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| Section | Objectives |
|---|---|
| Topic 1: Use Related OCI Services | - Design machine learning solutions for business use cases - Integrate OCI Data and AI services - Apply OCI services for data ingestion, storage, and processing |
| Topic 2: OCI Data Science - Introduction and Configuration | - Understand OCI Data Science service concepts and architecture - Use OCI Data Science notebooks and sessions - Configure and manage Data Science resources |
| Topic 3: Implement End-to-End Machine Learning Lifecycle | - Deploy models and consume model endpoints - Build, train, and evaluate machine learning models - Prepare and manage datasets - Automate machine learning workflows and pipelines - Save and manage models using Model Catalog |
| Topic 4: Apply MLOps Practices | - Monitor and maintain machine learning models - Implement model lifecycle management - Use best practices for operationalizing ML solutions |
| Topic 5: Design and Set Up Data Science Workspace | - Create and configure Data Science projects - Manage notebook sessions and compute resources - Use Accelerated Data Science SDK and open source tools |
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NEW QUESTION # 43
Where do calls to stdout and stderr from score.py go in the model deployment?
Answer: C
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Locate stdout/stderr output from score.py in deployment.
Understand Deployment: score.py runs in a model endpoint; logs are managed by OCI.
Evaluate Options:
A: False—No VM file is defined by default; logs go to OCI Logging.
B: False—Console displays UI, not raw logs.
C: False—Cloud Shell is a separate tool, not a log destination.
D: True—Predict logs in OCI Logging capture stdout/stderr.
Reasoning: OCI centralizes logs in its Logging service for deployments.
Conclusion: D is correct.
OCI documentation states: “During model deployment, stdout and stderr from score.py are automatically sent to the predict log in the OCI Logging service, as configured in the deployment settings.” A, B, and C don’t align with this managed logging approach—only D is accurate.
1: Oracle Cloud Infrastructure Data Science Documentation, "Model Deployment - Logging".
NEW QUESTION # 44
Which step is a part of the AutoML pipeline?
Answer: A
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Identify a step in OCI’s AutoML pipeline.
Understand AutoML: Automates model building—includes preprocessing, selection, and tuning.
Evaluate Options:
A: Feature Extraction (e.g., PCA) isn’t explicitly part of OCI AutoML—too specific.
B: Saving to Model Catalog is post-AutoML, not a pipeline step.
C: Deployment is a separate action after AutoML—incorrect.
D: Feature Selection (e.g., choosing relevant features) is a core AutoML step—correct.
Reasoning: OCI AutoML automates feature selection, algorithm choice, and tuning—D fits.
Conclusion: D is correct.
OCI AutoML’s pipeline includes “feature selection, algorithm selection, adaptive sampling, and hyperparameter tuning,” per the documentation. Extraction (A) isn’t highlighted, while saving (B) and deployment (C) are post-process actions—only Feature Selection (D) is an integral automated step.
1: Oracle Cloud Infrastructure Data Science Documentation, "AutoML Pipeline".
NEW QUESTION # 45
You are a data scientist with a set of text and image files that need annotation, and you want to use Oracle Cloud Infrastructure (OCI) Data Labeling. Which of the following THREE annotation classes are supported by the tool?
Answer: C,E,F
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Identify supported annotation classes in OCI Data Labeling.
Understand Tool: Supports image/text annotations for ML.
Evaluate Options:
A: Object detection—Yes (bounding boxes).
B: Named entity—Text-specific, not primary for images.
C: Classification—Yes (labels for images/text).
D: Key-point—Not listed in OCI docs.
E: Polygonal—Not explicitly supported.
F: Semantic segmentation—Yes (pixel-level).
Reasoning: A, C, F match OCI’s image/text focus.
Conclusion: A, C, F are correct.
OCI Data Labeling supports “object detection (A), classification (C), and semantic segmentation (F) for images and text,” per documentation. B is text-specific, D and E aren’t highlighted—only A, C, F are core classes.
1: Oracle Cloud Infrastructure Data Labeling Documentation, "Annotation Types".
NEW QUESTION # 46
Which Oracle Accelerated Data Science (ADS) classes can be used for easy access to datasets fromreference libraries and index websites, such as scikit-learn?
Answer: D
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Identify ADS class for accessing datasets (e.g., scikit-learn).
Evaluate Options:
A: DatasetBrowser—Not an ADS class.
B: DatasetFactory—Loads datasets from sources like scikit-learn—correct.
C: ADSTuner—Hyperparameter tuning, not data access.
D: SecretKeeper—Manages credentials, not datasets.
Reasoning: DatasetFactory simplifies dataset loading (e.g., DatasetFactory.open()).
Conclusion: B is correct.
OCI documentation states: “DatasetFactory in ADS SDK provides methods to easily load datasets from libraries like scikit-learn or other sources (e.g., DatasetFactory.open('sklearn.datasets:load_iris')).” A isn’t real, C tunes models, and D handles secrets—only B fits.
1: Oracle Cloud Infrastructure ADS SDK Documentation, "DatasetFactory".
NEW QUESTION # 47
You are given a task of writing a program that sorts document images by language. Which Oracle AI Service would you use?
Answer: B
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 # 48
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