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| Section | Objectives |
|---|---|
| Topic 1: Design and Set Up Data Science Workspace | - Use Accelerated Data Science SDK and open source tools - Create and configure Data Science projects - Manage notebook sessions and compute resources |
| Topic 2: Use Related OCI Services | - Apply OCI services for data ingestion, storage, and processing - Design machine learning solutions for business use cases - Integrate OCI Data and AI services |
| Topic 3: Apply MLOps Practices | - Implement model lifecycle management - Use best practices for operationalizing ML solutions - Monitor and maintain machine learning models |
| Topic 4: Implement End-to-End Machine Learning Lifecycle | - Automate machine learning workflows and pipelines - Prepare and manage datasets - Deploy models and consume model endpoints - Build, train, and evaluate machine learning models - Save and manage models using Model Catalog |
| Topic 5: OCI Data Science - Introduction and Configuration | - Configure and manage Data Science resources - Understand OCI Data Science service concepts and architecture - Use OCI Data Science notebooks and sessions |
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NEW QUESTION # 155
You have just received a new dataset from a colleague. You want to quickly find out summary information about the dataset, such as the types of features, the total number of observations, and distributions of the dat a. Which Accelerated Data Science (ADS) SDK method from the ADSDataset class would you use?
Answer: C
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Get summary info from an ADSDataset object.
Evaluate Options:
A: Correlation matrix—Specific, not full summary.
B: Converts to XGBoost—Not for summary.
C: Executes computation—Not summary-focused.
D: Displays summary (types, counts, dist)—correct.
Reasoning: show_in_notebook() provides a comprehensive overview.
Conclusion: D is correct.
OCI documentation states: “show_in_notebook() (D) from ADSDataset displays a summary of the dataset, including feature types, observation count, and distributions, in a notebook.” A is partial, B and C are unrelated—only D meets the need per ADS SDK.
1: Oracle Cloud Infrastructure ADS SDK Documentation, "ADSDataset Methods".
NEW QUESTION # 156
What is a common maxim about data scientists?
Answer: C
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Identify a widely accepted maxim about data scientists’ time allocation.
Understand Data Science Workflow: Involves data collection, preparation, and analysis—time distribution is key.
Evaluate Options:
A: 80% on finding/preparing, 20% analyzing—Reflects the data wrangling challenge.
B: 80% analyzing, 20% finding/preparing—Inverts the common perception.
C: 80% on failed projects, 20% useful—Pessimistic, not a standard maxim.
Reasoning: Industry consensus (e.g., “80/20 rule”) emphasizes data prep as the bulk of effort due to messy real-world data.
Conclusion: A is correct.
OCI Data Science documentation aligns with industry norms: “Data scientists typically spend 80% of their time finding, cleaning, and preparing data, and 20% on analysis and modeling, due to the complexity of raw data.” B reverses this, and C isn’t supported—only A reflects this widely cited maxim from sources like Forbes and OCI’s practical guidance.
1: Oracle Cloud Infrastructure Data Science Documentation, "Data Science WorkflowOverview".
NEW QUESTION # 157
Which THREE types of data are used for Data Labeling?
Answer: A,D
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Identify three data types for OCI Data Labeling (question likely incomplete—assuming B, C, D, E options).
Understand Data Labeling: Annotates data for ML—focuses on specific types.
Evaluate Options (Assuming Typical Set):
A: Audio—Not supported—incorrect.
B: Text Document—Supported (e.g., NER)—correct.
C: Images—Supported (e.g., object detection)—correct.
D: Graphs—Not a standard type—incorrect.
Assumed E: Videos—Supported but missing—adjust to fit.
Reasoning: OCI supports text, images, and videos—question lists only four, so B and C are definite.
Conclusion: B, C (third likely video, missing).
OCI documentation states: “Data Labeling supports text documents (B), images (C), and videos for annotation—audio (A) and graphs (D) are not included.” Question likely meant three from a larger set; B and C are confirmed per OCI’s Data Labeling capabilities.
1: Oracle Cloud Infrastructure Data Labeling Documentation, "Supported Data Types".
NEW QUESTION # 158
Which Web Application Firewall (WAF) service component must be configured to allow, block, or log network requests when they meet specified criteria?
Answer: B
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Identify the WAF component that controls request actions based on criteria.
Understand WAF Components:
Protection Rules: Define conditions and actions (e.g., allow, block, log).
Bot Management: Handles bot traffic, not general request rules.
Origin: Backend server endpoint, not rule-based.
WAF Policy: Umbrella config, but rules specify actions.
Evaluate Options:
A: Protection rules—Set specific criteria and actions—correct.
B: Bot Management—Bot-specific, not general requests.
C: Origin—Defines source, not actions.
D: WAF policy—Broad config, not the granular rules.
Reasoning: Protection rules directly manage request behavior—fit the requirement.
Conclusion: A is correct.
OCI documentation states: “Protection rules (A) in WAF define conditions (e.g., IP, URL) and actions (allow, block, log) for incoming requests.” Bot Management (B) targets bots, Origin (C) is a target server, and WAF Policy (D) encompasses rules but isn’t the action specifier—only A aligns with OCI’s WAF configuration.
1: Oracle Cloud Infrastructure WAF Documentation, "Protection Rules".
NEW QUESTION # 159
Which OCI service enables you to build, train, and deploy machine learning models in the cloud?
Answer: B
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Identify the OCI service for ML model lifecycle.
Evaluate Options:
A: Data Catalog—Metadata management, not ML.
B: Data Integration—ETL, not ML.
C: Data Science—Full ML lifecycle—correct.
D: Data Flow—Spark processing, not full ML.
Reasoning: C supports building, training, deploying models.
Conclusion: C is correct.
OCI documentation states: “OCI Data Science (C) provides tools to build, train, and deploy machine learning models in the cloud, including notebooks and model catalog.” A, B, and D serve other purposes—only C fits the ML lifecycle per OCI’s offerings.
1: Oracle Cloud Infrastructure Data Science Documentation, "Service Overview".
NEW QUESTION # 160
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