Which statistical method is commonly used for data quality assessment?
- Descriptive statistics
- Hypothesis testing
- Inferential statistics
- Regression analysis
Descriptive statistics are commonly used for data quality assessment as they provide a summary of the key characteristics of a dataset, such as measures of central tendency (mean, median, mode), dispersion (range, variance, standard deviation), and distribution (histograms, box plots). These statistics help analysts understand the underlying patterns, trends, and outliers in the data, enabling them to assess data quality and make informed decisions based on the findings.
What is the difference between data profiling and data monitoring in the context of data quality assessment?
- Data profiling analyzes the structure and content of data at a static point in time, while data monitoring continuously observes data quality over time.
- Data profiling assesses data accuracy, while data monitoring assesses data completeness.
- Data profiling focuses on identifying outliers, while data monitoring identifies data trends.
- Data profiling involves data cleansing, while data monitoring involves data validation.
Data profiling involves analyzing the structure, content, and quality of data to understand its characteristics at a specific point in time. It helps identify data anomalies, patterns, and inconsistencies, which are essential for understanding data quality issues. On the other hand, data monitoring involves continuously observing data quality over time to detect deviations from expected patterns or thresholds. It ensures that data remains accurate, consistent, and reliable over time, allowing organizations to proactively address data quality issues as they arise.
Which metric evaluates the accuracy of data against a trusted reference source?
- Accuracy
- Consistency
- Timeliness
- Validity
Accuracy is a data quality metric that assesses the correctness and precision of data against a trusted reference source. It involves comparing the data values in a dataset with known or authoritative sources to determine their level of agreement. Accurate data ensures that information is reliable and dependable for decision-making and analysis purposes.
In normalization, the process of breaking down a large table into smaller tables to reduce data redundancy and improve data integrity is called ________.
- Aggregation
- Compaction
- Decomposition
- Integration
Normalization involves decomposing a large table into smaller, related tables to eliminate redundancy and improve data integrity by reducing the chances of anomalies.
In an ERD, what does a rectangle represent?
- Attribute
- Entity
- Process
- Relationship
In an Entity-Relationship Diagram (ERD), a rectangle represents an entity, which is a real-world object or concept that is distinguishable from other objects. It typically corresponds to a table in a database.
What are the main components of a Data Lake architecture?
- Data ingestion, Storage, Processing, Security
- Data modeling, ETL, Reporting, Dashboards
- NoSQL databases, Data warehouses, Data marts, OLAP cubes
- Tables, Indexes, Views, Triggers
The main components of a Data Lake architecture typically include data ingestion, storage, processing, and security. These components work together to store and manage large volumes of diverse data efficiently.
Scenario: A regulatory audit requires your organization to provide a comprehensive overview of data flow and transformations. How would you leverage metadata management and data lineage to address the audit requirements effectively?
- Depend solely on manual documentation for audit, neglect data lineage analysis, limit stakeholder communication
- Document metadata and data lineage, analyze data flow and transformations, generate comprehensive reports for audit, involve relevant stakeholders in the process
- Ignore metadata management and data lineage, provide limited data flow information, focus on compliance with regulatory requirements only
- Use generic templates for audit reports, overlook data lineage and metadata, minimize stakeholder involvement
Leveraging metadata management and data lineage involves documenting metadata and data lineage, analyzing data flow and transformations, and generating comprehensive reports for the audit. Involving relevant stakeholders ensures that the audit requirements are effectively addressed, providing transparency and compliance with regulatory standards.
Scenario: Your company is planning to implement a new data warehouse solution. As the data engineer, you are tasked with selecting an appropriate data loading strategy. Given the company's requirements for near real-time analytics, which data loading strategy would you recommend and why?
- Bulk Loading
- Change Data Capture (CDC)
- Incremental Loading
- Parallel Loading
Change Data Capture (CDC) captures only the changes made to the source data since the last extraction. This approach ensures near real-time analytics by transferring only the modified data, reducing the processing time and allowing for quicker insights.
Scenario: Your team is tasked with building a data integration solution that requires seamless integration with cloud services such as AWS and Azure. Which ETL tool would be most suitable for this scenario, and what features make it a good fit?
- AWS Glue
- Fivetran
- Matillion
- Stitch Data
Matillion is well-suited for seamless integration with cloud services like AWS and Azure. Its native integration with cloud platforms, drag-and-drop interface, and scalability make it an ideal choice for building data integration solutions in cloud environments.
What is the primary goal of data loading in a database?
- To delete data from the database
- To encrypt data in the database
- To import data into the database for storage and analysis
- To optimize database queries
The primary goal of data loading in a database is to import data into the database for storage and analysis, enabling users to query and manipulate the data effectively.