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| Section | Weight | Objectives |
|---|---|---|
| MLOps and OCI Integration | 20% | - OCI ecosystem
|
| Model Development and Deployment | 30% | - Model training
|
| Machine Learning Fundamentals | 20% | - Unsupervised learning
|
| OCI Data Science Service | 30% | - Projects and notebooks
|
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NEW QUESTION # 18
Six months ago, you created and deployed a model that predicts customer churn for a call centre. Initially, it was yielding quality predictions. However, over the last two months, users are questioning the credibility of the predictions. Which TWO methods would you employ to verify the accuracy of the model?
Answer: A,C
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Address declining prediction accuracy and verify model performance.
Analyze Problem: Degradation over time suggests data drift or model staleness—common ML issues.
Evaluate Options:
A . Retrain the model: Uses new data to update the model—fixes accuracy—correct.
B . Validate with recent data: Tests performance but doesn’t fix—diagnostic only.
C . Drift monitoring: Detects data distribution shifts—verifies cause—correct.
D . Redeploy the model: Repeats deployment, doesn’t address root cause.
E . Operational monitoring: Tracks infra (e.g., latency), not prediction accuracy.
Reasoning: C identifies drift (why accuracy dropped), A corrects it—best pair for verification and improvement.
Conclusion: A and C are correct.
OCI documentation states: “Drift monitoring (C) detects changes in data distribution that impact accuracy, while retraining (A) with new data restores model performance.” Validation (B) checks but doesn’t fix, redeployment (D) is redundant, and operational monitoring (E) is infra-focused—only A and C align with OCI’s model maintenance strategy.
1: Oracle Cloud Infrastructure Data Science Documentation, "Model Monitoring and Retraining".
NEW QUESTION # 19
You are given a task of writing a program that sorts document images by language. Which Oracle AI Service would you use?
Answer: D
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Select an OCI AI service to sort images by language.
Evaluate Options:
A: Digital Assistant—Chatbots, not image/language processing.
B: Vision—Image analysis (e.g., object detection), not language sorting.
C: Speech—Audio-to-text, not image-based.
D: Language—Text analysis (e.g., language detection) after OCR—correct.
Reasoning: Images need OCR (Vision) then language detection (Language)—D fits the sorting task.
Conclusion: D is correct.
OCI Language “detects and classifies languages in text,” often paired with OCI Vision’s OCR to process document images. Vision (B) extracts text, but Language (D) sorts by language—Digital Assistant (A) and Speech (C) don’t apply. Documentation supports this workflow.
1: Oracle Cloud Infrastructure Language Documentation, "Language Detection".
NEW QUESTION # 20
You are working as a data scientist for a healthcare company. They decide to analyze the data to find patterns in a large volume of electronic medical records. You are asked to build a PySpark solution to analyze these records in a JupyterLab notebook. What is the order of recommended stepsto develop a PySpark application in Oracle Cloud Infrastructure (OCI) Data Science?
Answer: C
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Sequence steps for PySpark app development.
Steps:
Launch notebook: First—sets up environment.
Install PySpark conda: Second—adds Spark libraries.
Configure core-site.xml: Third—connects to data.
Develop app: Fourth—writes code.
Data Flow: Fifth—scales (optional).
Evaluate: D (1, 2, 3, 4, 5) matches this logical order.
Reasoning: Notebook first, then setup and coding.
Conclusion: D is correct.
OCI documentation states: “1) Launch a notebook session, 2) install a PySpark conda env, 3) configure core-site.xml, 4) develop your PySpark app, 5) optionally use Data Flow (D).” Other orders (A, B, C) misplace notebook launch or config—D is correct.
1: Oracle Cloud Infrastructure Data Science Documentation, "PySpark Development".
NEW QUESTION # 21
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: C
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 # 22
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?
Answer: A,B,E
Explanation:
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".
NEW QUESTION # 23
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