On the other hand, real-time data synchronization continuously updates data across systems as changes occur. While this method ensures that all locations have the most current data, it can also lead to high resource consumption, as it requires constant monitoring and immediate data transfer. This can overwhelm network bandwidth, particularly in environments with high transaction volumes. Incremental data synchronization, however, strikes a balance between efficiency and performance. This technique only transfers the data that has changed since the last synchronization event, significantly reducing the amount of data transmitted over the network. By focusing solely on the modifications, it minimizes bandwidth usage and optimizes system performance, allowing for quicker updates without overwhelming the network. This method is particularly advantageous for organizations with large datasets that experience frequent changes, as it ensures data consistency while conserving resources. In summary, for a multinational corporation dealing with a large volume of frequently changing data, incremental data synchronization is the most efficient choice. It effectively balances the need for up-to-date information with the constraints of bandwidth and system performance, making it the ideal solution for maintaining data consistency across multiple locations.

In contrast, the Basic service tier is not suitable for high transaction workloads as it is limited in terms of performance and scalability. It is designed for small-scale applications and would likely lead to performance bottlenecks under heavy load. Moreover, implementing a single database model without sharding or partitioning can hinder performance, especially as the database grows. Sharding and partitioning are techniques that distribute data across multiple databases or tables, which can significantly enhance performance by allowing parallel processing of transactions. Lastly, relying solely on manual scaling during peak hours is inefficient and can lead to downtime or degraded performance if the scaling is not executed promptly. Automation in scaling is vital to ensure that resources are allocated dynamically based on real-time demand, thus maintaining optimal performance levels. In summary, the best approach for the company is to leverage the Hyperscale service tier, which provides the necessary flexibility and performance to accommodate their high transaction requirements while ensuring efficient resource management.

The Single Database option provides a dedicated database with a fixed set of resources, which can lead to underutilization during off-peak hours and higher costs. In contrast, the Managed Instance option offers a fully managed SQL Server instance in Azure, which is beneficial for applications requiring SQL Server features but may also lead to higher costs if not fully utilized. The Elastic Pool option is particularly advantageous for scenarios with variable workloads across multiple databases. It allows multiple databases to share a pool of resources, which can dynamically allocate resources based on demand. This means that during peak hours, the databases can utilize more resources, while during off-peak hours, they can scale down, leading to significant cost savings. The Elastic Pool is designed to optimize resource usage and minimize costs, making it the most suitable choice for the company’s needs. In summary, the Elastic Pool option provides the necessary scalability to handle peak loads while ensuring cost-effectiveness during off-peak times, making it the ideal solution for the company’s migration to Azure SQL Database.

For Database A, the current cost is $300. With a 30% increase in usage, the new cost can be calculated as follows: \[ \text{New Cost for Database A} = \text{Current Cost} + (\text{Current Cost} \times \text{Increase Percentage}) \] \[ = 300 + (300 \times 0.30) \] \[ = 300 + 90 = 390 \] For Database B, the current cost is $600. With a 10% decrease in usage, the new cost is calculated similarly: \[ \text{New Cost for Database B} = \text{Current Cost} – (\text{Current Cost} \times \text{Decrease Percentage}) \] \[ = 600 – (600 \times 0.10) \] \[ = 600 – 60 = 540 \] Now, to find the total projected cost for both databases, we add the new costs together: \[ \text{Total Projected Cost} = \text{New Cost for Database A} + \text{New Cost for Database B} \] \[ = 390 + 540 = 930 \] However, it appears that the options provided do not include this total. Therefore, let’s analyze the options again. The correct calculation should reflect the anticipated changes accurately. If we consider the projected costs based on the percentage changes, we can see that the calculations yield a total of $930, which is not among the options. This indicates a potential oversight in the options provided. In a real-world scenario, it is crucial to ensure that the projected costs align with the anticipated usage changes and that the options reflect realistic outcomes based on the calculations. This exercise emphasizes the importance of understanding how usage impacts costs in Azure SQL Database pricing models and the necessity of accurate forecasting for effective cost management. In conclusion, the projected costs for both databases, based on the anticipated changes in usage, should be carefully calculated and verified against the available pricing models to ensure accurate budgeting and financial planning.

Geo-replication supports point-in-time restore capabilities, allowing the company to recover data to a specific moment before a failure occurred. This is crucial for financial services, where data integrity and availability are paramount. The geo-replication feature also provides automatic failover capabilities, which means that in the event of a failure, the system can switch to the secondary database with minimal manual intervention. On the other hand, a manual backup and restore process (option b) may not provide the same level of immediacy and reliability. While daily backups can help recover data, they do not allow for point-in-time recovery unless the company implements a more complex strategy involving transaction log backups, which can be cumbersome and prone to human error. Using Azure Blob Storage for backups (option c) is a viable option for data storage, but it does not inherently provide the point-in-time recovery feature that geo-replication offers. Restoring from Blob Storage would also involve additional steps and potential downtime. Lastly, configuring a secondary Azure SQL Database in a different region without geo-replication (option d) would not provide the necessary high availability and disaster recovery capabilities, as it lacks the automatic synchronization and failover features that geo-replication provides. In summary, the best approach for the company is to implement Azure SQL Database’s built-in geo-replication feature, as it aligns perfectly with their requirements for high availability, disaster recovery, and point-in-time recovery, ensuring minimal data loss and downtime in the event of a failure.

To address this issue, the database administrator should first review the firewall settings in the Azure portal. They should ensure that the IP addresses of all applications that need access to the database are included in the allowed list. Additionally, the administrator should check for any recent changes to the firewall rules that might have affected connectivity. While the other options present potential issues, they are less likely to be the root cause in this context. For instance, running an outdated version of SQL Server may lead to compatibility issues, but it would not typically cause intermittent connectivity problems. Similarly, while application optimization is important, it is less likely to be the primary cause if the network team has confirmed that the infrastructure is functioning correctly. Lastly, reaching resource limits in an Azure subscription could lead to throttling, but this would generally manifest as consistent performance degradation rather than intermittent connectivity issues. In summary, the database administrator should focus on verifying and adjusting the firewall rules to ensure that all necessary IP addresses are permitted access to the database, thereby resolving the connectivity issues effectively.

On the other hand, simply increasing the DTU allocation may provide a temporary boost in performance but does not resolve the underlying issues related to inefficient queries or blocking. This approach can lead to increased costs without addressing the root cause of the performance degradation. Rewriting all queries to use temporary tables instead of table variables is not a universally applicable solution. While temporary tables can be beneficial in certain scenarios, they may introduce additional overhead and complexity, particularly if not used judiciously based on the specific requirements of each query. Lastly, enabling automatic tuning features can be beneficial, but it should not be done without first analyzing the specific queries causing performance issues. Automatic tuning can help optimize performance by recommending indexes or adjusting query plans, but it is essential to have a clear understanding of the workload and the specific problems being faced to ensure that the tuning recommendations are appropriate. In summary, implementing filtered indexes on frequently queried columns is the most effective strategy to reduce blocking and improve query performance, as it directly addresses the inefficiencies in query execution while minimizing the impact on system resources.

To achieve 3NF, a database must first satisfy the conditions of the First Normal Form (1NF) and the Second Normal Form (2NF). In 1NF, all attributes must contain atomic values, and there should be no repeating groups. In 2NF, the database must be in 1NF, and all non-key attributes must be fully functionally dependent on the primary key, meaning that there should be no partial dependencies. Once these conditions are met, to achieve 3NF, the database must ensure that all non-key attributes are only dependent on the primary key and not on other non-key attributes. This eliminates transitive dependencies, which can lead to anomalies during data manipulation operations such as insertions, updates, and deletions. While Boyce-Codd Normal Form (BCNF) is a stricter version of 3NF that addresses certain types of anomalies not covered by 3NF, it is not necessary for the scenario described, as the primary goal is to eliminate transitive dependencies. Therefore, the development team should aim for the Third Normal Form (3NF) to ensure that their database design is efficient and maintains data integrity while minimizing redundancy.

RBAC allows organizations to define roles within the system and assign permissions based on these roles rather than individual users. This means that users are granted access to data and functions that are relevant to their job responsibilities, thereby limiting exposure to sensitive information. For instance, a customer service representative may have access to customer contact details but not to financial records, while a financial analyst may have access to both. This structured approach not only simplifies permission management but also enhances accountability, as access can be easily audited based on roles. In contrast, allowing all users to access the database with a single shared account (option b) undermines security by making it impossible to track individual actions and increases the risk of unauthorized access. Discretionary access control (option c) can lead to excessive permissions being granted, as users may not fully understand the implications of sharing access to sensitive data. Finally, enabling full database encryption without access restrictions (option d) does not address the need for controlled access; while encryption protects data at rest, it does not prevent unauthorized users from accessing the database in the first place. In summary, adopting RBAC aligns with best practices for database security, ensuring that access is appropriately restricted and managed according to the specific needs of users within the organization. This approach not only protects sensitive information but also supports compliance with regulatory requirements regarding data access and privacy.

In contrast, allowing partial dependencies, as suggested in option b, would violate the principles of 2NF and could lead to redundancy and update anomalies. Creating a single table that combines Customers, Orders, and Products, as mentioned in option c, would lead to a denormalized structure that complicates data retrieval and increases redundancy, making it difficult to maintain data integrity. Lastly, while using surrogate keys (option d) can simplify certain aspects of database design, it is not a substitute for ensuring that all attributes are properly normalized. Surrogate keys should be used judiciously and not at the expense of adhering to normalization principles. Thus, the correct approach is to prioritize the establishment of primary keys and ensure that all non-key attributes are fully functionally dependent on those keys, thereby adhering to normalization principles and promoting a well-structured database design.

In contrast, migrating all databases at once can lead to significant downtime and potential data integrity issues, especially if unexpected problems arise during the migration. Using a single database instance for all applications may simplify management but can lead to performance bottlenecks and complicate scaling efforts. Lastly, relying solely on manual backups without testing the restore process is a risky practice; it does not guarantee that the backups are valid or that they can be restored successfully in case of a failure during migration. Best practices for database migration to Azure include thorough planning, testing, and validation of the migration process. This involves assessing the current database environment, understanding application dependencies, and ensuring that the target Azure environment is properly configured. Additionally, utilizing Azure Database Migration Service can facilitate the migration process, providing tools for assessment, migration, and ongoing synchronization. By prioritizing a phased migration strategy with continuous synchronization, the company can effectively manage risks and ensure a smooth transition to Azure SQL Database.

In contrast, relying solely on a traditional relational database schema without modifications would limit the AI model’s ability to access diverse data types and may lead to performance bottlenecks when processing large datasets. Creating a separate database instance for the AI model introduces complexities related to data synchronization, which can lead to data integrity issues and increased maintenance overhead. Lastly, while using a NoSQL database might provide faster access to data, it compromises the relational integrity that is often critical in CRM systems, where relationships between entities (like customers, orders, and products) must be preserved for accurate analytics. Therefore, the best approach is to implement a data lake architecture, as it not only enhances the AI model’s access to varied datasets but also supports the necessary data integrity and security measures required in a cloud-based relational database management system. This strategy aligns with best practices in data management and AI integration, ensuring that the organization can leverage its data assets effectively while maintaining robust governance and compliance standards.

By adhering to the principle of least privilege, organizations can significantly enhance their security posture, as it minimizes the risk of insider threats and accidental data exposure. This approach is particularly important in the context of compliance with data protection regulations such as the General Data Protection Regulation (GDPR) and the Health Insurance Portability and Accountability Act (HIPAA), which mandate strict controls over access to sensitive personal data. In contrast, the other options present significant security risks. For instance, utilizing a single sign-on (SSO) solution without proper role differentiation can lead to excessive access rights, making it easier for unauthorized users to gain access to sensitive data. Enabling public access to the database undermines security entirely, as it exposes the database to potential attacks from anyone on the internet, regardless of password strength. Lastly, while regularly changing passwords can be a good practice, doing so without implementing additional access controls or monitoring does not address the underlying issue of access management and can lead to user frustration and poor password hygiene. In summary, implementing RBAC with the principle of least privilege not only aligns with best practices in database security but also supports compliance with critical data protection regulations, making it the most effective strategy for safeguarding sensitive customer information.

Following the trigger, the first action should be to send an email notification to the sales team, informing them of the new order. This is crucial for timely communication and ensures that the sales team is aware of incoming orders. Next, the order details must be logged into a database. This action is vital for record-keeping and allows for future analysis of sales data. It is important to ensure that the database connection is properly configured to allow for seamless data entry. The most critical part of this workflow is the conditional action that checks if the order total exceeds $500. This is implemented using a conditional control in Logic Apps, which evaluates the order total. If the condition is met (i.e., the total exceeds $500), a notification is sent to the finance department for approval. This step is essential for managing high-value transactions and ensuring that they are reviewed appropriately. The other options present various shortcomings. For instance, option b lacks automation for logging orders, which defeats the purpose of using Logic Apps. Option c does not incorporate any conditional logic, which is necessary for handling high-value orders. Lastly, option d sends notifications to finance without considering the order total, which could lead to unnecessary approvals and inefficiencies. In summary, the correct approach involves a structured sequence of actions that includes triggers, notifications, logging, and conditional checks, ensuring a comprehensive and efficient workflow automation process.

In contrast, the state-based approach, which involves maintaining the entire schema in a single file, can lead to challenges in collaboration. When multiple developers make changes to the schema, merging these changes can become complex and error-prone. This approach may also require more extensive testing to ensure that the entire schema is consistent after each update, as opposed to the more granular testing that can be performed with migration scripts. The hybrid approach, while it combines elements of both methods, may not provide the same level of clarity and control as a pure migration-based approach. Manual change tracking is generally not recommended in a collaborative environment due to its inherent risks of human error and lack of automation. Using a migration-based approach allows for better version control, easier rollbacks, and clearer documentation of changes, which are essential for maintaining the integrity of the database schema in a dynamic development environment. This method aligns well with best practices in database management, ensuring that all team members can work effectively without stepping on each other’s toes.

In contrast, implementing a blanket data retention policy without considering the specific needs of each data type can lead to non-compliance, as GDPR mandates that personal data should not be retained longer than necessary for the purposes for which it was processed. Relying solely on third-party audits may provide some level of assurance, but it does not replace the need for the organization to actively engage in compliance efforts and understand their own data processing activities. Lastly, focusing exclusively on technical measures like encryption, while neglecting organizational policies, fails to address the holistic approach required for effective governance. GDPR emphasizes the importance of both technical and organizational measures, including staff training, data governance policies, and incident response plans. Thus, the most effective approach for the company is to conduct DPIAs for all new projects involving personal data, as this aligns with GDPR requirements and promotes a proactive stance on data protection and compliance.

Azure Cosmos DB with provisioned throughput, while highly scalable, requires a fixed amount of throughput to be provisioned, which may lead to unnecessary costs during low usage periods. Azure SQL Managed Instance offers a more traditional approach with fixed resources, which does not align well with the need for dynamic scaling. Similarly, Azure Database for PostgreSQL with dedicated compute does not provide the flexibility of scaling down resources during low usage, leading to potentially higher costs. The serverless model of Azure SQL Database allows for automatic pause and resume capabilities, meaning that if the database is not in use, it can pause and incur no charges, further enhancing cost-effectiveness. This feature is particularly beneficial for applications with unpredictable workloads, as it ensures that the company only pays for what it uses. Additionally, the serverless option supports auto-scaling, which is crucial for maintaining performance during peak times without manual intervention. In summary, the Azure SQL Database serverless option is the most suitable choice for the company’s requirements, as it effectively balances performance, scalability, and cost management in a dynamic workload environment.

The Single Database option provides a dedicated database with a fixed set of resources, which can lead to underutilization during low-demand periods and overutilization during peak times, resulting in higher costs. The Managed Instance option offers a fully managed SQL Server instance with compatibility for SQL Server features, but it is typically more expensive and may not be necessary for applications that do not require full SQL Server capabilities. The Elastic Pool option is particularly advantageous for applications with variable workloads. It allows multiple databases to share a pool of resources, which can dynamically allocate compute and storage resources based on demand. This means that during periods of low activity, resources can be conserved, while during peak times, additional resources can be utilized without the need for provisioning separate databases. This flexibility leads to cost savings and efficient resource management, making it the ideal choice for applications with fluctuating workloads. In summary, the Elastic Pool option provides a balanced approach to managing costs while ensuring that performance needs are met, especially for applications that experience variable workloads. This understanding of Azure SQL Database deployment options is essential for making informed decisions during the migration process.

On the other hand, while the number of tables and indexes (option b) is important for understanding the database structure, it does not provide insights into performance. Similarly, the total number of records in each table (option c) is more about data volume than performance efficiency. Lastly, database backup frequency and retention policy (option d) are critical for data protection and recovery but do not directly relate to the performance of the database post-migration. Thus, focusing on query execution time and resource utilization metrics allows the administrator to assess whether the migration has maintained or improved the database’s performance, ensuring that the application relying on the database continues to function optimally. This approach aligns with best practices for validating database migrations, which emphasize performance monitoring as a key component of the post-migration process.

SQL Authentication, while a viable option, requires users to manage separate credentials specifically for the database, which can lead to increased administrative overhead and potential security risks. It does not support features like MFA or conditional access, making it less secure compared to AAD. Windows Authentication is primarily used in on-premises environments and relies on Active Directory Domain Services. While it can be used in Azure SQL Database, it is not as flexible as AAD, especially in hybrid scenarios where users may need to access resources from various locations and devices. Managed Identity Authentication is designed for Azure services to authenticate to other Azure services without storing credentials in code. While it is useful for service-to-service authentication, it does not apply to user authentication scenarios directly. In summary, Azure Active Directory Authentication is the optimal choice for organizations looking to streamline user access while maintaining robust security measures. It supports a wide range of authentication scenarios and is particularly beneficial in environments that utilize both on-premises and cloud resources, ensuring that users can access the database securely and efficiently with their existing corporate credentials.

To accurately represent this relationship, a junction table (also known as a bridge table or associative entity) should be implemented. This junction table will contain foreign keys referencing the primary keys of both the products and suppliers tables. By doing so, the database can maintain a clear and organized structure that allows for efficient querying and data manipulation. This design not only prevents data duplication but also enforces referential integrity, ensuring that each product-supplier relationship is valid and traceable. On the other hand, creating separate tables for products and suppliers without establishing any relationships would lead to data isolation, making it impossible to track which suppliers provide which products. Similarly, using a single table to store all information would result in a denormalized structure that complicates data retrieval and increases the risk of data anomalies. Allowing duplicate entries for products and suppliers would further exacerbate redundancy issues, leading to inconsistencies and difficulties in maintaining accurate records. In summary, the correct approach involves implementing a many-to-many relationship through a junction table, which effectively supports the complex interactions between products and suppliers while upholding the principles of data integrity and minimizing redundancy. This design choice is essential for creating a robust and scalable inventory management system.

When configuring active geo-replication, it is also important to consider the performance implications of latency. While having the primary and secondary databases in the same region might seem beneficial for reducing latency, it defeats the purpose of geo-replication, which is to protect against regional failures. Therefore, option (b) is incorrect as it does not align with the fundamental goal of geo-replication. Additionally, while using the same performance tier for both databases (option c) can help maintain consistency in performance, it is not as critical as ensuring geographical separation. The performance tier should be chosen based on the workload requirements of each database, but it does not directly impact the replication process itself. Lastly, setting up the secondary database as a read-write replica (option d) is not a valid configuration for active geo-replication. In this setup, the secondary database is read-only, which allows for offloading read operations and improving performance for users querying the secondary database. This configuration is designed to ensure that the primary database remains the sole point for write operations, thus maintaining data integrity and consistency. In summary, the most critical consideration when setting up active geo-replication is ensuring that the primary and secondary databases are in different Azure regions, as this provides the necessary geographical redundancy to protect against regional outages.

The best strategy is to implement a geo-redundant storage solution with automated failover capabilities that replicates data every 5 minutes. This approach ensures that data is consistently backed up and can be restored within the required RTO, as the automated failover allows for immediate switching to a backup system in the event of a failure. The 5-minute replication interval also comfortably meets the RPO requirement, as it limits potential data loss to just 5 minutes. In contrast, the other options present significant drawbacks. A single-region backup solution that performs daily backups would not meet the RPO, as it could result in up to 24 hours of data loss. A manual recovery process relying on offsite backup tapes would likely exceed the RTO, leading to unacceptable downtime. Lastly, while a multi-region active-active configuration provides high availability, it may introduce unnecessary complexity and cost, especially when the defined RTO and RPO can be met with a simpler geo-redundant solution. Thus, the selected strategy effectively balances the need for rapid recovery and minimal data loss with cost considerations, making it the most suitable choice for the company’s disaster recovery plan.

Increasing the budget to accommodate current spending levels does not address the underlying issue of resource waste and may lead to further inefficiencies. Simply shutting down all virtual machines could disrupt business operations and may not necessarily lead to cost savings if critical resources are turned off. Migrating workloads to a cheaper cloud provider without a thorough analysis could result in unforeseen costs and operational challenges, as the company may not fully understand the implications of such a move. In addition, implementing a tagging strategy aligns with Azure’s best practices for cost management, which emphasize the importance of visibility and accountability in resource usage. This approach not only helps in identifying waste but also supports ongoing optimization efforts, such as rightsizing resources and leveraging reserved instances for predictable workloads. By taking a structured approach to cost management, the company can ensure that its cloud spending aligns with its business goals while maximizing the value derived from its Azure investments.

While increasing the database DTU may seem like a viable option, it is often a temporary fix that does not address the root cause of the performance issues. Simply adding more resources without understanding the underlying problems can lead to unnecessary costs and may not yield the desired improvements. Disabling the VNet service endpoint is counterproductive, as it compromises the security of the database by allowing direct public access. This could expose the database to potential threats and vulnerabilities, which is contrary to best practices in database security. Implementing a geo-replication strategy could help distribute the load across multiple regions, but it is a more complex solution that may not be necessary if the performance issues can be resolved through query optimization. Geo-replication is typically used for disaster recovery and high availability rather than immediate performance improvements. In summary, the most effective approach to resolving the connectivity and performance issues in this scenario is to focus on analyzing and optimizing the query execution plans, ensuring that the database operates efficiently while maintaining secure access through the VNet service endpoint. This method not only addresses the current performance challenges but also aligns with best practices for database management in Azure.

Optimizing these queries can lead to significant performance improvements without immediately increasing costs associated with higher DTU allocations. This approach aligns with best practices in database management, where understanding and optimizing query performance is often more effective than simply adding resources. While increasing the DTU allocation (option b) may provide a temporary fix, it does not address the root cause of the performance issues and could lead to unnecessary costs if the underlying queries are not optimized. Implementing a read replica (option c) can help with read-heavy workloads but does not directly resolve issues related to CPU utilization caused by inefficient queries. Enabling auto-scaling (option d) may also help manage load fluctuations, but again, it does not solve the immediate problem of high CPU usage due to poorly performing queries. In summary, the most effective first step in this scenario is to analyze the query performance using Query Store, as it provides actionable insights that can lead to long-term improvements in database performance. This methodical approach ensures that the administrator addresses the core issues rather than merely applying a band-aid solution.

The GDPR mandates that organizations must ensure the protection of personal data, which includes implementing measures that are proportionate to the risk associated with the data being processed. By categorizing data into tiers, the organization can prioritize resources and compliance efforts effectively, ensuring that sensitive data receives the highest level of protection. Focusing solely on encryption, as suggested in option b, is insufficient because while encryption is a critical component of data protection, it does not address the broader requirements of data governance, such as data minimization, purpose limitation, and ensuring individuals’ rights are upheld. Establishing a centralized data repository without a classification scheme, as mentioned in option c, could lead to mismanagement of sensitive data and potential non-compliance with GDPR, as it does not provide the necessary context for data handling practices. Lastly, relying solely on periodic audits of data access logs, as indicated in option d, is a reactive approach that fails to establish a proactive governance framework. Compliance requires ongoing management and oversight, not just retrospective audits. In summary, a robust data classification scheme is essential for effective data governance and compliance with GDPR, as it enables organizations to manage their data assets responsibly and in accordance with regulatory requirements.

In contrast, offline migration requires the database to be taken offline, meaning that no transactions can occur during the migration. This approach can lead to significant downtime, which is often unacceptable for financial services companies that rely on real-time data access for their operations. Hybrid migration, while a viable option, usually combines elements of both online and offline strategies, but may not provide the continuous access that the company needs. Snapshot migration, on the other hand, involves creating a point-in-time copy of the database, which can also lead to downtime and may not ensure data consistency if changes occur during the snapshot process. Therefore, for a financial services company that prioritizes minimal downtime and data consistency, online migration is the most suitable strategy. It allows for real-time data access and ensures that the migration process does not disrupt ongoing operations, making it the preferred choice in scenarios where continuous availability is critical.

The wait type PAGEIOLATCH_SH is particularly relevant when discussing memory issues. This wait type occurs when a thread is waiting for a page to be read from disk into memory. If the database is experiencing high PAGEIOLATCH_SH waits, it suggests that the system is frequently reading data from disk rather than accessing it from memory, which can significantly slow down query performance. This situation often arises when there is insufficient memory allocated to the database, leading to increased disk I/O operations. On the other hand, CXPACKET waits are typically associated with parallel query execution and can indicate issues with query plans rather than memory. LCK_M_X waits indicate that a transaction is waiting for an exclusive lock, which is more related to concurrency issues than memory constraints. ASYNC_NETWORK_IO waits occur when the server is waiting for the client to read data, which is not directly related to memory resources. Understanding these wait types is crucial for diagnosing performance issues effectively. By focusing on PAGEIOLATCH_SH, the database administrator can determine whether increasing the memory allocation or optimizing the database design to reduce disk I/O is necessary. This nuanced understanding of wait statistics allows for targeted troubleshooting and performance tuning, ensuring that the database operates efficiently under varying workloads.

To revert to the previous query plan, the administrator should utilize the “Force Plan” feature within the Query Store. This feature enables the administrator to select a specific execution plan that was previously used for the query and enforce its use moving forward. This is particularly useful when a new plan is less efficient than the one that was previously in use. Disabling Query Store and restarting the database (option b) would not address the issue, as it would remove the historical performance data and not revert the execution plan. Manually rewriting the query (option c) may improve performance but does not directly address the issue of the changed execution plan. Increasing the DTU allocation (option d) could provide more resources but does not guarantee that the query will execute with the optimal plan, as the underlying issue of the execution plan change remains unresolved. In summary, the most effective approach to ensure optimal performance in this scenario is to leverage the Query Store’s capabilities to force the previous execution plan, thereby restoring the query’s performance to its prior state. This method not only resolves the immediate performance issue but also allows the administrator to maintain control over query execution plans moving forward.