Free DA0-001 sample questions

Correct answer: Creating a dimensional model with sales as the fact table and store, product, and date as dimensions.

A dimensional model using a star or snowflake schema is the industry standard for data warehousing and analytics. By creating a central fact table for sales metrics and separate dimension tables for store, product, and date, queries become simpler and much faster. This design directly supports the business requirement of slicing and dicing data by these key dimensions.

Correct answer: Join

A join operation is used to combine rows from two or more tables based on a related column between them. In this scenario, the `customer_id` column exists in both datasets and can be used as the key to join the customer names with their corresponding order amounts. Append/Union operations stack datasets vertically and are not suitable for this task.

Correct answer: True

This statement is true. A primary key is a fundamental constraint in relational database design. Its core purpose is to uniquely identify each record in a table. To achieve this, it enforces two rules: the values must be unique (no duplicates) and they cannot be NULL.

Correct answers: Mean; Standard deviation

The mean (average) provides a measure of central tendency, giving the analyst a quick understanding of the typical value in a numerical column. Calculating the mean and standard deviation are fundamental first steps in profiling numerical data to understand its distribution and spread.

The standard deviation measures the amount of variation or dispersion of a set of values. A low standard deviation indicates that the values tend to be close to the mean, while a high standard deviation indicates that the values are spread out over a wider range. This is crucial for understanding data consistency and identifying potential outliers.

Correct answer: To offload analytical queries from the transactional system, preventing performance degradation on the OLTP database.

The primary reason for a data warehouse is to separate analytical workloads (OLAP) from transactional workloads (OLTP). Running complex, long-running analytical queries directly against an OLTP database can lock tables and severely degrade the performance of the primary business application. The data warehouse is structured specifically for analysis and reporting, ensuring the operational system remains responsive.

Correct answer: To arrange identical data into groups, often in conjunction with aggregate functions.

The `GROUP BY` clause is used to group rows that have the same values in specified columns into summary rows. It is almost always used with aggregate functions like `COUNT()`, `MAX()`, `MIN()`, `SUM()`, `AVG()` to perform a calculation on each group. For example, `GROUP BY category` would allow you to calculate the `SUM(sales)` for each product category.

Correct answer: Data standardization

Data standardization is the process of transforming data into a common format. In this case, converting various phone number formats into a single, consistent one (e.g., `5551234567`) is a classic example of standardization. This is a critical step in data cleansing to ensure data quality and enable accurate matching, grouping, and analysis.

Correct answer: Invalid data entry

The use of '0' as a placeholder for missing age data is an example of invalid data entry. While technically a number, '0' is not a valid age for a customer and acts as a hidden NULL value. Including these invalid entries in a calculation for the average will skew the result, making it artificially low. This is a common data quality problem that must be addressed by filtering out or imputing these values.

Correct answer: ORDER BY profit DESC LIMIT 5

To find the top 5 most profitable customers, the analyst must first calculate the profit for each customer (likely using SUM and GROUP BY), then sort the results in descending order of profit using `ORDER BY profit DESC`. Finally, the `LIMIT 5` clause restricts the output to only the top 5 rows from the sorted result set.

Correct answer: Association analysis

Association analysis, also known as market basket analysis, is a technique used to discover relationships between variables in large datasets. Its primary application is to find items that are frequently purchased together. The output is a set of association rules, such as '{Diapers} -> {Beer}', which can be used for product placement, promotions, and recommendation engines.