The two factors that the database administrator should monitor to gauge server health are CPU usage and memory usage. CPU usage is the percentage of time that the processor (CPU) of the server is busy executing instructions or processes. CPU usage indicates how much workload the server can handle and how fast it can process requests. High CPU usage may affect the performance or availability of the server and cause delays or errors. Memory usage is the amount of physical memory (RAM) or virtual memory (swap space) that the server uses to store data or run applications. Memory usage indicates how much space the server has to store temporary or intermediate data or results. High memory usage may affect the performance or availability of the server and cause swapping or paging. The other options are either not relevant or not direct indicators of server health. For example, transaction logs are files that record the changes made by transactions on the database; network sniffer is a tool that captures and analyzes network traffic; domain controllers are servers that manage user authentication and authorization in a network; firewall traffic is the amount of data that passes through a firewall device or software.

The best way for the administrator to accomplish this task is to create a materialized view. A materialized view is a type of view that stores the result of a query on one or more tables as a separate table in the database. A materialized view can aggregate data from multiple tables in a way that does not impact the original tables, and then provide this information to a department as a single source of truth. A materialized view also improves query performance and efficiency by reducing the need to recompute complex queries every time they are executed. The other options are either not suitable or not optimal for this task. For example, creating indexes on those tables may improve query performance on individual tables, but not on aggregated data; creating a new database may require additional resources and maintenance, and may introduce inconsistency or redundancy; creating a function may require additional coding and execution, and may not store the result as a separate table. Reference: CompTIA DataSys+ Course Outline, Domain 2.0 Database Deployment, Objective 2.2 Given a scenario, create database objects using scripting and programming languages.

The option that best describes a collection of data that shares the same properties or attributes is entity set. An entity set is a term used in the entity-relationship (ER) model, which is a conceptual model for designing and representing databases. An entity set is a collection of entities that have the same type or characteristics, such as students, courses, products, etc. An entity is an object or thing that can be identified and distinguished from others, such as a specific student, course, product, etc. An entity set can have one or more attributes that describe the properties or features of the entities, such as name, age, price, etc. An entity set can also have one or more relationships with other entity sets that define how the entities are associated or connected, such as enrolled, taught by, purchased by, etc. The other options are either different terms or not related to the ER model at all. For example, relation set is a term used in the relational model, which is a logical model for implementing and manipulating databases; ER model is a term used to refer to the entity-relationship model itself; tuples are rows or records in a table or relation.

The correct answer is A. Creating views to support repeat queries. CompTIA DataSys+ emphasizes that views are an effective way to improve performance, usability, and consistency when users frequently run complex queries against live production tables. A view is a virtual table based on a predefined SQL query that presents data in a simplified and reusable format without physically storing the data.
When analysts run complex queries directly against live tables, it can increase CPU usage, I/O operations, and locking contention, potentially impacting other users and critical workloads. By creating views, the database administrator can encapsulate complex joins, filters, and aggregations into a single logical object. Analysts can then query the view instead of repeatedly executing expensive SQL statements. This reduces query complexity, improves readability, and can lead to more predictable performance. DataSys+ highlights views as a best practice for supporting reporting and analytical workloads while minimizing disruption to transactional systems.
Option B, removing data redundancy, is related to normalization and data integrity, not query performance optimization for analysts. Option C, modifying the data in the table, risks data integrity issues and does not address performance concerns. Option D, deleting records from the table, may reduce table size but is not an appropriate or safe method for improving user query performance and can result in data loss.
CompTIA DataSys+ also notes that views can enhance security by limiting analysts' access to only the required columns or rows, further protecting live data. In some implementations, indexed or materialized views can offer additional performance benefits for repeated queries.
Therefore, the best solution to improve analyst performance when running complex, repeat queries on live tables is creating views to support repeat queries, making option A the correct and fully verified answer.