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| Section | Objectives |
|---|---|
| Topic 1: Design and Set Up Data Science Workspace | - Create and configure Data Science projects - Use Accelerated Data Science SDK and open source tools - Manage notebook sessions and compute resources |
| Topic 2: OCI Data Science - Introduction and Configuration | - Configure and manage Data Science resources - Use OCI Data Science notebooks and sessions - Understand OCI Data Science service concepts and architecture |
| Topic 3: Implement End-to-End Machine Learning Lifecycle | - Deploy models and consume model endpoints - Automate machine learning workflows and pipelines - Prepare and manage datasets - Save and manage models using Model Catalog - Build, train, and evaluate machine learning models |
| Topic 4: Use Related OCI Services | - Integrate OCI Data and AI services - Apply OCI services for data ingestion, storage, and processing - Design machine learning solutions for business use cases |
| Topic 5: Apply MLOps Practices | - Use best practices for operationalizing ML solutions - Monitor and maintain machine learning models - Implement model lifecycle management |
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NEW QUESTION # 149
You are a data scientist building a pipeline in the Oracle Cloud Infrastructure (OCI) Data Science service for your machine learning project. You want to optimize the pipeline completion time by running some steps in parallel. Which statement is true about running pipeline steps in parallel?
Answer: D
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Understand parallel execution in OCI Pipelines.
Evaluate Options:
A: False—Pipelines support parallelism.
B: True—DAG allows sequential/parallel steps—correct.
C: False—Not all steps must be parallel.
D: False—Independence enables parallelism.
Reasoning: B reflects OCI’s DAG-based flexibility.
Conclusion: B is correct.
OCI documentation states: “Pipeline steps can run sequentially or in parallel, defined by a directedacyclic graph (DAG) (B), allowing optimization of completion time.” A, C, and D misrepresent this—only B aligns with OCI’s pipeline design.
1: Oracle Cloud Infrastructure Data Science Documentation, "Pipeline Parallelism".
NEW QUESTION # 150
What is feature engineering in machine learning used for?
Answer: A
Explanation:
Detailed Answer in Step-by-Step Solution:
Define Feature Engineering: It’s the process of creating or modifying features to improve model performance.
Evaluate Options:
A: Parameter tuning adjusts model hyperparameters (e.g., learning rate), not features.
B: Model interpretation (e.g., SHAP values) explains predictions, not feature creation.
C: Transforming features (e.g., normalizing, encoding) is the core of feature engineering—correct.
D: Understanding features occurs during exploration, not engineering.
Reasoning: Feature engineering directly manipulates data inputs (e.g., converting timestamps to day-of-week), distinct from tuning or interpretation.
Conclusion: C is the precise definition.
OCI Data Science documentation defines feature engineering as “the process of transforming raw data into features that better represent the underlying problem to the predictive models, resulting in improved model accuracy.” Examples include scaling or creating interaction terms, aligning 1with C. Other options (A, B, D) relate to different ML stages.
1: Oracle Cloud Infrastructure Data Science Documentation, "Feature Engineering Overview".
NEW QUESTION # 151
Which activity is NOT a part of the machine learning life cycle?
Answer: D
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Identify which activity isn’t part of the ML lifecycle.
Define ML Lifecycle: Includes data access, preparation, modeling, evaluation, deployment, and monitoring.
Evaluate Options:
A: Database Management (e.g., DBA tasks) is IT-related, not specific to ML workflows.
B: Model Deployment (e.g., serving predictions) is a key ML phase—correctly included.
C: Modeling (e.g., training) is the core of ML—correctly included.
D: Data Access (e.g., retrieving data) is the first ML step—correctly included.
Reasoning: Database management supports infrastructure, not the ML process directly.
Conclusion: A is the outlier.
The OCI Data Science lifecycle includes “data access, exploration, feature engineering, modeling, deployment, and monitoring,” per the documentation. Database Management (A) is a general ITtask (e.g., optimizing Oracle DB), not an ML-specific activity, unlike B, C, and D, which are integral to OCI’s ML pipeline.
1: Oracle Cloud Infrastructure Data Science Documentation, "Machine Learning Lifecycle Overview".
NEW QUESTION # 152
You are given a task of writing a program that sorts document images by language. Which Oracle AI Service would you use?
Answer: C
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 # 153
As a data scientist, you use the Oracle Cloud Infrastructure (OCI) Language service to train custommodels. Which types of custom models can be trained?
Answer: C
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Identify custom model types for OCI Language service.
Understand OCI Language: Focuses on text analysis, not images.
Evaluate Options:
A: Image classification—Incorrect; Language is text-based.
B: Text classification, NER—Both text tasks—correct.
C: Sentiment—Pretrained, not custom; NER ok—incorrect.
D: Object detection—Image-based, incorrect.
Reasoning: B aligns with OCI Language’s custom text capabilities.
Conclusion: B is correct.
OCI documentation states: “OCI Language supports training custom models for text classification and Named Entity Recognition (NER) (B) using user data.” A and D involve images, C includes pretrained sentiment—only B matches OCI Language’s custom model scope.
1: Oracle Cloud Infrastructure Language Documentation, "Custom Model Types".
NEW QUESTION # 154
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