D496 Introduction to Data Science
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Free D496 Introduction to Data Science Questions
Data Quality refers to:
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accuracy.
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completeness.
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consistency.
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All of the above
Explanation
Data quality is a multidimensional concept that includes several critical attributes such as accuracy (data correctly represents real-world values), completeness (all necessary data is available), and consistency (data remains uniform across systems). Together, these characteristics ensure that data is trustworthy and usable for decision-making, reporting, and analytics. Poor data quality can lead to incorrect insights and business inefficiencies, making its continuous monitoring and improvement essential.
What is a data pipeline?
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special folder in OneLake storage where data can be exported from a lakehouse
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sequence of activities to orchestrate a data ingestion or transformation process
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saved Power Query
Explanation
A data pipeline is a set of automated processes designed to extract, transform, and load (ETL) data from various sources into a destination system, such as a database or data warehouse. It orchestrates the movement and transformation of data, ensuring that it flows efficiently, reliably, and in the correct format for analysis or reporting. Data pipelines are crucial for managing large-scale data workflows, enabling real-time or scheduled data processing without manual intervention.
Enterprise authority that ensures control and accountability for enterprise data through the establishment of decision rights and data policies and standards that are implemented and monitored through a formal structure of assigned roles, responsibilities, and accountabilities is called ________.
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Data stewardship
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Data sharing
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Data integrity
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Data governance
Explanation
Which of the following best describes a key distinction between centralized and decentralized data management systems?
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Centralized systems aggregate data in a single repository, while decentralized systems distribute data across multiple locations.
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Centralized systems prioritize data security, while decentralized systems focus on data accessibility.
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Centralized systems are exclusively used for real-time analytics, while decentralized systems are only for historical data analysis.
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Centralized systems require less maintenance than decentralized systems.
Explanation
The primary difference between centralized and decentralized data management systems lies in how data is stored and managed. Centralized systems bring all data into a single repository or data warehouse, which simplifies management, ensures consistency, and makes it easier to enforce data governance policies. In contrast, decentralized systems distribute data across multiple locations or departments, giving local units more control but often making integration and consistency more challenging. The choice between the two approaches depends on organizational needs for control, flexibility, and performance.
What is the primary focus of DATA SCIENCE?
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extracting insights from data
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managing large databases
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coding websites
Explanation
Data Science is an interdisciplinary field focused on extracting meaningful insights and knowledge from data using techniques from statistics, mathematics, programming, and machine learning. It involves collecting, cleaning, analyzing, and interpreting data to support data-driven decision-making and predictive modeling. While managing databases and coding are part of the process, the ultimate goal of data science is to transform data into actionable insights that help solve real-world problems or inform strategic decisions.
Pie charts are circular graph models divided into sectors, illustrating proportions. When is it advisable to not use Pie charts?
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Don't use when you have more than 6 categories
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Don't use when you have more than 25 categories
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Hard for beginners and common users to understand, better for experts
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When you have too similar values, where the circle's area makes it difficult to read
Explanation
Pie charts are effective for showing simple proportional relationships, but they lose clarity when the data includes too many categories or when the category values are very similar. When slices are nearly equal in size, it becomes difficult for viewers to visually distinguish differences between them, leading to misinterpretation. Therefore, pie charts are best reserved for datasets with a small number of distinct categories and noticeable variations in values. In cases of similar values, bar charts are typically a more accurate and readable alternative.
What is the primary function of the JOIN operation in SQL?
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To filter records from a single table based on specific criteria
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To aggregate data from multiple tables into a single summary
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To combine rows from two or more tables based on a common attribute
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To update existing records in a database table
Explanation
The JOIN operation in SQL is used to combine rows from two or more tables based on a related column between them, typically through a key such as a primary or foreign key. This allows users to query data that is stored across multiple related tables and present it as a single result set. Common types of joins include INNER JOIN, LEFT JOIN, RIGHT JOIN, and FULL JOIN, each serving a different purpose in how data is matched and retrieved.
What are Pipelines used for?
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Data scientists use pipelines for data classification and modelling
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Data ingestion and modelling
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Orchestration of model training, deployment and management
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Cloud-based storage and compute service offering auto-ML capability to AI engineers
Explanation
In data science and machine learning, pipelines are used to automate and orchestrate the sequence of tasks involved in the lifecycle of a model — including data preprocessing, training, validation, deployment, and monitoring. They streamline workflows by ensuring that processes are executed consistently and reproducibly, reducing manual intervention and human error. Pipelines also help integrate various tools and systems, allowing for efficient model management and continuous delivery in production environments.
What is a significant limitation of using pie charts for displaying data?
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They can effectively show trends over time.
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They may not accurately represent small differences between categories.
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They are suitable for displaying continuous data.
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They require a large amount of data to be effective.
Explanation
A key limitation of pie charts is that they struggle to accurately display small differences between categories. When slices are close in size, it becomes difficult to distinguish which category is larger or smaller, leading to potential misinterpretation. Pie charts are best for showing simple proportions of a whole but become ineffective when there are many categories or when the data values are very similar. In such cases, bar charts or other visualizations provide clearer insights.
Which of the following best defines descriptive analytics and the primary question it seeks to answer?
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Descriptive analytics involves predicting future trends and answers the question, 'What will happen?'
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Descriptive analytics summarizes past data and answers the question, 'What happened?'
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Descriptive analytics focuses on identifying relationships in data and answers the question, 'Why did it happen?'
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Descriptive analytics is concerned with data security and answers the question, 'How is data protected?'
Explanation
Descriptive analytics is the foundational level of data analytics that focuses on summarizing and interpreting historical data to understand what has already occurred. It uses methods such as data aggregation, reporting, and visualization to present insights in an understandable format. The goal is to identify patterns or trends in past performance, providing context for future analysis. By answering the question “What happened?”, descriptive analytics helps organizations monitor outcomes and establish a baseline for predictive and prescriptive analytics.
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