DS0-001 Exam Dumps : CompTIA DataSys+ Certification Exam

The NF (normal form) that is considered the most preferable for relational database design is 3 NF. 3 NF, or Third Normal Form, is a level of normalization that organizes data into tables and columns to reduce redundancy and improve consistency. Normalization is a process that applies a set of rules or criteria to eliminate or minimize the anomalies or problems that may arise from inserting, updating, or deleting data in a database. 3 NF is achieved when a table satisfies the following conditions: - It is in 2 NF (Second Normal Form), which means that every non-key column depends on the whole primary key and not on any subset of it - It has no transitive dependencies, which means that every non-key column depends directly on the primary key and not on any other non-key column 3 NF is considered the most preferable for relational database design because it ensures that each table has only one purpose or theme and that each column has only one value or meaning. This helps avoid data duplication, inconsistency, and update anomalies. The other options are either lower or higher levels of normalization that are either less preferable or less practical for relational database design. For example, 1 NF (First Normal Form) is the lowest level of normalization that requires each column to have atomic values and each row to have a unique identifier; 4 NF (Fourth Normal Form) is a higher level of normalization that requires each table to have no multi-valued dependencies, which means that there are no columns that can have more than one value for the same primary key value; 2 NF (Second Normal Form) is an intermediate level of normalization that requires each non-key column to depend on the whole primary key and not on any subset of it. References: CompTIA DataSys+ Course Outline, Domain 1.0 Database Fundamentals, Objective 1.2 Given a scenario, execute database tasks using scripting and programming languages.

A result of an on-path attack on a system is a Wi-Fi network that redirects to clones of legitimate websites. An on-path attack is a type of attack that intercepts and modifies the traffic between two parties without their knowledge or consent. An attacker can use an on-path attack to create a rogue Wi-Fi network that mimics a legitimate one, and then redirect the users to fake websites that look like the ones they intended to visit. The attacker can then steal the users’ personal or financial information, such as usernames, passwords, credit card numbers, or bank account details. The other options are either results of different types of attacks or not related to attacks at all. For example, a website that has crashed and is no longer accessible may be a result of a denial-of-service attack, an email from an unknown source requesting bank account details may be a result of a phishing attack, and a web application that returns the addresses of its customers may be a result of a poor design or a data breach. References: CompTIA DataSys+ Course Outline, Domain 4.0 Data and Database Security, Objective 4.4 Given a scenario, identify common types of attacks against databases.

The correct answer is B. INTEGER. According to CompTIA DataSys+ objectives, choosing the appropriate data type is a fundamental database design decision that directly affects storage efficiency, performance, and data integrity. Product quantities represent whole numbers, such as the count of items in inventory, units sold, or stock remaining. Because these values do not require decimal precision, an integer-based data type is the most appropriate and storage-efficient choice.

An INTEGER data type is specifically designed to store whole numbers and consumes significantly less storage than floating-point or double-precision data types. DataSys+ emphasizes that database administrators should always select the smallest data type that accurately represents the data, as this reduces disk usage, improves cache efficiency, and enhances query performance—especially in large tables with millions of rows.

Option A, DOUBLE, and option D, FLOAT, are floating-point data types intended for values that require decimal precision, such as scientific measurements or financial calculations involving fractions. These data types consume more storage and can introduce rounding errors, making them unsuitable for storing simple quantity counts. Using floating-point types for inventory quantities would be inefficient and unnecessary.

Option C, COUNT, is not a data type at all. COUNT is an aggregate SQL function used in queries to calculate the number of rows or non-null values in a result set. It cannot be used to define a column in a table schema.

CompTIA DataSys+ highlights that efficient schema design includes proper data typing to minimize resource consumption while maintaining accuracy. By using INTEGER for product quantities, administrators ensure optimal storage utilization, faster indexing, and predictable arithmetic operations.

Therefore, to store product quantities accurately while consuming the minimal amount of storage, INTEGER is the best and most appropriate choice, making option B the correct and fully verified answer.