This question is part of the Visualization and Reporting domain, focusing on delivery methods for dashboards. The requirement for displaying financial transactions "as they are updated" implies a need for real-time updates and interactivity, which narrows down the options.

Real-time (Option A): Real-time delivery ensures the dashboard reflects the latest data as transactions are updated, meeting the requirement.

Snapshot (Option B): A snapshot provides a static view at a specific point, not suitable for ongoing updates.

Dynamic (Option C): A dynamic dashboard allows for interactivity and can be updated as data changes, complementing real-time delivery.

Static (Option D): Static dashboards don’t update automatically, making this incorrect.

Ad hoc (Option E): Ad hoc delivery is for one-time reports, not ongoing updates.

Time series (Option F): Time series refers to a data type or visualization, not a delivery method.

The DA0-002 Visualization and Reporting domain includes understanding "the appropriate visualization in the form of a report or dashboard" with delivery methods Real-time and dynamic methods best support the need for updated financial transaction dashboards.

This question pertains to the Data Analysis domain, focusing on date calculations. The task is to calculate the difference between dates in a file and the current date, requiring proper date handling.

Convert data to date format and use date functions (Option A): Flat files often store dates as strings (e.g., "2023-01-01"). Converting them to a date format (e.g., using Python’s datetime or SQL’s TO_DATE) allows the use of date functions (e.g., DATEDIFF) to calculate the difference to the current date, which is the best approach.

Validate the date format with logical functions and use date functions to analyze (Option B): Validation is unnecessary if conversion handles format issues, making this overly complex.

Use date functions to analyze the data with no conversion (Option C): Without converting to a date format, date functions may fail if the data is stored as strings.

Transform data to a numerical value and use mathematical functions (Option D): This is inefficient and error-prone compared to using date functions.

The DA0-002 Data Analysis domain includes "applying the appropriate descriptive statistical methods," and converting to date format followed by date functions is the standard method for such calculations.

This question pertains to the Data Concepts and Environments domain, focusing on types of server infrastructure. The task is to identify an infrastructure where a company owns and maintains all servers.

Private (Option A): A private infrastructure (often on-premises) means the company owns and maintains its own servers, typically in a private data center, which matches the requirement.

Cloud (Option B): Cloud infrastructure is managed by third-party providers, not owned by the company.

Hybrid (Option C): Hybrid combines on-premises and cloud, so not all servers are owned by the company.

Public (Option D): Public infrastructure is a cloud model shared across multiple organizations, not owned by the company.

The DA0-002 Data Concepts and Environments domain includes understanding "data environments," and a private infrastructure requires the company to purchase and maintain its own servers.

This question falls under the Data Analysis domain, focusing on analytical methods for reporting. The task is to identify the causes of customer churn over three years, which involves analyzing historical data.

Inferential (Option A): Inferential statistics make predictions or generalizations about a population, not focused on identifying causes in historical data.

Descriptive (Option B): Descriptive analytics summarizes historical data to identify patterns and causes (e.g., reasons for churn), which fits the task.

Prescriptive (Option C): Prescriptive analytics provides recommendations, which goes beyond identifying causes.

Predictive (Option D): Predictive analytics forecasts future outcomes, not focused on historical causes.

The DA0-002 Data Analysis domain includes "applying the appropriate descriptive statistical methods," and descriptive analytics is best for identifying causes in historical data.

This question pertains to the Data Governance domain, focusing on troubleshooting data freshness issues in reports. The report isn’t showing the latest data despite a recent source update, indicating a potential refresh or connectivity issue.

Event log (Option A): Event logs might provide insight into errors, but they’re not the first step for checking data freshness.

User privileges (Option B): Privileges might affect access, but the user can see the report, so this isn’t the issue.

Database connection (Option C): If the database connection failed or isn’t refreshing properly, the report won’t reflect the latest data, making this the first thing to check.

Report corruption (Option D): Corruption might cause errors, but it’s less likely than a connectivity issue for this scenario.

The DA0-002 Data Governance domain includes "data quality control concepts," such as ensuring data freshness by verifying database connections.