CompTIA CompTIA Data+ DA0-001 New Questions

Filter on any of the responses that do not say “January” and update them to “January”. This is because filtering and updating are data cleansing techniques that can be used to ensure data consistency, which means that the data is uniform and follows a standard format. By filtering on any of the responses that do not say “January” and updating them to “January”, the analyst can make sure that all the responses for the month of January are written in the same way. The other steps arenot appropriate for ensuring data consistency. Here is why:

Deleting any of the responses that do not have “January” written out would result in data loss, which means that some information would be missing from the data set. This could affect the accuracy and reliability of the analysis.

Replacing any of the responses that have “01” would not solve the problem of data inconsistency, because there would still be two different ways of writing the month of January: “Jan” and “January”. This could cause confusion and errors in the analysis.

Sorting any of the responses that say “Jan” and updating them to “01” would also not solve the problem of data inconsistency, because there would still be two different ways of writing the month of January: “01” and “January”. This could also cause confusion and errors in the analysis.

Transactional SQL typically refers to:

OLTP (Online Transaction Processing) workloads

Frequent inserts, updates, and deletes

Strong ACID properties (Atomicity, Consistency, Isolation, Durability)

The standard database model used for this is the relational database:

Data stored in normalized tables with defined relationships (primary/foreign keys)

SQL used as the primary query language

Designed for transactional reliability and integrity

Why the other options are incorrect:

Snowflake schema (A) and Star schema (D) are data warehouse schema designs, used primarily for analytics (OLAP), not for core transactional workloads.

Hierarchical (B) is an older model largely superseded by relational for general transactional systems and is not the primary model referenced for “transactional SQL.”

Therefore, the best answer is Relational (C).

CompTIA Data+ Reference (concept alignment):

DA0-001 Objectives – Data concepts: relational databases as the core technology for transactional systems.

Study content: difference between relational OLTP systems vs analytic schemas (star/snowflake).

A telephone number formatted as XXX-XXX-XXXX would be considered a text data type, as it is composed of alphanumeric characters and symbols. A numeric data type is composed of only numbers, such as integers or decimals. A date data type is composed of values that represent dates or times, such as YYYY-MM-DD or HH:MM:SS. A float data type is composed of numbers with fractional parts, such as 3.14 or 0.5. Reference: Guide to CompTIA Data+ and Practice Questions - Pass Your Cert

A star schema is a type of database schema that consists of one fact table and multiple dimension tables. The fact table contains the measures or metrics of the business process, such as sales, orders, or transactions. The dimension tables contain the attributes or characteristics of the business entities, such as products, customers, or locations. The fact table is connected to the dimension tables by foreign keys that reference the primary keys of the dimension tables. The fact table is located at the center of the schema, while the dimension tables are located at the edges, forming a star-like shape1.

A star schema is an example of a denormalized schema, which means that the dimension tables are not normalized and may contain redundant or repeated data. This is done to improve the performance and simplicity of queries, as there are fewer joins and tables involved. A star schema is suitable for data warehouses and business intelligence applications that require fast and efficient data retrieval2.