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| Section | Weight | Objectives |
|---|---|---|
| Integrate Related OCI Services | 10% | - Integration with OCI Object Storage, Vault, and Networking - Use OCI AI and data services with Data Science |
| Apply MLOps Practices | 20% | - ML pipelines, automation, and reproducibility - Governance, auditing, and compliance - Model monitoring, drift detection, and performance tracking |
| Design and Set Up Data Science Workspace | 15% | - Manage access control, security, and IAM integration - Create and manage projects and notebook sessions - Configure compute shapes, storage, and networking |
| Implement End-to-End Machine Learning Lifecycle | 45% | - Use AutoML and built-in algorithms - Data preparation, exploration, and transformation - Model saving, cataloging, and versioning - Deploy models and manage endpoints - Model development, training, and evaluation |
| 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 |
>> 1Z0-1110-26 Originale Fragen <<
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56. Frage
Which of the following programming languages are most widely used by data scientists?
Antwort: C
Begründung:
Detailed Answer in Step-by-Step Solution:
Objective: Identify top languages for data science.
Evaluate Options:
A: C/C++—Low-level, less common for data tasks.
B: Python (ML, libraries), R (stats), SQL (data)—Industry standards.
C: Java (enterprise), JavaScript (web)—Not data-focused.
Reasoning: B aligns with data science tools (e.g., pandas, ggplot).
Conclusion: B is correct.
OCI documentation highlights “Python, R, and SQL as the most widely used languages in Data Science for modeling, analysis, and data querying.” C/C++ (A) and Java/JS (C) are less prevalent—B matches OCI’s notebook support and industry trends.
1: Oracle Cloud Infrastructure Data Science Documentation, "Supported Languages".
57. Frage
You have built a machine model to predict whether a bank customer is going to default on a loan. You want to use Local Interpretable Model-Agnostic Explanations (LIME) to understand a specific prediction. What is the key idea behind LIME?
Antwort: A
Begründung:
Detailed Answer in Step-by-Step Solution:
Objective: Define LIME’s core concept.
Understand LIME: Explains individual predictions with local surrogate models.
Evaluate Options:
A: Complex global, simple local—Correct LIME principle.
B: Agnosticism—True but not the key idea.
C: Global/local similarity—False.
D: Local vs. global agnosticism—Incorrect distinction.
Reasoning: A captures LIME’s local approximation focus.
Conclusion: A is correct.
OCI documentation notes: “LIME (A) explains predictions by approximating complex global models with simpler local surrogate models around specific instances.” B, C, and D misalign—only A reflects LIME’s foundational idea per OCI’s interpretability tools.
1: Oracle Cloud Infrastructure Data Science Documentation, "Model Interpretability - LIME".
58. Frage
Where do calls to stdout and stderr from score.py go in a model deployment?
Antwort: A
Begründung:
Detailed Answer in Step-by-Step Solution:
Objective: Locate score.py output in OCI model deployment.
Understand Deployment: Logs are centralized in OCI Logging.
Evaluate Options:
A: VM file—Not default; requires custom config—incorrect.
B: Predict log in OCI Logging—Standard destination—correct.
C: Cloud Shell—Separate tool, not logs—incorrect.
D: Console—UI, not raw logs—incorrect.
Reasoning: B aligns with OCI’s logging integration.
Conclusion: B is correct.
OCI documentation states: “score.py stdout and stderr are captured in the predict log within OCI Logging service (B), configured during deployment.” A isn’t standard, C and D don’t receive logs—only B fits OCI’s logging setup.
1: Oracle Cloud Infrastructure Data Science Documentation, "Model Deployment Logging".
59. Frage
You are using a custom application with third-party APIs to manage application and data hosted in an Oracle Cloud Infrastructure (OCI) tenancy. Although your third-party APIs don’t support OCI’s signature-based authentication, you want them to communicate with OCI resources. Which authentication option must you use to ensure this?
Antwort: C
Begründung:
Detailed Answer in Step-by-Step Solution:
Objective: Select an auth method for third-party APIs lacking OCI signature support.
Understand OCI Auth: Typically uses API keys, but alternatives exist for non-standard APIs.
Evaluate Options:
A: Username/password—Not API-friendly, insecure.
B: API Signing Key—Requires signature-based auth, unsupported here.
C: SSH Key—For instance access, not APIs.
D: Auth Token—Simple token for API calls—correct.
Reasoning: Auth Token provides a bearer token for APIs without signature complexity.
Conclusion: D is correct.
OCI documentation states: “For third-party APIs not supporting signature-based authentication, use an Auth Token (D), a secure, revocable token for accessing OCI resources via REST APIs.” A, B, and C don’t fit non-signature scenarios—only D ensures compatibility per OCI’s IAM options.
1: Oracle Cloud Infrastructure IAM Documentation, "Auth Tokens for API Access".
60. Frage
You are working in your notebook session and find that your notebook session does not have enough compute CPU and memory for your workload. How would you scale up your notebook session without losing your work?
Antwort: C
Begründung:
Detailed Answer in Step-by-Step Solution:
Objective: Scale up a notebook session without losing work.
Understand Persistence: Block volume stores session data (e.g., /home/datascience).
Evaluate Options:
A: Recreating work—inefficient, risks loss.
B: Local download/upload—cumbersome, unnecessary.
C: Use block volume persistence, scale up—efficient, preserves work—correct.
D: Object Storage—extra steps, not needed with block volume.
Reasoning: C leverages OCI’s built-in persistence for seamless scaling.
Conclusion: C is correct.
OCI documentation states: “Files in /home/datascience are stored on the block volume. To scale up, deactivate the session, provision a new one with a larger shape, and the block volume persists your work.” A loses data, B and D add complexity—only C is optimal.
1: Oracle Cloud Infrastructure Data Science Documentation, "Scaling Notebook Sessions".
61. Frage
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