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| Section | Weight | Objectives |
|---|---|---|
| OCI Data Science - Introduction & Configuration | 10% | - Tenancy and environment configuration for Data Science - Capabilities of the Accelerated Data Science (ADS) SDK - Overview and core concepts of OCI Data Science |
| 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 |
| Design and Set Up Data Science Workspace | 15% | - Create and manage projects and notebook sessions - Configure compute shapes, storage, and networking - Manage access control, security, and IAM integration |
| Implement End-to-End Machine Learning Lifecycle | 45% | - Data preparation, exploration, and transformation - Model saving, cataloging, and versioning - Use AutoML and built-in algorithms - Deploy models and manage endpoints - Model development, training, and evaluation |
>> 1Z0-1110-26 100％시험패스 공부자료 <<
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질문 # 158
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?
정답：B
설명：
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".
질문 # 159
You have received machine learning model training code, without clear information about the optimal shape to run the training. How would you proceed to identify the optimal compute shape for your model training that provides a balanced cost and processing time?
정답：A
설명：
Detailed Answer in Step-by-Step Solution:
Objective: Optimize compute shape for cost and time.
Evaluate Options:
A: Tuning params—Focuses on model, not shape.
B: Strongest shape—Costly, unbalanced.
C: Scale up when utilized—Balances cost/time—correct.
D: Random start—Unsystematic.
Reasoning: C iteratively optimizes based on utilization.
Conclusion: C is correct.
OCI documentation advises: “Start with a small shape, monitor utilization and time (C); scale up if fully utilized until performance stabilizes—optimizes cost and speed.” A misfocuses, B overspends, D lacks method—only C aligns.
1: Oracle Cloud Infrastructure Data Science Documentation, "Compute Shape Optimization".
질문 # 160
Which encryption is used for Oracle Data Science?
정답：C
설명：
Detailed Answer in Step-by-Step Solution:
Objective: Identify encryption standard for OCI Data Science.
Understand OCI Encryption: Applies to data at rest and in transit.
Evaluate Options:
A: AES-256—Industry-standard, OCI default—correct.
B: DES—Outdated, weak—incorrect.
C: TDES—Older, less secure—incorrect.
D: Twofish—Not OCI standard—incorrect.
E: RSA—Asymmetric, not primary for data at rest—incorrect.
Reasoning: AES-256 is OCI’s go-to for Data Science resources.
Conclusion: A is correct.
OCI documentation states: “Data Science services encrypt data at rest using AES-256 (A), ensuring high security for notebooks, jobs, and models.” B, C, D, and E are either outdated or not used—only A matches OCI’s encryption policy.
1: Oracle Cloud Infrastructure Data Science Documentation, "Data Encryption".
질문 # 161
What happens when a notebook session is deactivated?
정답：A
설명：
Detailed Answer in Step-by-Step Solution:
Understand Notebook Sessions: These are OCI compute instances running JupyterLab.
Deactivation Impact: Deactivating stops the session to save costs.
Evaluate Options:
A: False—Costs decrease as compute stops.
B: False—Boot volume data isn’t preserved; block volume data is.
C: True—The compute instance shuts down, halting billing.
D: False—Block volume persists unless explicitly deleted.
Reasoning: Deactivation stops the instance (C), preserving block volume data separately.
Conclusion: C is correct.
The OCI documentation states: “When a notebook session is deactivated, the underlying compute instance stops, and billing for compute resources ceases. Data on the attached block volume is preserved, but the boot volume is not.” A is backwards, B misattributes preservation, and D overstates deletion—only C aligns with the process.
1: Oracle Cloud Infrastructure Data Science Documentation, "Notebook Session Lifecycle".
질문 # 162
While working with Git on Oracle Cloud Infrastructure (OCI) Data Science, you notice that two of the operations are taking more time than the others due to your slow internet speed. Which TWO operations would experience the delay?
정답：B,E
설명：
Detailed Answer in Step-by-Step Solution:
Analyze Git Operations: Identify which depend on internet speed.
Evaluate Options:
A . Staging (git add): Local operation—adds files to the index; no network involved.
B . Updating local repo (git pull): Downloads remote changes—requires internet, slowed by poor connectivity.
C . Pushing changes (git push): Uploads local commits to remote—network-dependent, delayed by slow speed.
D . Committing (git commit): Local snapshot—no network needed.
E . Converting to Git repo (git init): Local initialization—no internet required.
Reasoning: Only B and C involve network transfers, directly impacted by slow internet.
Conclusion: B and C are the correct choices.
Git operations like git pull (B) and git push (C) rely on network communication with a remote repository, such as OCI Code Repository, and are documented as “bandwidth-sensitive” in OCI’s guides. Local actions like staging (A), committing (D), and initializing (E) occur on the user’s machine, unaffected by internet speed. This matches standard Git behavior and OCI’s implementation.
1: Oracle Cloud Infrastructure Data Science Documentation, "Using Git in Notebook Sessions".
질문 # 163
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