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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: MLOps and OCI Integration | 20% | - Automation and pipelines
|
| Topic 2: Model Development and Deployment | 30% | - Model training
|
| Topic 3: Machine Learning Fundamentals | 20% | - Supervised learning
|
| Topic 4: OCI Data Science Service | 30% | - Model catalog
|
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NEW QUESTION # 80
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 # 81
What do you use the score.py file for?
Answer: D
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Determine the purpose of score.py in OCI Data Science model deployment.
Understand Model Deployment: When deploying a model in OCI, artifacts include score.py, runtime.yaml, etc.
Evaluate Options:
A: Infrastructure configuration (e.g., compute shape) is handled by deployment settings, not score.py.
B: score.py contains the inference logic (e.g., load_model(), predict())—correct.
C: Conda environment is defined in runtime.yaml or a requirements file—not score.py.
D: Scaling (e.g., instance count) is set in deployment configuration—not score.py.
Reasoning: score.py is the script executed by the deployment endpoint to load the model and make predictions.
Conclusion: B is the correct purpose.
The OCI Data Science documentation states: “The score.py file is a required artifact for model deployment, containing the inference logic—functions like load_model() to load the model and predict() to generate predictions from input data.” Infrastructure (A) and scaling (D) are managed via the OCI Console or SDK, while the environment (C) is specified in runtime.yaml. B is the precise role of score.py in OCI’s deployment workflow.
1: Oracle Cloud Infrastructure Data Science Documentation, "Model Deployment - score.py".
NEW QUESTION # 82
Which technique can be used for feature engineering in the machine learning lifecycle?
Answer: B
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Identify a feature engineering technique in ML.
Understand Feature Engineering: Transforms raw data into model-ready features.
Evaluate Options:
A . PCA: Reduces dimensionality—feature engineering—correct.
B . K-means: Clustering model—not feature engineering.
C . SVM: Classification model—not feature engineering.
D . Gradient boosting: Model training—not feature engineering.
Reasoning: PCA creates new features via transformation—fits definition.
Conclusion: A is correct.
OCI documentation states: “Feature engineering techniques like Principal Component Analysis (PCA) (A) transform data into new features to enhance model performance.” B, C, and D are modeling techniques—only A aligns with OCI’s feature engineering stage.
1: Oracle Cloud Infrastructure Data Science Documentation, "Feature Engineering Techniques".
NEW QUESTION # 83
You are a data scientist leveraging the Oracle Cloud Infrastructure (OCI) Language AI service for various types of text analyses. Which TWO capabilities can you utilize with this tool?
Answer: D,E
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Identify two OCI Language AI capabilities.
Understand OCI Language: Focuses on text analysis tasks.
Evaluate Options:
A: Table extraction—Vision, not Language—incorrect.
B: Punctuation correction—Not offered—incorrect.
C: Sentence diagramming—Not supported—incorrect.
D: Topic classification—Supported (custom/pretrained)—correct.
E: Sentiment analysis—Supported (pretrained)—correct.
Reasoning: D and E are core text analysis features of OCI Language.
Conclusion: D and E are correct.
OCI documentation states: “OCI Language offers topic classification (D) and sentiment analysis (E) for text analysis, among other features.” A belongs to Vision, B and C aren’t available—only D and E match OCI Language’s capabilities.
1: Oracle Cloud Infrastructure Language Documentation, "Text Analysis Features".
NEW QUESTION # 84
You are asked to prepare data for a custom-built model that requires transcribing Spanish video recordings into a readable text format with profane words identified. Which Oracle Cloud Service would you use?
Answer: A
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Transcribe Spanish video audio and identify profanity.
Evaluate Options:
A: Anomaly Detection—Not for transcription or text analysis.
B: Speech—Converts audio to text (e.g., Spanish), base for further analysis—correct.
C: Translation—Translates text, not transcription.
D: Language—Analyzes text (e.g., profanity), but needs transcribed input.
Reasoning: Speech (B) transcribes video audio; Language could follow for profanity.
Conclusion: B is correct for transcription.
OCI Speech “transcribes audio from video or audio files into text, supporting languages like Spanish.” Post-transcription, OCI Language could detect profanity, but B is the starting point—Anomaly (A) and Translation (C) don’t fit.
1: Oracle Cloud Infrastructure Speech Documentation, "Transcription Features".
NEW QUESTION # 85
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