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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: MLOps and OCI Integration | 20% | - Automation and pipelines
|
| Topic 2: OCI Data Science Service | 30% | - Model catalog
|
| Topic 3: Machine Learning Fundamentals | 20% | - Supervised learning
|
| Topic 4: Model Development and Deployment | 30% | - Model training
|
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NEW QUESTION # 123
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: A,C,E
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 # 124
You have trained a binary classifier for a loan application and saved this model into the model catalog. A colleague wants to examine the model, and you need to share the model with your colleague. From the model catalog, which model artifacts can be shared?
Answer: D
Explanation:
Detailed Answer in Step-by-Step Solution:
Understand Model Catalog: The OCI Model Catalog stores trained models and associated details.
Identify Shareable Artifacts: When sharing, all components—model file, metadata (e.g., name, description), hyperparameters (e.g., learning rate), and metrics (e.g., accuracy)—are accessible.
Evaluate Options:
A: Excludes the model itself—incorrect.
B: Excludes metrics—incorrect.
C: Excludes metadata and hyperparameters—incorrect.
D: Includes all components—correct.
Conclusion: D is comprehensive and accurate.
The OCI Model Catalog allows sharing of the model artifact (the trained model), metadata, hyperparameters, and performance metrics, enabling full examination by colleagues. This isdetailed in the official documentation. (Oracle Cloud Infrastructure Data Science Documentation, "Model Catalog Overview").
NEW QUESTION # 125
You have received machine learning model training code, without clear information about the optimal shape to run the training. How would you proceed to identify the optimal compute shape for your model training that provides a balanced cost and processing time?
Answer: B
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Optimize compute shape for cost and time.
Evaluate Options:
A: Tuning params—Focuses on model, not shape.
B: Strongest shape—Costly, unbalanced.
C: Scale up when utilized—Balances cost/time—correct.
D: Random start—Unsystematic.
Reasoning: C iteratively optimizes based on utilization.
Conclusion: C is correct.
OCI documentation advises: “Start with a small shape, monitor utilization and time (C); scale up if fully utilized until performance stabilizes—optimizes cost and speed.” A misfocuses, B overspends, D lacks method—only C aligns.
1: Oracle Cloud Infrastructure Data Science Documentation, "Compute Shape Optimization".
NEW QUESTION # 126
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: A
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 # 127
You are creating an Oracle Cloud Infrastructure (OCI) Data Science job that will run on a recurring basis in a production environment. This job will pick up sensitive data from an Object Storage Bucket, train a model, and save it to the model catalog. How would you design the authentication mechanism for the job?
Answer: A
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Securely authenticate a recurring OCI Job.
Evaluate Options:
A: PAR—Limited duration, insecure for recurring jobs.
B: Resource principal—Secure, managed auth for Jobs—correct.
C: Personal config—Unscalable, security risk.
D: Vault with personal keys—Complex, still uses user creds.
Reasoning: B uses OCI’s native, secure resource principal mechanism.
Conclusion: B is correct.
OCI documentation states: “For Jobs accessing sensitive data, use resource principals with a dynamic group (e.g., resource.type = 'datasciencejobrun') and policies granting access to Object Storage and Model Catalog—secure and scalable.” A is temporary, C and D risk credential exposure—B is best practice.
1: Oracle Cloud Infrastructure Data Science Documentation, "Job Authentication".
NEW QUESTION # 128
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