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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Integrate Related OCI Services | 10% | - Integration with OCI Object Storage, Vault, and Networking - Use OCI AI and data services with Data Science |
| Topic 2: Implement End-to-End Machine Learning Lifecycle | 45% | - Deploy models and manage endpoints - Use AutoML and built-in algorithms - Data preparation, exploration, and transformation - Model development, training, and evaluation - Model saving, cataloging, and versioning |
| 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% | - Overview and core concepts of OCI Data Science - Tenancy and environment configuration for Data Science - Capabilities of the Accelerated Data Science (ADS) SDK |
| Topic 5: Apply MLOps Practices | 20% | - ML pipelines, automation, and reproducibility - Governance, auditing, and compliance - Model monitoring, drift detection, and performance tracking |
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11. Frage
You are a computer vision engineer building an image recognition model. You decide to use Oracle Data Labeling to annotate your image dat a. Which of the following THREE are possible ways to annotate an image in Data Labeling?
Antwort: B,C,E
Begründung:
Detailed Answer in Step-by-Step Solution:
Objective: Identify three annotation methods in OCI Data Labeling for images.
Understand Data Labeling: Supports image annotations for ML.
Evaluate Options:
A: Semantic segmentation with boxes—Incorrect; segmentation is pixel-based, not boxes.
B: Single label (classification)—Supported—correct.
C: No bounding boxes—False; boxes are supported.
D: Object detection with boxes—Supported—correct.
E: Multiple labels (multi-label)—Supported—correct.
Reasoning: B (classification), D (detection), E (multi-label) match OCI capabilities.
Conclusion: B, D, E are correct.
OCI documentation states: “Data Labeling supports image annotations via single-label classification (B), object detection with bounding boxes (D), and multi-label classification (E).” A misdefines segmentation, C contradicts support—only B, D, E are valid per OCI’s Data Labeling features.
1: Oracle Cloud Infrastructure Data Labeling Documentation, "Image Annotation Types".
12. Frage
You want to write a Python script to create a collection of different projects for your data science team. Which Oracle Cloud Infrastructure (OCI) Data Science interface would you use?
Antwort: D
Begründung:
Detailed Answer in Step-by-Step Solution:
Objective: Choose an interface for a Python script to manage projects.
Evaluate Options:
A: OCI SDK—Python-based, scriptable—correct.
B: Console—GUI, not scriptable.
C: CLI—Command-based, not Python-native.
D: Mobile App—Not for scripting.
Reasoning: A enables programmatic project creation.
Conclusion: A is correct.
OCI documentation states: “Use the OCI Python SDK (A) to programmatically manage Data Science resources, like creating projects, via Python scripts.” B, C, and D don’t support Python scripting—only A fits.
1: Oracle Cloud Infrastructure SDK Documentation, "Data Science API".
13. Frage
You are a data scientist working for a utilities company. You have developed an algorithm that detects anomalies from a utility reader in the grid. The size of the model artifact is about 2 GB, and you are trying to store it in the model catalog. Which THREE interfaces could you use to save the model artifact into the model catalog?
Antwort: A,B,C
Begründung:
Detailed Answer in Step-by-Step Solution:
Objective: Identify interfaces to save a 2 GB model to the Model Catalog.
Evaluate Options:
A: OCI CLI—Supports Data Science tasks—possible but not primary.
B: ADS SDK—Designed for model catalog ops—correct.
C: ODSC CLI—Not standard; likely typo for OCI CLI.
D: Console—GUI for catalog uploads—correct.
E: OCI Python SDK—Programmatic catalog access—correct.
F: Git CLI—Version control, not catalog-related.
Reasoning: B, D, E are OCI’s primary interfaces; A is valid but less emphasized.
Conclusion: B, D, E are correct (A plausible but not top-tier).
OCI documentation lists “ADS SDK (B), OCI Console (D), and OCI Python SDK (E) as primary methods to save models to the Model Catalog.” OCI CLI (A) works but isn’t highlighted, C isn’t real, and F is unrelated—B, D, E are the standard trio.
1: Oracle Cloud Infrastructure Data Science Documentation, "Model Catalog Interfaces".
14. Frage
Which statement about Oracle Cloud Infrastructure Anomaly Detection is true?
Antwort: D
Begründung:
Detailed Answer in Step-by-Step Solution:
Objective: Find a true statement about OCI Anomaly Detection.
Understand Service: Detects anomalies in multivariate data (e.g., time series).
Evaluate Options:
A: False—Accepted types are CSV/JSON, not SQL/Python.
B: Partially true—Focuses on numerical data (e.g., sensors), not text broadly.
C: True—Used for fraud, intrusions, and sensor anomalies (key use cases).
D: False—Trained on customer data only, not general datasets.
Reasoning: C aligns with documented applications; others misalign.
Conclusion: C is correct.
OCI Anomaly Detection documentation states: “The service is designed to detect anomalies in time series data, making it valuable for fraud detection, network intrusion analysis, and sensor discrepancies.” A is incorrect (file formats), B overgeneralizes (numerical focus), and D misstates training data—only C matches the service’s purpose.
1: Oracle Cloud Infrastructure Anomaly Detection Documentation, "Use Cases".
15. Frage
Arrange the following in the correct Git Repository workflow order:
Install, configure, and authenticate Git.
Configure SSH keys for the Git repository.
Create a local and remote Git repository.
Commit files to the local Git repository.
Push the commit to the remote Git repository.
Antwort: D
Begründung:
Detailed Answer in Step-by-Step Solution:
Step 1: Install, configure, and authenticate Git: Git must be installed and configured (e.g., git config --global user.name) before any repository actions.
Step 2: Configure SSH keys: SSH keys are set up (e.g., ssh-keygen) and added to the remote service (e.g., GitHub, OCI Code Repository) for secure access.
Step 3: Create local and remote Git repository: Initialize a local repo (git init) and create/link a remote repo (e.g., git remote add origin).
Step 4: Commit files: Add files (git add .) and commit them locally (git commit -m "message").
Step 5: Push to remote: Push local commits to the remote repo (git push origin main).
Evaluate Options: Only D (1, 2, 3, 4, 5) follows this logical sequence; others (e.g., A starts with SSH before Git installation) are illogical.
The standard Git workflow in OCI Data Science or general practice begins with installing Git (1), configuring SSH for secure access (2), creating repositories (3), committing locally (4), and pushing remotely (5). The OCI Code Repository documentation aligns with this: “First, install Git and configure authentication (e.g., SSH), then set up repositories and manage code.” D is the only option reflecting this industry-standard process.
1: Oracle Cloud Infrastructure Code Repository Documentation, "Git Workflow Basics".
16. Frage
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Auf unterschiedliche Art und Weise kann man verschiedene Zwecke erfüllen. Was wichtig ist, dass man welchen Weg einschlägt. Viele Leute beteiligen sich an der Oracle 1Z0-1110-26 Zertifizierungsprüfung, um seine Lebens-und Arbeitsumstände zu verbessern. Wie alle wissen, dass es nicht so leicht ist, die Oracle 1Z0-1110-26 (Oracle Cloud Infrastructure Data Science Professional) Zertifizierungsprüfung zu bestehen. Für die Prüfung verwendet man viel Energie und Zeit. Traurigerweise haben sie die Oracle 1Z0-1110-26 Prüfung noch nicht bestanden.
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