Microsoft DP-300 Administering Microsoft Azure SQL Solutions Exam Dumps and Practice Test Questions Set 2 Q21-40

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Question 21: 

You need to implement a strategy that allows multiple Azure SQL Databases to share resources efficiently and reduce overall cost, while still maintaining individual database isolation. Which feature should you use?

A) Elastic Pool
B) Business Critical tier
C) Hyperscale tier
D) Read Scale-Out

Answer:  A) Elastic Pool

Explanation:

Elastic Pool is designed to allow multiple databases to share a set of allocated resources, including compute and storage. This is particularly useful when the databases have varying or unpredictable workloads, as resources can be dynamically utilized by whichever database requires them at a given time. By pooling resources, organizations can reduce the overall cost compared to provisioning resources for each database individually, which might result in underutilization and higher expenses. Elastic Pools maintain isolation at the database level, ensuring that performance of one database does not directly interfere with another, while still taking advantage of shared resources.

The Business Critical tier is primarily focused on providing low-latency storage, high availability, and maximum performance for a single database. While it offers excellent resiliency and throughput for individual workloads, it does not provide a mechanism for multiple databases to share resources. Each database in Business Critical tier is provisioned with its own dedicated compute and storage, meaning costs can escalate quickly if multiple databases are involved. For organizations looking to optimize cost across several databases, Business Critical is not the most efficient choice.

The Hyperscale tier is designed for extremely large databases where storage and compute need to scale independently. It supports rapid scaling of storage up to petabytes and allows fast replication for read-only workloads. Hyperscale is ideal for workloads that require very high capacity and flexibility, but it is not intended for cost optimization through shared resources among multiple databases. Each database in Hyperscale still maintains its own compute and storage environment, so the tier does not address the problem of efficiently consolidating resources across several databases.

Read Scale-Out allows offloading read-only workloads from the primary database to secondary replicas. This helps improve performance for read-heavy workloads by distributing the query load but does not provide shared compute or storage across multiple databases. It is a feature for performance optimization rather than cost management. In scenarios where multiple databases have varying workloads and require resource efficiency, Elastic Pool is the correct approach because it achieves a balance of cost reduction, resource sharing, and isolation, which none of the other options can provide simultaneously.

Question 22: 

You must secure sensitive data in an Azure SQL Database while allowing computations without revealing the raw data to database administrators. Which feature should you use?

A) Transparent Data Encryption
B) Always Encrypted with secure enclaves
C) Row-Level Security
D) Dynamic Data Masking

Answer: B) Always Encrypted with secure enclaves

Explanation:

Transparent Data Encryption (TDE) encrypts the data at rest to protect it from unauthorized access to the storage level. While TDE ensures that backup and physical storage are secure, it does not prevent privileged users or database administrators from accessing the plaintext data during queries. TDE focuses on encryption for disk and backup security but does not provide the capability to execute computations on encrypted data while keeping the data confidential from administrators, which is a key requirement in this scenario.

Row-Level Security is a feature that restricts access to specific rows within a table based on the characteristics of the user executing the query. While this allows for more granular access control, it does not encrypt the data itself, and administrators with sufficient privileges can still see all underlying data. Row-Level Security helps enforce access policies but does not provide cryptographic protection for sensitive data during processing or guarantee that data cannot be accessed by the database system itself.

Dynamic Data Masking obfuscates sensitive information in query results, typically by replacing certain characters or digits to hide the true value. This is useful for preventing accidental exposure of data to certain users or applications, but it does not encrypt the data. Masking is not a cryptographic security measure and does not prevent database administrators from accessing the raw values. Its primary use is for presentation-level obfuscation, not for true data protection during computations.

Always Encrypted with secure enclaves enables sensitive data to remain encrypted while still allowing certain computations, such as range comparisons or aggregations, to be performed securely within the enclave. The keys used for encryption are stored client-side and never exposed to the database engine or administrators. This ensures that sensitive information remains confidential even during operations that require computation on the data. By combining encryption with secure enclaves, this feature fulfills the requirement of maintaining confidentiality while allowing database operations, making it the correct choice in this context.

Question 23: 

You want to monitor Azure SQL Database for anomalous activities and receive proactive alerts for suspicious patterns. Which feature should you enable?

A) Threat Detection
B) Query Store
C) Automatic Plan Correction
D) Transparent Data Encryption

Answer:  A) Threat Detection

Explanation:

Threat Detection continuously monitors databases for potential security threats, such as unusual login attempts, SQL injection patterns, or access from unexpected geographic locations. It automatically analyzes activity patterns and generates alerts to inform administrators or security teams about suspicious behavior. This proactive monitoring helps organizations respond to potential breaches quickly and maintain a secure database environment. Threat Detection is specifically designed to detect anomalies, making it the most suitable option for proactive security monitoring.

Query Store captures the historical performance data of queries, execution plans, and runtime statistics. While this is highly useful for troubleshooting performance issues, identifying regressions, and tuning workloads, it does not provide any direct security monitoring or alerting for anomalous activity. Its primary focus is performance analysis rather than threat detection, so it cannot fulfill the requirement for proactive alerts related to suspicious behavior.

Automatic Plan Correction helps identify query performance regressions and automatically forces a previous plan to restore optimal performance. It is valuable for maintaining consistent query performance, but it does not monitor security or detect abnormal access patterns. Its function is entirely performance-oriented, not security-related, and therefore it cannot serve as a tool for detecting anomalous activity in the database.

Transparent Data Encryption ensures that the database is encrypted at rest, protecting data from unauthorized access to the storage level. However, it does not provide monitoring or alerts for suspicious activity. While TDE is essential for securing stored data, it does not address the requirement of detecting and alerting on unusual behaviors. Threat Detection remains the only feature among the options that combines continuous monitoring, anomaly detection, and proactive alerting to address security concerns effectively.

Question 24: 

You are tasked with ensuring your Azure SQL Database has minimal downtime during maintenance and that client connections are automatically redirected in the event of failover. Which feature is most appropriate?

A) Auto-Failover Groups
B) Zone Redundant Configuration
C) Accelerated Database Recovery
D) Ledger

Answer:  A) Auto-Failover Groups

Explanation:

Auto-Failover Groups provide automatic failover capabilities between primary and secondary databases, often across regions, to ensure high availability. They handle redirection of client connections transparently during failover events, minimizing downtime and disruption. This feature is designed for mission-critical applications where maintaining continuous access is essential, ensuring that failover processes do not require manual intervention and that applications can reconnect seamlessly.

Zone Redundant Configuration replicates resources across multiple availability zones within a region to improve resiliency against localized hardware or data center failures. While it provides higher availability within a single region, it does not handle cross-region failover or automatic client redirection. The failover process is limited to a single region, and clients may need to manually reconnect to another replica in case of a broader regional outage.

Accelerated Database Recovery focuses on reducing the time it takes for a database to recover after a failure or rollback operation. While this improves recovery time, it does not automatically redirect client connections or provide high availability across regions. Its scope is limited to speeding up recovery processes rather than maintaining seamless connectivity during failover events.

Ledger provides tamper-evident transaction records to ensure integrity and immutability. While it is useful for auditing and verifying data authenticity, it does not provide high availability, failover capabilities, or client redirection. Auto-Failover Groups remain the only option that ensures both minimal downtime and seamless client connection management in failover scenarios, making it the appropriate choice.

Question 25: 

You need to optimize an Azure SQL Database that is rarely active to reduce compute costs while maintaining automatic scaling when activity resumes. Which deployment model should you choose?

A) Serverless compute tier
B) Hyperscale tier
C) Business Critical tier
D) Elastic Pool

Answer:  A) Serverless compute tier

Explanation:

Serverless compute tier is designed for workloads with intermittent or unpredictable activity. It automatically pauses the database when there are no active connections and resumes when new activity occurs, significantly reducing compute costs during idle periods. The automatic scaling feature adjusts compute resources according to workload requirements, ensuring performance is available when needed without incurring unnecessary charges during inactivity.

Hyperscale tier supports very large databases with independent scaling of storage and compute, allowing rapid expansion to handle high-capacity workloads. While Hyperscale is highly flexible and performant, it is not designed to pause or automatically scale down during inactivity, which means costs remain consistent regardless of utilization. For infrequently used databases, this can result in unnecessary expenditure.

Business Critical tier is optimized for low-latency, high-performance workloads, with features such as high IOPS and redundancy for fast recovery. However, the resources are continuously allocated, and there is no built-in mechanism to pause the database during idle periods. This tier is suitable for mission-critical, high-traffic applications, but not for cost-saving optimization for rarely used databases.

Elastic Pool allows multiple databases to share resources, which can optimize costs for groups of databases with variable workloads. However, it does not include the ability to pause idle databases individually. Resources are still allocated from the pool, so infrequently used databases continue to consume compute resources. Serverless compute tier is specifically designed to minimize costs for databases with sporadic usage while providing automatic scaling when activity resumes, making it the ideal choice in this scenario.

Question 26:

You want to improve the performance of read-heavy workloads in Azure SQL Database without impacting write operations. Which feature allows read-only workloads to be directed to secondary replicas?

A) Read Scale-Out
B) Auto-Failover Groups
C) Transparent Network Redirect
D) Elastic Pool

Answer:  A) Read Scale-Out

Explanation:

Read Scale-Out is a feature available in the Business Critical tier of Azure SQL Database that allows read-only queries to be offloaded to one or more secondary replicas. This separation of read and write workloads enables applications that perform a high volume of read operations, such as reporting or analytics, to avoid competing with write-intensive operations on the primary database. By directing read-only traffic to secondary replicas, it improves overall performance and reduces latency for read-heavy workloads without negatively impacting transactional consistency on the primary node.

Auto-Failover Groups, on the other hand, are primarily designed for high availability and disaster recovery. They enable automatic failover between primary and secondary databases in different regions, ensuring minimal downtime during outages. While they include readable secondary replicas, their main focus is failover and continuity rather than performance optimization for read workloads, so they are not the ideal choice for offloading reads in a read-heavy environment.

Transparent Network Redirect is a client-side feature that helps applications reconnect automatically to the primary database after a failover event. It ensures minimal connection disruption, but it does not provide a mechanism to direct read workloads to secondary replicas. Its role is strictly in connection management after failover, not in workload distribution or performance enhancement.

Elastic Pool is used to share resources across multiple databases to optimize cost and manage variable utilization patterns. While it helps prevent resource over-provisioning and allows multiple databases to share compute and storage, it does not create read replicas or distribute read traffic. Therefore, it does not directly address performance improvement for read-heavy workloads.

Considering all options, Read Scale-Out is specifically designed for improving read performance by offloading read-only queries to secondary replicas, making it the most suitable solution for scenarios where read-heavy workloads need optimization without affecting writes.

Question 27: 

You need to encrypt sensitive columns while allowing queries to be executed without exposing plaintext to the server. Which feature should you use?

A) Always Encrypted
B) Transparent Data Encryption
C) Dynamic Data Masking
D) Column-Level Permissions

Answer: A) Always Encrypted

Explanation:

Always Encrypted is designed to protect sensitive data in SQL databases by ensuring that encryption and decryption occur exclusively on the client side. This allows applications to run queries, joins, and filters on encrypted data without exposing plaintext to the database engine. This capability is essential for scenarios where regulatory requirements or organizational policies prohibit the database server from ever seeing unencrypted sensitive data, such as personally identifiable information (PII) or financial data.

Transparent Data Encryption (TDE) encrypts the entire database at rest, securing it against unauthorized access to the underlying storage. While TDE is effective for protecting data files and backups, it does not encrypt data in memory during query execution, meaning the database engine has access to plaintext. This makes TDE unsuitable when the requirement is to prevent the server itself from seeing sensitive column data.

Dynamic Data Masking is another feature intended to obscure sensitive data in query results. While it can limit the visibility of specific columns to certain users, it does not encrypt the data. Users with direct access to the database or administrative privileges can still see the plaintext, so it does not fulfill the requirement of client-side encryption.

Column-Level Permissions restrict which users can access specific columns but do not apply encryption or cryptographic protections. This feature helps enforce security policies but does not prevent exposure of sensitive data during query execution if the server itself is compromised or if the user has sufficient privileges.

Among these options, Always Encrypted is the only feature that guarantees sensitive column data remains encrypted at all times on the server, while still enabling query operations, making it the correct choice for client-side encryption scenarios.

Question 28: 

You need to assess which SQL Server workloads are compatible with Azure SQL Database and obtain SKU recommendations for migration. Which tool should you use?

A) Azure Migrate: Database Assessment
B) Azure Advisor
C) SQL Server Profiler
D) Database Experimentation Assistant

Answer: A) Azure Migrate: Database Assessment

Explanation: 

Azure Migrate: Database Assessment is specifically designed to analyze on-premises SQL Server instances for migration readiness to Azure SQL Database. It examines the workload, schema, and feature usage to determine whether each database is compatible with Azure SQL Database and identifies potential blockers or unsupported features. In addition, it generates SKU recommendations based on database size, resource utilization, and performance metrics, providing actionable guidance for choosing the appropriate service tier.

Azure Advisor offers personalized best practice recommendations for Azure resources, focusing on cost optimization, high availability, performance, and security. While useful for ongoing optimization in Azure, it does not provide workload compatibility assessments or SKU recommendations for migrating on-premises databases to Azure SQL Database.

SQL Server Profiler captures and traces query execution events and server activity, which can help analyze performance and diagnose issues. However, it is not a migration assessment tool and does not evaluate feature compatibility or suggest target Azure SKUs.

Database Experimentation Assistant allows evaluation of performance regressions when upgrading SQL Server versions by replaying workloads in a test environment. While helpful for pre-upgrade testing, it does not provide Azure migration recommendations or compatibility assessments.

Given the requirement to determine compatibility with Azure SQL Database and obtain SKU recommendations, Azure Migrate: Database Assessment is the correct tool, as it is purpose-built for cloud migration assessment and guidance.

Question 29: 

You want to monitor query performance over time and capture execution plans to detect regressions. Which feature should you enable?

A) Query Store
B) Extended Events
C) Intelligent Insights
D) SQL Auditing

Answer: A) Query Store

Explanation: 

Query Store is a feature that continuously captures query execution statistics and execution plans, retaining historical data for analysis over time. This enables database administrators to track performance trends, identify regressions, and understand how query performance changes with schema or configuration updates. By storing execution plans, Query Store allows easy comparison of current and past query behavior, facilitating troubleshooting and tuning of problem queries.

Extended Events provides a lightweight, flexible mechanism to collect diagnostic information about server activity. While extremely powerful for capturing detailed performance and operational data, it requires careful configuration and expertise to interpret the results. It does not inherently provide a straightforward way to monitor historical query performance trends or store execution plans for regression analysis.

Intelligent Insights is an Azure SQL feature that analyzes telemetry data and provides automated performance recommendations. While it highlights potential issues and suggests optimizations, it does not maintain detailed historical execution plans or allow comprehensive tracking of query performance regressions over time.

SQL Auditing logs database activity for compliance purposes, capturing events such as logins, schema changes, and data access. While valuable for auditing, it does not monitor query performance or retain execution plan history, and therefore cannot support performance regression analysis.

Query Store is specifically designed to monitor query performance over time, retain execution plans, and provide insights into query regressions, making it the correct feature for this scenario.

Question 30: 

You need to protect Azure SQL Database against accidental deletion or data loss while supporting long-term retention. Which feature should you implement?

A) Long-Term Backup Retention
B) Transparent Data Encryption
C) Failover Groups
D) Auto-Failover Groups

Answer: A) Long-Term Backup Retention

Explanation: 

Long-Term Backup Retention allows organizations to retain full database backups for extended periods, typically several years, enabling point-in-time recovery to safeguard against accidental deletion or data corruption. This feature ensures compliance with regulatory requirements for data retention and provides a reliable method for restoring historical versions of a database if needed. Administrators can configure retention policies based on organizational needs, giving flexibility for long-term protection strategies.

Transparent Data Encryption secures data at rest by encrypting database files and backups, protecting them from unauthorized access. However, it does not provide backup copies or long-term retention; it is primarily a security feature rather than a disaster recovery solution.

Failover Groups provide high availability and geo-replication capabilities by maintaining secondary databases that can be failed over in case of an outage. While they ensure minimal downtime during regional failures, they do not retain backups for extended periods, meaning they cannot serve as a long-term data protection or recovery solution.

Auto-Failover Groups extend the capabilities of failover groups by automating failover processes between regions. They improve availability and resilience but, like failover groups, do not store historical backups for long-term retention.

Among these options, Long-Term Backup Retention directly addresses the requirement for protecting against accidental deletion or data loss with support for extended retention, making it the correct choice.

Question 31: 

You need to encrypt data at rest in Azure SQL Database to meet regulatory compliance requirements. Which feature should you enable?

A) Transparent Data Encryption
B) Always Encrypted
C) Dynamic Data Masking
D) Row-Level Security

Answer:  A) Transparent Data Encryption

Explanation:

Transparent Data Encryption (TDE) is designed to encrypt the entire database at rest, including all data files and backups, without requiring changes to the application. This encryption occurs transparently, meaning users and applications continue to operate normally without being aware of the underlying encryption process. TDE ensures that if the database files or backups are accessed outside the authorized environment, the data remains protected. This is especially important for organizations that need to comply with regulatory frameworks such as GDPR, HIPAA, or PCI DSS, which often mandate encryption of sensitive information stored at rest.

Always Encrypted, on the other hand, encrypts specific sensitive columns and protects data both at rest and in transit. However, its primary purpose is to ensure that the encryption keys remain on the client side, preventing database administrators or other users from viewing the sensitive data. While Always Encrypted is crucial for protecting sensitive columns like credit card numbers or personal identifiers, it does not automatically encrypt the entire database at rest and therefore does not fully satisfy regulatory compliance requirements that mandate comprehensive encryption of all stored data.

Dynamic Data Masking (DDM) is focused on obfuscating sensitive data when queried by unauthorized users. For example, it can replace the visible digits of a social security number with a mask, but the underlying data stored in the database remains unencrypted. DDM is primarily a mechanism to reduce accidental exposure of sensitive information in query results, rather than a full encryption solution for regulatory compliance. While it adds a layer of security, it does not satisfy requirements for encrypting stored data at rest.

Row-Level Security (RLS) provides fine-grained access control by allowing specific users to view only the rows of a table that meet certain criteria. While RLS is excellent for enforcing policies and restricting data access based on user attributes, it does not provide any form of encryption. Data stored on disk or in backups remains in clear text unless another encryption feature such as TDE is enabled.

Transparent Data Encryption is the appropriate choice in this scenario because it meets the requirement of encrypting data at rest across the entire database. Unlike Always Encrypted, which is column-specific, or DDM and RLS, which focus on masking and access control, TDE provides comprehensive encryption that ensures compliance with regulatory mandates for protecting stored data. It is transparent to applications, simple to enable, and automatically applies to backups, making it a robust and widely adopted solution for data-at-rest protection.

Question 32: 

You want to automatically correct query performance regressions in Azure SQL Database without manual intervention. Which feature should you use?

A) Automatic Plan Correction
B) Query Store
C) Intelligent Insights
D) Threat Detection

Answer:  A) Automatic Plan Correction

Explanation:

Automatic Plan Correction is a feature of Azure SQL Database that monitors query performance continuously and automatically identifies regressions caused by changes in the execution plan. When a regression is detected, the system can force a previously known good plan to restore query performance without any manual intervention. This is particularly useful in dynamic environments where query patterns and data volumes can change over time, potentially leading to degraded performance for critical workloads. By automating the correction, it minimizes downtime and ensures consistent application performance.

Query Store captures historical information about queries, execution plans, and runtime statistics. It is an excellent tool for analyzing performance trends, troubleshooting query issues, and understanding execution plan changes over time. However, Query Store by itself does not automatically fix performance regressions; it only stores the data and allows administrators to manually force plans if needed. Its value lies in providing insight and the ability to perform manual intervention rather than fully automating performance corrections.

Intelligent Insights provides proactive monitoring and recommendations for improving database performance. It analyzes workloads, identifies potential issues, and suggests corrective actions such as index tuning or query optimizations. However, Intelligent Insights does not automatically apply these recommendations; a database administrator must review and implement them. Therefore, while useful for identifying opportunities for optimization, it does not meet the requirement for automatic intervention.

Threat Detection focuses on identifying unusual or suspicious activities, such as SQL injection attempts, anomalous logins, or potential security breaches. It provides alerts and recommendations to secure the database, but it does not manage query performance or execution plans. Threat Detection is security-oriented, not performance-oriented, and therefore does not address the requirement in this scenario.

Automatic Plan Correction is the correct choice because it provides fully automated detection and remediation of query performance regressions. Unlike Query Store and Intelligent Insights, which require manual review or intervention, Automatic Plan Correction ensures that performance issues are addressed immediately, maintaining database efficiency and reliability without administrative overhead. It is specifically designed to keep execution plans stable and applications running smoothly in Azure SQL Database environments.

Question 33: 

You need to ensure that database backups are geo-redundant to protect against regional disasters. Which feature should you configure?

A) Geo-Redundant Backup Storage
B) Long-Term Backup Retention
C) Auto-Failover Groups
D) Transparent Data Encryption

Answer:  A) Geo-Redundant Backup Storage

Explanation:

Geo-Redundant Backup Storage (GRS) is specifically designed to protect Azure SQL Database backups against regional outages. When enabled, GRS automatically replicates all backups to a secondary Azure region, ensuring that if the primary region suffers a catastrophic failure, the backups remain accessible and can be restored in the secondary region. This level of redundancy is critical for meeting disaster recovery and compliance requirements that mandate protection against site-level failures. It offers peace of mind by making sure that both short-term and long-term backups are safeguarded in a geographically separate location.

Long-Term Backup Retention (LTR) is intended to retain backups for extended periods, often months or years, to satisfy archival or compliance requirements. While LTR ensures that older backup copies are preserved and available for future restoration, it does not inherently provide geographic replication. Backups retained for long-term purposes could still be stored only in the primary region unless paired with geo-redundancy, which makes LTR insufficient by itself for regional disaster protection.

Auto-Failover Groups focus on high availability and seamless failover of active databases rather than backup replication. They automatically switch client connections from a primary database to a secondary one during outages, minimizing downtime for users. While they help maintain application availability, Auto-Failover Groups do not inherently replicate backups for disaster recovery purposes; they replicate data to a secondary instance for failover, but the feature does not cover historical backup storage across regions.

Transparent Data Encryption (TDE) encrypts the database at rest to ensure that data and backups are secure from unauthorized access. While TDE is essential for data security and regulatory compliance, it does not replicate backups to another region. TDE alone does not satisfy the requirement for regional disaster protection because encryption does not provide geographic redundancy or backup recovery in case of a regional failure.

Geo-Redundant Backup Storage is the correct choice because it specifically addresses the need for protecting backups against regional disasters. Unlike LTR, which focuses on retention duration, or Auto-Failover Groups, which focus on live database availability, GRS ensures that backup copies exist in multiple regions, enabling restoration in a different geographic location. TDE complements security but does not provide redundancy. Therefore, for disaster recovery at the backup level, Geo-Redundant Backup Storage is the optimal solution.

Question 34: 

You want to implement row-level access control based on user attributes in Azure SQL Database. Which feature should you use?

A) Row-Level Security
B) Dynamic Data Masking
C) Always Encrypted
D) Transparent Data Encryption

Answer:  A) Row-Level Security

Explanation:

Row-Level Security (RLS) allows database administrators to create security policies that filter rows based on the characteristics or attributes of the user executing the query. This enables fine-grained control, ensuring that users can only access the subset of data that they are authorized to see. For example, employees in a sales department can be restricted to viewing only their region’s sales records. RLS is implemented at the database level and enforces security automatically during query execution, without requiring changes to application code.

Dynamic Data Masking (DDM) modifies the way data is presented to users by masking sensitive values in query results. For example, it may display only the last four digits of a Social Security Number or partially hide a credit card number. While DDM helps prevent accidental exposure of sensitive information, it does not prevent a user from accessing rows they should not see. It only masks the data when retrieved, which is fundamentally different from row-level access enforcement.

Always Encrypted protects sensitive columns by encrypting data so that only authorized client applications can decrypt it. While this ensures that certain sensitive data remains unreadable by unauthorized users, it does not control which rows a user can access. It focuses on protecting sensitive data content rather than restricting data visibility based on user attributes, which is the requirement in this scenario.

Transparent Data Encryption encrypts the database at rest and secures backups, ensuring that unauthorized access to stored data does not expose information. However, TDE does not provide mechanisms for access control. Users with proper database permissions can still query any row; encryption only protects against offline data theft, not live query access.

Row-Level Security is the correct choice because it enforces access policies directly at the row level based on user attributes, meeting the requirement for attribute-based access control. Unlike DDM, Always Encrypted, or TDE, which focus on masking or encrypting data, RLS ensures that users cannot see rows they are not authorized to access. It provides the precise, automated enforcement needed for secure multi-user environments.

Question 35: 

You need to redirect client connections to a secondary database automatically after a failover. Which feature should you use?

A) Transparent Network Redirect
B) Auto-Failover Groups
C) Read Scale-Out
D) Elastic Pool

Answer:  A) Transparent Network Redirect

Explanation: 

Transparent Network Redirect (TNR) is designed to automatically update client connections when a failover occurs in Azure SQL Database. When a primary database becomes unavailable due to maintenance, outages, or other failures, TNR ensures that client applications are redirected to the new active secondary database without requiring changes to connection strings or manual intervention. This feature allows applications to maintain continuity and minimizes downtime during failovers.

Auto-Failover Groups (AFG) provide high availability and disaster recovery by maintaining a secondary database that can take over if the primary fails. While AFG ensures that a failover target exists, the client connection may not automatically redirect without additional configuration, or it may require retry logic in the application. AFG focuses on replication and failover rather than the seamless redirection of client connections.

Read Scale-Out enables offloading of read workloads to secondary replicas, improving performance for read-heavy operations. While it allows users to query secondary replicas, it does not automatically handle redirection of connections after a failover. Its purpose is performance optimization rather than failover connectivity management.

Elastic Pool allows multiple databases to share resources efficiently, optimizing costs in scenarios with variable workloads. While beneficial for resource management, Elastic Pool does not handle failover events or connection redirection. Its scope is limited to compute and storage optimization across databases.

Transparent Network Redirect is the correct choice because it ensures seamless redirection of client connections after failover. Unlike Auto-Failover Groups, Read Scale-Out, or Elastic Pool, TNR directly addresses the requirement of maintaining application connectivity without requiring application-level changes, providing a truly automated failover experience.

Question 36: 

You want to reduce the time it takes to recover from long-running transactions in Azure SQL Database. Which feature should you use?

A) Accelerated Database Recovery
B) Auto-Failover Groups
C) Transparent Data Encryption
D) Long-Term Backup Retention

Answer:  A) Accelerated Database Recovery

Explanation:

Accelerated Database Recovery (ADR) is designed specifically to reduce recovery times for long-running transactions in Azure SQL Database. ADR works by maintaining a persistent version store that tracks transaction versions separately from the main database. This mechanism allows the database engine to quickly undo transactions during a rollback or after a crash without waiting for traditional transaction log recovery, which can be slow for large, long-running operations. As a result, downtime caused by rollbacks is minimized, and the database can resume normal operations almost immediately.

Auto-Failover Groups, on the other hand, focus on high availability and disaster recovery. They enable automatic failover between primary and secondary databases, ensuring business continuity during server or regional failures. However, they do not directly reduce the time needed to recover from long-running transactions. While they can improve overall system uptime, the recovery of individual complex transactions remains governed by standard transaction log recovery unless ADR is enabled.

Transparent Data Encryption (TDE) secures data at rest by encrypting database files and backups. TDE ensures that unauthorized users cannot read database files without the proper encryption keys, which is crucial for security and compliance. However, TDE does not modify transaction processing or rollback behavior and therefore has no impact on the time required to recover long-running transactions. Its primary role is data protection rather than performance optimization during recovery.

Long-Term Backup Retention allows Azure SQL Database to retain backups for extended periods, which is useful for compliance, auditing, or historical recovery needs. While it ensures that past data can be restored, it does not accelerate the rollback of active transactions. Restoring from a long-term backup can be a lengthy process and does not address the performance bottleneck caused by complex or lengthy transactional operations.

The correct choice is Accelerated Database Recovery because it is explicitly designed to minimize downtime and speed up recovery for long-running transactions. By separating version management and using a persistent version store, ADR eliminates traditional delays in rolling back complex transactions, which makes it the only option among the four that directly addresses the requirement.

Question 37: 

You want to perform analytics on read-only replicas of a Business Critical Azure SQL Database without affecting the primary workload. Which feature should you enable?

A) Read Scale-Out
B) Auto-Failover Groups
C) Elastic Pool
D) Hyperscale tier

Answer:  A) Read Scale-Out

Explanation:

Read Scale-Out is a feature available for Business Critical Azure SQL Databases that provides one or more secondary replicas capable of handling read-only workloads. This allows heavy analytical queries to run on replicas without consuming resources on the primary database, preserving performance for transactional workloads. It is particularly valuable for reporting, BI dashboards, or complex analytics on production data without causing query contention on the main database.

Auto-Failover Groups are primarily focused on high availability and disaster recovery. They allow for automatic failover to a secondary server in another region or availability zone when the primary fails. While this feature ensures minimal downtime, it is not designed to offload read workloads for analytics. Attempting to use failover groups for reporting could unnecessarily complicate availability management and does not provide dedicated read replicas for analytics.

Elastic Pool is a mechanism for sharing resources, such as CPU and storage, among multiple databases in Azure SQL. It helps optimize costs when databases have fluctuating usage patterns but does not create additional replicas for read workloads. While it efficiently balances compute and storage, it cannot offload queries from a Business Critical database, meaning analytics would still impact primary performance if run directly against the main database.

Hyperscale tier is optimized for very large databases that require fast storage scaling and high performance. While it supports rapid growth and scaling, it does not inherently provide secondary read-only replicas for Business Critical workloads. Using Hyperscale alone would not achieve the goal of running analytics independently of the primary workload.

Read Scale-Out is the correct choice because it directly addresses the requirement to perform analytics on read-only replicas without affecting primary operations. By separating read and write workloads, it provides a performance-efficient solution for reporting and analytical queries.

Question 38: 

You need to enable auditing for Azure SQL Database that writes logs to a central, durable storage location for compliance purposes. Which destination should you use?

A) Azure Storage account
B) Log Analytics workspace
C) Event Hubs
D) Power BI

Answer:  A) Azure Storage account

Explanation:

Azure Storage accounts provide a reliable, scalable, and durable solution for storing audit logs from Azure SQL Database. By sending audit logs to a storage account, organizations can retain data for long periods to satisfy compliance, legal, or regulatory requirements. The storage account also offers options for geo-redundancy and tiered storage, ensuring that auditing logs remain available and cost-effective over time.

Log Analytics workspace is designed for centralized monitoring and querying of operational telemetry. While it can receive and analyze logs, it is optimized for active monitoring rather than long-term compliance storage. Data retention policies are typically shorter, and logs stored in Log Analytics are not primarily intended for long-term archival.

Event Hubs is a streaming platform that enables ingestion of large volumes of event data in real time to downstream processing systems. While Event Hubs is suitable for integrating audit logs with other systems for analysis or alerting, it does not itself serve as a persistent storage solution. Logs must be moved to another storage destination to meet compliance and durability requirements.

Power BI is a reporting and visualization tool that helps turn data into actionable insights. While it can connect to audit logs for dashboards and analytics, it is not a storage platform and cannot provide durable, central storage for compliance purposes. Using Power BI alone would not satisfy regulatory retention requirements.

Azure Storage account is the correct choice because it provides a secure, durable, and centralized repository for audit logs. It ensures long-term retention, meets compliance standards, and allows controlled access, which directly addresses the requirement of storing logs in a central location for auditing purposes.

Question 39: 

You need to scale storage independently of compute for a very large Azure SQL Database with multi-terabyte data. Which tier should you choose?

A) Hyperscale
B) Business Critical
C) General Purpose
D) Serverless

Answer:  A) Hyperscale

Explanation:

Hyperscale tier is designed for very large Azure SQL Databases that require independent scaling of storage and compute resources. Unlike other tiers, Hyperscale decouples storage from compute, allowing databases to grow rapidly into multi-terabyte sizes without being constrained by the amount of compute allocated. It achieves this by using multiple layers of page servers to provide virtually unlimited storage while maintaining high performance and rapid backup/restore capabilities.

Business Critical tier is intended for high-performance workloads with low-latency requirements. It provides high availability with multiple replicas but has storage limits tied to the compute size. This makes it unsuitable for massive, multi-terabyte growth where storage needs may scale independently from CPU or memory.

General Purpose tier provides balanced compute and storage suitable for most typical workloads. While it is cost-effective and reliable, it does not support independent scaling of storage at hyperscale levels. Very large databases may quickly exceed the tier’s storage limits if compute scaling alone is increased.

Serverless tier automatically scales compute resources based on workload demand, which is useful for variable workloads to optimize cost. However, serverless does not provide the same hyperscale storage architecture. Storage is still constrained by the tier’s limits, and multi-terabyte databases cannot scale seamlessly without significant planning.

Hyperscale is the correct choice because it allows storage and compute to scale independently, handles multi-terabyte databases, and supports rapid growth without impacting performance. Its architecture ensures that very large databases are efficiently managed while maintaining operational flexibility.

Question 40: 

You want to automatically detect performance regressions in Azure SQL Database queries and remediate them. Which feature should you enable?

A) Automatic Plan Correction
B) Query Store
C) Intelligent Insights
D) Extended Events

Answer:  A) Automatic Plan Correction

Explanation:

Automatic Plan Correction is a feature that continuously monitors query performance for regressions. It identifies execution plans that are causing performance degradation and automatically forces the database to use a previously known good plan. This proactive approach minimizes downtime and ensures consistent query performance without requiring manual intervention from administrators.

Query Store is a component that collects historical information about queries, execution plans, and runtime statistics. It is highly valuable for analyzing trends, troubleshooting, and understanding query performance over time. However, while it can identify regressions, it does not automatically remediate or force plan corrections. Administrators must manually apply fixes based on the data in the Query Store.

Intelligent Insights provides performance recommendations and proactive alerts for potential issues. It analyzes workloads and offers guidance to improve query efficiency or detect anomalies. However, it only recommends actions and does not implement changes automatically. Administrators still need to review and apply suggestions manually.

Extended Events is a lightweight performance monitoring system for capturing detailed diagnostic information about SQL Server events. It is useful for troubleshooting, auditing, and deep performance analysis, but it does not remediate regressions. Extended Events focuses on data collection and insight generation rather than automated correction of query plans.

Automatic Plan Correction is the correct choice because it not only detects query regressions but also automatically applies a known good execution plan to restore performance. This ensures continuous optimization of the database without manual intervention, directly fulfilling the requirement for automated detection and remediation.