
| Implement End-to-End Machine Learning Lifecycle | 45% | - Data preparation, exploration, and transformation - Deploy models and manage endpoints - Model saving, cataloging, and versioning - Use AutoML and built-in algorithms - Model development, training, and evaluation |
| OCI Data Science - Introduction & Configuration | 10% | - Tenancy and environment configuration for Data Science - Overview and core concepts of OCI Data Science - Capabilities of the Accelerated Data Science (ADS) SDK |
| Apply MLOps Practices | 20% | - Governance, auditing, and compliance - Model monitoring, drift detection, and performance tracking - ML pipelines, automation, and reproducibility |
| Integrate Related OCI Services | 10% | - Integration with OCI Object Storage, Vault, and Networking - Use OCI AI and data services with Data Science |
| Design and Set Up Data Science Workspace | 15% | - Manage access control, security, and IAM integration - Configure compute shapes, storage, and networking - Create and manage projects and notebook sessions |
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NEW QUESTION # 83
You are a data scientist leveraging Oracle Cloud Infrastructure (OCI) to create a model and need some additional Python libraries for processing genome sequencing dat a. Which of the following THREE statements are correct with respect to installing additional Python libraries to process the data?
Answer: A,B,C
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Identify correct statements about installing Python libraries in OCI Data Science.
Understand Notebook Sessions: Run in a managed environment with specific permissions.
Evaluate Options:
A: False—No root privileges; users operate as datascience with limited sudo.
B: True—pip install from PyPI works with internet access (e.g., NAT Gateway).
C: False—Yum isn’t available; pip is the primary tool as a normal user.
D: False—Misstated; youcaninstall non-preinstalled libraries—likely a typo (intended opposite).
E: True—Custom repos are supported with proper network config.
Correct Interpretation: Assuming D’s intent was “Youcaninstall…” (common exam error), B, D (corrected), E are true.
Conclusion: B, D (corrected), E are correct.
OCI documentation states: “In notebook sessions, you can install Python libraries from PyPI (B) or private repositories (E) using pip, but root privileges (A) are not granted—users operate asdatascience.” Yum (C) isn’t supported, and D’s phrasing contradicts capability—corrected, it’s true you can install beyond preinstalled. B, D (adjusted), E align with OCI’s flexibility.
1: Oracle Cloud Infrastructure Data Science Documentation, "Installing Libraries in Notebook Sessions".
NEW QUESTION # 84
As a data scientist, you require a pipeline to train ML models. When can a pipeline run be initiated?
Answer: A
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Determine when an OCI Data Science pipeline can start.
Understand Pipelines: They’re workflows with defined steps, executed on demand or scheduled.
Evaluate Options:
A: Once created, a pipeline can be run immediately—correct.
B: “During run state” implies it’s already running—illogical.
C: “After active state” is unclear; pipelines run when triggered, not post-state.
D: “Before active state” is vague—creation precedes running.
Reasoning: Pipelines are executable post-creation via UI/CLI—simplest interpretation is A.
Conclusion: A is correct.
OCI Data Science documentation states: “After a pipeline is created, you can initiate a pipeline run immediately or schedule it using the OCI Console, CLI, or SDK.” B, C, and D misalign with this—running starts post-creation (A), not during/after ambiguous states.
1: Oracle Cloud Infrastructure Data Science Documentation, "Pipelines - Running a Pipeline".
NEW QUESTION # 85
Which components are a part of the OCI Identity and Access Management service?
Answer: C
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Identify IAM components in OCI.
Understand IAM: Manages access via policies, groups, etc.
Evaluate Options:
A: Policies—Core IAM component—correct.
B: Subnets—Networking, not IAM.
C: Instances—Compute, not IAM.
D: VCN—Networking, not IAM.
Reasoning: Only A is an IAM element—others are resources.
Conclusion: A is correct.
OCI documentation states: “IAM includes components like policies (A), groups, and compartments to control resource access.” B, C, and D are infrastructure, not IAM—only A fits per OCI’s IAM framework.
1: Oracle Cloud Infrastructure IAM Documentation, "IAM Components".
NEW QUESTION # 86
What does the Data Science Service template in Oracle Resource Manager (ORM) NOTautomatically create?
Answer: B
Explanation:
Detailed Answer in Step-by-Step Solution:
Understand ORM Template: It automates OCI Data Science setup with predefined configurations.
Evaluate Components:
A: User groups are created for role-based access—automated.
B: Dynamic groups (e.g., for notebook sessions) are included—automated.
C: Individual users require manual creation via IAM—not automated.
D: Basic policies (e.g., access to Data Science resources) are included—automated.
Reasoning: ORM focuses on infrastructure and permissions, not user accounts.
Conclusion: C is the exception.
The OCI Resource Manager template for Data Science “automatically provisions user groups, dynamic groups, and policies for basic use cases,” but “individual users must be created separately in IAM and assigned to groups.” C is the only item not handled by the template, per the documentation.
1: Oracle Cloud Infrastructure Resource Manager Documentation, "Data Science Template".
NEW QUESTION # 87
You want to evaluate the relationship between feature values and target variables. You have a large number of observations having a near uniform distribution and the features are highly correlated. Which model explanation technique should you choose?
Answer: B
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Select an explanation technique for feature-target relationships with correlated features.
Evaluate Options:
A: Permutation—Breaks with high correlation.
B: LIME—Local, not global relationships.
C: Dependence—Not a standard term; vague.
D: ALE—Handles correlation, shows feature effects—correct.
Reasoning: ALE is robust to correlated features, ideal here.
Conclusion: D is correct.
OCI documentation states: “Accumulated Local Effects (ALE) (D) evaluates feature-target relationships, accounting for correlations, unlike permutation importance (A) which falters with high correlation.” B is local, C isn’t defined—only D fits per OCI’s explanation tools.
1: Oracle Cloud Infrastructure Data Science Documentation, "Model Explanation Techniques".
NEW QUESTION # 88
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