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Oracle Cloud Infrastructure 2025 Data Science Professional Sample Questions (Q31-Q36):

NEW QUESTION # 31
Which model has an open-source, open model format that allows you to run machine learning models on different platforms?

A. PySpark
B. TensorFlow
C. ONNX
D. PyTorch

Answer: C

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Identify an open model format for cross-platform ML model execution.
* Evaluate Options:
* A. PySpark: A big data framework, not a model format.
* B. PyTorch: An ML framework with its own format, not inherently cross-platform without conversion.
* C. TensorFlow: An ML framework with its SavedModel format, not universally open across platforms.
* D. ONNX: Open Neural Network Exchange, an open-source format for model interoperability across frameworks.
* Reasoning: ONNX is designed for portability (e.g., convert PyTorch to ONNX, run in TensorFlow), unlike framework-specific options.
* Conclusion: D is the correct choice.
ONNX (D) is "an open-source model format that enables interoperability between ML frameworks like PyTorch and TensorFlow," per OCI documentation. PySpark (A) is a processing tool, while PyTorch (B) and TensorFlow (C) are frameworks with native formats-only ONNX ensures cross-platform compatibility.
Oracle Cloud Infrastructure Data Science Documentation, "Supported Model Formats".

NEW QUESTION # 32
During a job run, you receive an error message that no space is left on your disk device. To solve the problem, you must increase the size of the job storage. What would be the most efficient way to do this with Data Science Jobs?

A. Your code using too much disk space. Refactor the code to identify the problem
B. On the job run, set the environment variable that helps increase the size of the storage
C. Create a new job with increased storage size and then run the job
D. Edit the job, change the size of the storage of your job, and start a new job run

Answer: C

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Efficiently increase storage for an OCI Job.
* Understand Jobs: Storage (block volume) is set at job creation, not dynamically adjustable.
* Evaluate Options:
* A: False-Jobs can't edit storage post-creation; it's fixed.
* B: False-No environment variable adjusts storage size.
* C: True-Create a new job with larger storage (e.g., 200 GB) and run it.
* D: False-Refactoring code is inefficient compared to increasing storage.
* Reasoning: C is the standard OCI process for adjusting resources.
* Conclusion: C is correct.
OCI documentation states: "Storage size for a Data Science Job is specified during job creation (e.g., block volume size). To increase it, create a new job with a larger storage configuration and initiate a new run." Editing (A) isn't supported, variables (B) don't apply, and refactoring (D) avoids the issue-only C is efficient.
Oracle Cloud Infrastructure Data Science Documentation, "Jobs - Storage Configuration".

NEW QUESTION # 33
You are a computer vision engineer building an image recognition model. You decide to use Oracle Data Labeling to annotate your image data. Which of the following THREE are possible ways to annotate an image in Data Labeling?

A. Adding labels to an image using semantic segmentation, by drawing multiple bounding boxes to an image
B. Adding a single label to an image
C. Adding labels to an image using object detection, by drawing bounding boxes to an image
D. Adding labels to an image by drawing a bounding box to an image is not supported by Data Labeling
E. Adding multiple labels to an image

Answer: B,C,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.
Oracle Cloud Infrastructure Data Labeling Documentation, "Image Annotation Types".

NEW QUESTION # 34
Which of these options allow the sharing and loading back of ML models into a notebook session?

A. Model catalog
B. Model deployment
C. Model provenance
D. Model taxonomy

Answer: A

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Identify the mechanism for sharing and reloading ML models in OCI Data Science.
* Evaluate Options:
* A. Model provenance: Tracks model origin-informative but not a sharing mechanism.
* B. Model taxonomy: Categorizes models (e.g., regression)-not for sharing/loading.
* C. Model deployment: Makes models accessible as endpoints, not for notebook reloading.
* D. Model catalog: Stores models and artifacts, enabling sharing and loading into sessions.
* Reasoning: The Model Catalog is OCI's centralized repository for saving, sharing, and retrieving models (e.g., via ADS SDK).
* Conclusion: D is the correct tool.
The OCI Model Catalog "enables data scientists to save trained models and their artifacts, share them with team members, and load them back into notebook sessions for further use or evaluation." Provenance (A) and taxonomy (B) are metadata, while deployment (C) serves inference, not notebook access. D is explicitly designed for this purpose.
Oracle Cloud Infrastructure Data Science Documentation, "Model Catalog Usage".

NEW QUESTION # 35
Which step is a part of the AutoML pipeline?

A. Model saved to Model Catalog
B. Feature Selection
C. Feature Extraction
D. Model Deployment

Answer: B

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.
Oracle Cloud Infrastructure Data Science Documentation, "AutoML Pipeline".

NEW QUESTION # 36
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