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| Section | Objectives |
|---|---|
| Topic 1: 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 2: Apply MLOps Practices | - Use best practices for operationalizing ML solutions - Implement model lifecycle management - Monitor and maintain machine learning models |
| Topic 3: Design and Set Up Data Science Workspace | - Manage notebook sessions and compute resources - Create and configure Data Science projects - Use Accelerated Data Science SDK and open source tools |
| Topic 4: Use Related OCI Services | - Integrate OCI Data and AI services - Design machine learning solutions for business use cases - Apply OCI services for data ingestion, storage, and processing |
| Topic 5: Implement End-to-End Machine Learning Lifecycle | - Prepare and manage datasets - Build, train, and evaluate machine learning models - Automate machine learning workflows and pipelines - Save and manage models using Model Catalog - Deploy models and consume model endpoints |
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NEW QUESTION # 67
Which statement best describes Oracle Cloud Infrastructure Data Science Jobs?
Answer: A
Explanation:
Detailed Answer in Step-by-Step Solution:
Understand OCI Data Science Jobs: This service automates ML tasks (e.g., training, evaluation) with configurable, repeatable executions.
Key Characteristics: Jobs run on OCI’s infrastructure, managed by Oracle, not the customer or third parties, and are specific to Data Science, not general DevOps.
Evaluate Options:
A: Correct—Jobs are defined by users (e.g., via scripts) and executed on OCI’s fully managed compute resources.
B: Incorrect—Infrastructure is managed by OCI, not the customer.
C: Incorrect—No third-party cloud integration; it’s OCI-specific.
D: Incorrect—Jobs are for Data Science tasks (e.g., ML training), not all DevOps workloads (e.g., CI/CD pipelines).
Reasoning: “Fully managed” means OCI handles provisioning and scaling, aligning with A.
Conclusion: A accurately reflects the service’s purpose and operation.
OCI Data Science Jobs “allow users to define and execute repeatable machine learning tasks, such as model training or batch processing, on fully managed OCI infrastructure.” This eliminates customer management (B), third-party clouds (C), or broad DevOps scope (D). The documentation emphasizes automation and management by OCI, making A the precise description.
1: Oracle Cloud Infrastructure Data Science Documentation, "Overview of Jobs" section.
NEW QUESTION # 68
You are given the task of writing a program that sorts document images by language. Which Oracle service would you use?
Answer: C
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Identify the Oracle service to sort document images by language.
Task Breakdown: Requires extracting text from images (OCR) and detecting language—two potential services involved.
Evaluate Options:
A . Oracle Digital Assistant: Builds chatbots—irrelevant to image or language processing.
B . OCI Language: Detects and classifies languages in text—ideal for sorting after text extraction.
C . OCI Speech: Transcribes audio to text—not applicable to images.
D . OCI Vision: Performs OCR to extract text from images—necessary but not sufficient for language sorting.
Reasoning: The task emphasizes “sorting by language.” OCI Vision extracts text, but OCI Language identifies the language (e.g., English, Spanish). Since the question asks for one service and focuses on sorting, OCI Language (B) is the best fit, assuming text extraction is a precursor step.
Conclusion: B is correct.
OCI Language “provides language detection and classification capabilities, enabling identification of languages in text extracted from documents,” per the documentation. OCI Vision handles OCR, but the sorting task aligns with OCI Language (B). Digital Assistant (A) and Speech (C) don’t apply, and while Vision (D) is a prerequisite, B is the primary service for language sorting as per OCI’s AI service design.
1: Oracle Cloud Infrastructure Language Documentation, "Language Detection Features".
NEW QUESTION # 69
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: B
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 # 70
You are a data scientist working for a utilities company. You have developed an algorithm that detects anomalies from a utility reader in the grid. The size of the model artifact is about 2 GB, and you are trying to store it in the model catalog. Which THREE interfaces could you use to save the model artifact into the model catalog?
Answer: B,C,D
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Identify interfaces to save a 2 GB model to the Model Catalog.
Evaluate Options:
A: OCI CLI—Supports Data Science tasks—possible but not primary.
B: ADS SDK—Designed for model catalog ops—correct.
C: ODSC CLI—Not standard; likely typo for OCI CLI.
D: Console—GUI for catalog uploads—correct.
E: OCI Python SDK—Programmatic catalog access—correct.
F: Git CLI—Version control, not catalog-related.
Reasoning: B, D, E are OCI’s primary interfaces; A is valid but less emphasized.
Conclusion: B, D, E are correct (A plausible but not top-tier).
OCI documentation lists “ADS SDK (B), OCI Console (D), and OCI Python SDK (E) as primary methods to save models to the Model Catalog.” OCI CLI (A) works but isn’t highlighted, C isn’t real, and F is unrelated—B, D, E are the standard trio.
1: Oracle Cloud Infrastructure Data Science Documentation, "Model Catalog Interfaces".
NEW QUESTION # 71
You have a dataset with fewer than 1000 observations, and you are using Oracle AutoML to build a classifier. While visualizing the results of each stage of the Oracle AutoML pipeline, you notice that no visualization has been generated for one of the stages. Which stage is not visualized?
Answer: C
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Identify the non-visualized AutoML stage with small data.
Understand AutoML Pipeline: Includes sampling, feature/algorithm selection, tuning.
Evaluate Options:
A: Feature selection—Visualized (e.g., feature importance).
B: Algorithm selection—Visualized (e.g., algorithm scores).
C: Adaptive sampling—Skipped/visualization absent for <1000 rows.
D: Hyperparameter tuning—Visualized (e.g., trial plots).
Reasoning: Adaptive sampling optimizes large datasets; small data skips it, omitting visuals.
Conclusion: C is correct.
OCI AutoML documentation notes: “Adaptive sampling is applied to large datasets (>1000 rows) to reduce size; for smaller datasets, it’s skipped, and no visualization is generated.” Other stages (A, B,D) produce visuals—only C is absent here.
1: Oracle Cloud Infrastructure AutoML Documentation, "Pipeline Stages".
NEW QUESTION # 72
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