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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Apply MLOps Practices | 20% | - ML pipelines, automation, and reproducibility - Governance, auditing, and compliance - Model monitoring, drift detection, and performance tracking |
| Topic 2: Implement End-to-End Machine Learning Lifecycle | 45% | - Model development, training, and evaluation - Model saving, cataloging, and versioning - Deploy models and manage endpoints - Use AutoML and built-in algorithms - Data preparation, exploration, and transformation |
| Topic 3: Design and Set Up Data Science Workspace | 15% | - Create and manage projects and notebook sessions - Manage access control, security, and IAM integration - Configure compute shapes, storage, and networking |
| Topic 4: 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 |
| Topic 5: Integrate Related OCI Services | 10% | - Integration with OCI Object Storage, Vault, and Networking - Use OCI AI and data services with Data Science |
>> 1Z0-1110-26 Valid Exam Objectives <<
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NEW QUESTION # 36
Which OCI service provides a managed Kubernetes service for deploying, scaling, and managing containerized applications?
Answer: D
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Identify OCI’s managed Kubernetes service.
Evaluate Options:
A: Container Registry—Stores images, not Kubernetes.
B: Load Balancing—Network service, not Kubernetes.
C: Container Engine (OKE)—Managed Kubernetes—correct.
D: Streaming—Data streaming, not containers.
Reasoning: C is OCI’s Kubernetes offering—OKE.
Conclusion: C is correct.
OCI documentation states: “OCI Container Engine for Kubernetes (OKE) (C) provides a managed service to deploy, scale, and manage containerized applications using Kubernetes.” A, B, and D serve other purposes—only C matches per OCI’s container services.
1: Oracle Cloud Infrastructure OKE Documentation, "Overview".
NEW QUESTION # 37
You have an embarrassingly parallel or distributed batch job on a large amount of data that you consider running using Data Science Jobs. What would be the best approach to run the workload?
Answer: D
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Optimize embarrassingly parallel workload in OCI Jobs.
Evaluate Options:
A: Sequential runs—Inefficient for parallel tasks.
B: Simultaneous runs—Maximizes parallelism—correct.
C: False—Jobs support parallelism.
D: One job per run—Misstates capability, wasteful.
Reasoning: B leverages OCI’s parallel run support.
Conclusion: B is correct.
OCI documentation states: “For embarrassingly parallel tasks, create one Job and launch multiple simultaneous Job Runs (B) to process data efficiently.” A is slow, C is incorrect, and Dovercomplicates—B is the best approach.
1: Oracle Cloud Infrastructure Data Science Documentation, "Parallel Job Execution".
NEW QUESTION # 38
Which CLI command allows the customized conda environment to be shared with co-workers?
Answer: A
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Share a custom conda environment in OCI Data Science.
Understand Commands: OCI provides odsc CLI for environment management.
Evaluate Options:
A: clone duplicates an environment locally—not for sharing.
B: publish uploads the environment to Object Storage for team access—correct.
C: modify doesn’t exist as a standard command.
D: install sets up an environment locally—not for sharing.
Reasoning: Sharing requires publishing to a shared location (Object Storage), which publish achieves.
Conclusion: B is the correct command.
The OCI Data Science CLI documentation states: “Use odsc conda publish to package and upload a custom conda environment to an Object Storage Bucket, making it accessible to other users.” clone (A) is for local duplication, modify (C) isn’t valid, and install (D) is for local setup—not sharing. B is the designated sharing mechanism.
1: Oracle Cloud Infrastructure Data Science CLI Reference, "odsc conda publish".
NEW QUESTION # 39
You have configured the Management Agent on an Oracle Cloud Infrastructure (OCI) Linux instance for log ingestion purposes. Which is a required configuration for OCI Logging Analytics service to collect data from multiple logs of this instance?
Answer: A
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Identify the required configuration for OCI Logging Analytics to collect logs from an instance.
Understand Logging Analytics: Collects and analyzes logs from OCI resources via Management Agents.
Key Concepts:
Entity: Represents the instance (e.g., Linux VM).
Source: Defines log locations (e.g., file paths).
Log Group: Organizes logs for analysis.
Evaluate Options:
A: Log-Log Group—Groups logs, not collection setup.
B: Entity-Log—Links instance to logs, but not source-specific.
C: Source-Entity—Maps log sources to the instance—correct.
D: Log Group-Source—Post-collection organization, not ingestion.
Reasoning: C establishes the link between the instance and its log sources—key for ingestion.
Conclusion: C is correct.
OCI documentation states: “To collect logs using Logging Analytics, configure a Source-Entity Association (C) to link the Management Agent on the instance (entity) to specific log sources (e.g., file paths).” A and D organize logs post-collection, B is less specific—only C is required for ingestion per OCI’s Logging Analytics setup.
1: Oracle Cloud Infrastructure Logging Analytics Documentation, "Configuring Log Collection".
NEW QUESTION # 40
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?
Answer: B
Explanation:
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".
NEW QUESTION # 41
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