The correct answer is B. Checkpoints. CompTIA DataSys+ explains that checkpoints play a critical role in database recovery mechanisms by limiting the amount of transaction log data that must be processed during recovery. Log-based recovery relies on transaction logs to restore the database to a consistent state after a failure. If checkpoints are infrequent, the database engine must replay a large portion of the transaction log, which increases recovery time and system overhead.
A checkpoint is a process where the database writes all modified (dirty) pages from memory to disk and records a synchronization point in the transaction log. After a checkpoint completes, the database knows that all changes up to that point are safely stored on disk. During recovery, the system only needs to process log entries that occurred after the most recent checkpoint, significantly reducing recovery workload and time.
DataSys+ highlights checkpoints as a key performance and availability feature, especially in systems with high transaction volumes.
Option A, deadlocks, are concurrency issues that occur when two or more transactions block each other indefinitely. While deadlock handling is important for transaction throughput, it does not reduce log-based recovery overhead. Option C, locks, control concurrent access to data and help maintain consistency, but they do not affect how much log data must be replayed during recovery. Option D, indexes, improve query performance but can actually increase logging activity because index changes are also logged.
CompTIA DataSys+ emphasizes that effective recovery planning includes optimizing logging behavior and checkpoint frequency. Properly configured checkpoints strike a balance between runtime performance and recovery efficiency by reducing excessive log replay without causing unnecessary disk I/O.
Therefore, to best mitigate the overhead caused by log-based recoveries, the database manager should use checkpoints, making option B the correct and fully verified answer.

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.

The join that is most appropriate for the analyst to use to create this database view is option D. This join uses the LEFT JOIN clause to combine the client table and the invoice table based on the matching values in the Key column. The WHERE clause filters out the rows where the Invoice.Key column is not null, meaning that the client has made a purchase. The result is a view that shows only the clients who have not made any purchases yet. The other options either do not produce the desired result or have syntax errors. For example, option A would show only the clients who have made purchases, option B would show only the invoices that do not have a matching client, and option C would show all the clients regardless of their purchase status. References: CompTIA DataSys+ Course Outline, Domain 1.0 Database Fundamentals, Objective 1.2 Given a scenario, execute database tasks using scripting and programming languages.

The computer service that associates IP network addresses with text-based names in order to facilitate identification and connectivity is IDNS. IDNS, or Internet Domain Name System (DNS), is a service that translates domain names into IP addresses and vice versa. Domain names are human-readable names that identify websites or devices on the internet, such as www.comptia.org or www.google.com. IP addresses are numerical identifiers that locate websites or devices on the internet, such as 104.18.26.46 or 142.250.72.238.
IDNS helps users to access websites or devices using domain names instead of IP addresses, which are easier to remember and type. IDNS also helps administrators to manage websites or devices using domain names instead of IP addresses, which are more flexible and scalable. The other options are either different computer services or not related to IP network addresses or text-based names at all. For example, LDAP, or Lightweight Directory Access Protocol, is a service that provides access to directory information such as users, groups, or devices on a network; NTP, or Network Time Protocol, is a service that synchronizes the clocks of computers or devices on a network; DHCP, or Dynamic Host Configuration Protocol, is a service that assigns IP addresses and other network configuration parameters to computers or devices on a network. References: CompTIA DataSys+ Course Outline, Domain 2.0 Database Deployment, Objective 2.1 Given a scenario, select an appropriate database deployment method.

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. References: CompTIA DataSys+ Course Outline, Domain 2.0 Database Deployment, Objective 2.2 Given a scenario, create database objects using scripting and programming languages.