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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: OCI Data Science - Introduction & Configuration | 10% | - Capabilities of the Accelerated Data Science (ADS) SDK - Overview and core concepts of OCI Data Science - Tenancy and environment configuration for Data Science |
| Topic 2: Implement End-to-End Machine Learning Lifecycle | 45% | - Model development, training, and evaluation - Deploy models and manage endpoints - Model saving, cataloging, and versioning - Use AutoML and built-in algorithms - Data preparation, exploration, and transformation |
| Topic 3: 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 |
| Topic 4: Integrate Related OCI Services | 10% | - Use OCI AI and data services with Data Science - Integration with OCI Object Storage, Vault, and Networking |
| Topic 5: Apply MLOps Practices | 20% | - Model monitoring, drift detection, and performance tracking - ML pipelines, automation, and reproducibility - Governance, auditing, and compliance |
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NEW QUESTION # 20
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 # 21
Which components are a part of the OCI Identity and Access Management service?
Answer: D
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 # 22
Which is NOT a valid OCI Data Science notebook session approach?
Answer: B
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Identify an invalid notebook session practice.
Evaluate Options:
A: Valid—Long runs should use Jobs, not notebooks.
B: Valid—Terminal runs with logging are supported.
C: Valid—Multi-user conflicts are a concern.
D: Invalid—Copying from Object Storage to block volume is unnecessary; direct access is best.
E: Valid—Resource principals are secure and recommended.
Reasoning: D contradicts OCI’s direct-access efficiency.
Conclusion: D is incorrect.
OCI documentation advises: “Access data in Object Storage directly from notebook sessions using SDKs or resource principals—avoid local copies (D) unless necessary.” A, B, C, and E are best practices—D is inefficient and not standard.
1: Oracle Cloud Infrastructure Data Science Documentation, "Notebook Session Best Practices".
NEW QUESTION # 23
After you have created and opened a notebook session, you want to use the Accelerated Data Science (ADS) SDK to access your data and get started with exploratory data analysis. From which TWO places can you access the ADS SDK?
Answer: A,E
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Locate sources for ADS SDK in OCI.
Understand ADS SDK: A Python library for Data Science tasks (e.g., EDA).
Evaluate Options:
A: Big Data Service—Spark-focused, not ADS source.
B: Machine Learning—Separate service, not ADS-related.
C: Conda in OCI Data Science—Preinstalled ADS in notebook sessions.
D: PyPI—Public source to install ADS (pip install oracle-ads).
E: ADW—Database, not an SDK source.
Reasoning: C (preinstalled) and D (installable) are practical access points.
Conclusion: C and D are correct.
OCI documentation states: “The ADS SDK is available in OCI Data Science notebook sessions via preinstalled conda environments (C) and can be installed from PyPI (D) using pip install oracle-ads.” Big Data (A), Machine Learning (B), and ADW (E) don’t host ADS—only C and D apply.
1: Oracle Cloud Infrastructure Data Science Documentation, "ADS SDK Installation".
NEW QUESTION # 24
Which OCI service provides a scalable environment for developers and data scientists to run Apache Spark applications at scale?
Answer: C
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Identify the OCI service for scalable Spark applications.
Evaluate Options:
A: Data Science—ML platform, not Spark-focused.
B: Anomaly Detection—Specific ML service, not general Spark.
C: Data Labeling—Annotation tool, not Spark-related.
D: Data Flow—Managed Spark service for big data.
Reasoning: Data Flow is OCI’s Spark execution engine.
Conclusion: D is correct.
OCI Data Flow “provides a fully managed environment to run Apache Spark applications at scale, ideal for data processing and ML tasks.” Data Science (A) supports Spark in notebooks, but Data Flow (D) is the dedicated, scalable solution—B and C are unrelated.
1: Oracle Cloud Infrastructure Data Flow Documentation, "Overview".
NEW QUESTION # 25
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