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Question 141:
An organization needs to implement a hybrid transactional/analytical processing solution with Azure SQL Database. Which feature enables near real-time analytics on transactional data?
A) Database snapshots
B) Read scale-out
C) Hyperscale architecture
D) Azure Synapse Link
Answer: D
Explanation:
Azure Synapse Link enables near real-time analytics on transactional data by providing hybrid transactional/analytical processing capabilities for Azure SQL Database. This feature automatically replicates transactional data to Azure Synapse Analytics in near real-time, enabling analytical queries without impacting operational workload performance.
Azure Synapse Link works by capturing changes from Azure SQL Database using change feed technology and automatically synchronizing them to a dedicated SQL pool or serverless SQL pool in Azure Synapse Analytics. The replication occurs continuously with minimal latency, typically within minutes of transaction commit. The analytical copy uses columnar storage optimized for analytical queries, while the operational database maintains row-based storage optimized for transactional operations.
This architecture separates transactional and analytical workloads, preventing resource contention. Business intelligence queries, complex aggregations, and analytical processing run against the Azure Synapse copy without consuming resources from the operational database. Applications continue executing OLTP operations with consistent performance while analysts perform concurrent analytical queries on current data.
Azure Synapse Link eliminates the need for traditional ETL pipelines that batch-load data to analytics platforms. The near real-time synchronization provides current data for analytics and reporting, enabling time-sensitive business decisions based on fresh operational data. The automated synchronization reduces administrative overhead compared to manually maintaining separate analytical databases.
Option A is incorrect because database snapshots create point-in-time read-only copies but do not provide continuous analytical processing capabilities. Option B is wrong because read scale-out directs read queries to replicas but does not provide specialized analytical storage or processing. Option C is incorrect because Hyperscale architecture provides storage scaling but does not specifically enable hybrid transactional/analytical processing with separate analytical engines.
Question 142:
A database administrator needs to configure Azure SQL Managed Instance to use a custom DNS server for name resolution. Where should the DNS configuration be applied?
A) Database-level configuration
B) Instance-level configuration
C) Virtual network configuration
D) Subnet configuration
Answer: C
Explanation:
Virtual network configuration is where DNS settings should be applied for Azure SQL Managed Instance to use a custom DNS server for name resolution. Managed Instance inherits DNS configuration from the virtual network where it is deployed, enabling integration with on-premises DNS infrastructure for hybrid scenarios.
Azure SQL Managed Instance deploys into a dedicated subnet within an Azure virtual network. The virtual network configuration includes DNS server settings that specify which DNS servers provide name resolution for resources in that network. When custom DNS servers are configured at the VNet level, all resources including Managed Instance use those servers for hostname resolution rather than Azure-provided DNS.
Custom DNS configuration is essential for hybrid scenarios where Managed Instance needs to resolve on-premises hostnames. For example, when configuring Windows authentication with on-premises Active Directory, Managed Instance must resolve domain controller hostnames. When using linked servers to on-premises SQL Servers, hostname resolution enables connection establishment. Custom DNS servers, typically hosted on-premises or on Azure VMs, provide this resolution capability.
Configuration involves specifying custom DNS server IP addresses in the virtual network settings. Azure supports multiple DNS servers for redundancy. If custom DNS servers become unavailable, name resolution fails, potentially impacting Managed Instance operations that depend on DNS. Organizations should ensure DNS infrastructure is highly available and properly configured to resolve both Azure and on-premises names.
Option A is incorrect because DNS configuration is not applied at individual database level. Option B is wrong because while Managed Instance uses DNS, the configuration itself is at the VNet level rather than instance-specific settings. Option D is incorrect because DNS is configured at the VNet level which applies to all subnets rather than individual subnet configuration.
Question 143:
An administrator needs to monitor deadlocks occurring in Azure SQL Database and capture detailed information for troubleshooting. Which feature should be enabled?
A) Query Performance Insight
B) Extended Events
C) SQL Auditing
D) Azure Monitor metrics
Answer: B
Explanation:
Extended Events should be enabled to monitor deadlocks and capture detailed information for troubleshooting in Azure SQL Database. This lightweight performance monitoring framework captures comprehensive deadlock information including participating queries, lock resources, deadlock graph, and victim selection details.
Extended Events provides targeted monitoring capabilities through event sessions that capture specific events like deadlocks without the overhead of tracing all database activity. Administrators create event sessions specifying the deadlock_graph event, which captures complete deadlock information in XML format. The deadlock graph shows all sessions involved, the resources they were attempting to access, lock types held and requested, and which session was chosen as the deadlock victim.
Event sessions can stream data to ring buffers for real-time analysis or write to Azure Storage for persistent collection and historical analysis. The XML deadlock graphs can be visualized using SQL Server Management Studio, which renders them as graphical representations showing the circular lock dependency. This visualization helps administrators quickly understand deadlock causes and identify which queries or application patterns require modification.
Deadlock analysis using Extended Events enables root cause identification. Common patterns include queries accessing tables in different orders, long-running transactions holding locks excessively, missing indexes causing lock escalation, and application logic that should use optimistic concurrency instead of pessimistic locking. Extended Events data provides the evidence needed to implement appropriate solutions.
Option A is incorrect because Query Performance Insight focuses on query resource consumption rather than detailed deadlock analysis. Option C is wrong because SQL Auditing tracks security events and data access rather than performance issues like deadlocks. Option D is incorrect because Azure Monitor metrics provide aggregate statistics but not detailed deadlock diagnostics.
Question 144:
An organization needs to implement zone redundancy for Azure SQL Database to achieve higher availability. Which service tier supports zone-redundant configuration?
A) Basic tier
B) Standard tier
C) Premium tier
D) All DTU-based tiers
Answer: C
Explanation:
Premium tier supports zone-redundant configuration for Azure SQL Database to achieve higher availability. Zone redundancy distributes database replicas across multiple availability zones within an Azure region, protecting against datacenter-level failures while maintaining high performance and automatic failover capabilities.
Zone-redundant configuration works by creating database replicas in physically separate availability zones, each with independent power, cooling, and networking infrastructure. The primary replica handles all read-write operations while synchronous replication maintains secondary replicas in other zones. If the primary zone experiences failure, automatic failover promotes a secondary replica to primary, typically completing within seconds without data loss.
Premium tier zone redundancy provides superior availability compared to locally redundant configurations. While local redundancy protects against server or rack failures within a datacenter, zone redundancy protects against entire datacenter failures caused by power outages, network failures, or natural disasters affecting the availability zone. Microsoft’s SLA for zone-redundant databases provides higher uptime guarantees than non-zone-redundant configurations.
Enabling zone redundancy is straightforward, requiring only a configuration option during database creation or as a modification to existing Premium or Business Critical tier databases. The feature incurs additional cost due to the extra replicas maintained across zones. Applications benefit from zone redundancy transparently, as connection strings and access patterns remain unchanged. The automatic failover ensures minimal disruption during zone failures.
Option A is incorrect because Basic tier does not support zone redundancy and provides minimal availability features. Option B is wrong because Standard tier does not support zone-redundant configuration. Option D is incorrect because only Premium and Business Critical tiers support zone redundancy, not all DTU-based tiers.
Question 145:
A database administrator needs to implement read-only routing for application queries in Azure SQL Database. Which feature enables this capability?
A) Geo-replication
B) Read scale-out
C) Database copy
D) Transactional replication
Answer: B
Explanation:
Read scale-out enables read-only routing for application queries in Azure SQL Database by allowing applications to offload read-only workloads to secondary replicas. This feature leverages the built-in high availability replicas in Premium and Business Critical tiers, providing additional read capacity without deploying separate databases.
Read scale-out works through connection string configuration using the ApplicationIntent parameter. When applications specify ApplicationIntent=ReadOnly, Azure SQL Database automatically routes connections to one of the read-only secondary replicas. These replicas maintain synchronization with the primary replica through synchronous replication, ensuring read queries access current data with minimal replication lag.
The read-only replicas are the same replicas used for high availability, so read scale-out provides additional value from existing infrastructure. Applications can separate read-write operations that connect to the primary replica from read-only operations like reporting, analytics, or dashboard queries that connect to secondary replicas. This separation prevents read-intensive operations from competing with transactional workloads for resources.
Read scale-out is particularly valuable for applications with heavy read workloads. Business intelligence tools, reporting applications, and read-heavy web applications benefit from offloading queries to dedicated read replicas. The feature is included with Premium and Business Critical tiers without additional licensing costs, though it is not available in lower tiers. Applications must handle potential replication lag, typically milliseconds, when reading from secondaries.
Option A is incorrect because geo-replication provides disaster recovery across regions rather than read-only routing within the primary region. Option C is wrong because database copy creates independent databases rather than providing routing to existing replicas. Option D is incorrect because transactional replication is an on-premises feature not available in Azure SQL Database.
Question 146:
An administrator needs to configure temporal tables in Azure SQL Database to track historical changes to data. Which system table stores the historical row versions?
A)tables
B) History table specified during temporal table creation
C)temporal_history
D) Database backup files
Answer: B
Explanation:
The history table specified during temporal table creation stores historical row versions for temporal tables in Azure SQL Database. System-versioned temporal tables automatically maintain complete change history by storing previous row versions in a separate history table whenever data modifications occur.
Temporal tables consist of two tables: the current table that stores current row versions and handles normal DML operations, and the history table that automatically captures previous row versions when updates or deletes occur. When creating a temporal table, administrators specify the history table name, or Azure SQL Database generates one automatically with a default naming convention. The history table schema matches the current table, including all columns plus period columns tracking validity time ranges.
The temporal table mechanism works transparently to applications. When an UPDATE statement modifies a row, the database engine copies the previous version to the history table before applying changes to the current table. When a DELETE statement removes a row, the engine moves the row to the history table. System-versioned temporal tables maintain two DATETIME2 columns indicating when each row version was valid, enabling time-based queries.
Temporal queries use the FOR SYSTEM_TIME clause to retrieve data as it existed at specific points in time or across time ranges. For example, SELECT * FROM Employees FOR SYSTEM_TIME AS OF ‘2024-01-01’ returns all employee records as they existed on that date. This capability supports auditing requirements, data recovery, trend analysis, and slowly changing dimension scenarios in data warehousing.
Option A is incorrect because sys.tables is a system catalog view that stores table metadata rather than temporal history data. Option C is wrong because sys.temporal_history is not a standard system table in SQL Database. Option D is incorrect because backup files provide recovery capabilities but are not the active storage for temporal table history.
Question 147:
A database administrator needs to configure Azure SQL Database to send diagnostic logs to Log Analytics workspace. Which diagnostic setting should be configured?
A) Azure Monitor metrics only
B) Diagnostic logs with specific log categories
C) SQL Auditing only
D) Query Performance Insight
Answer: B
Explanation:
Diagnostic logs with specific log categories should be configured to send Azure SQL Database logs to Log Analytics workspace. Diagnostic settings enable streaming of resource logs and metrics to various destinations including Log Analytics, providing centralized monitoring, analysis, and alerting capabilities.
Azure SQL Database provides multiple diagnostic log categories that capture different aspects of database operations. Key categories include SQLInsights for intelligent performance insights, AutomaticTuning for automatic tuning actions and recommendations, QueryStoreRuntimeStatistics for query execution performance data, QueryStoreWaitStatistics for query wait information, Errors for database errors and exceptions, DatabaseWaitStatistics for wait statistics, Timeouts for query timeouts, Blocks for blocking information, and Deadlocks for deadlock events.
Configuration involves creating diagnostic settings through Azure portal, PowerShell, or Azure CLI, specifying which log categories to collect and the destination Log Analytics workspace. Administrators can enable all categories for comprehensive monitoring or select specific categories based on monitoring needs and cost considerations. Each log category generates different data volumes affecting storage costs and query performance.
Once configured, logs stream continuously to Log Analytics where they can be queried using Kusto Query Language (KQL). This enables sophisticated analysis including correlation across multiple databases, trend identification over time, anomaly detection, and integration with Azure Sentinel for security analytics. Log Analytics retention policies determine how long data is stored, balancing analytical needs with storage costs.
Option A is incorrect because metrics alone do not provide the detailed diagnostic information available in logs. Option C is wrong because SQL Auditing serves compliance and security requirements but diagnostic logs provide broader operational and performance insights. Option D is incorrect because Query Performance Insight uses Query Store data but diagnostic settings provide the mechanism to export logs to Log Analytics.
Question 148:
An organization needs to implement cross-database queries in Azure SQL Database to join data across multiple databases. Which feature enables this capability?
A) Linked servers
B) Elastic queries
C) Database synonyms
D) Distributed transactions
Answer: B
Explanation:
Elastic queries enable cross-database query capabilities in Azure SQL Database, allowing applications to execute T-SQL queries that span multiple databases. This feature provides a unified query interface for accessing data distributed across multiple Azure SQL databases without requiring data duplication or complex application-layer joins.
Elastic queries work by defining external data sources that point to remote Azure SQL databases and creating external tables that map to tables in those remote databases. Once configured, queries can reference external tables using standard T-SQL syntax, and the elastic query engine transparently executes portions of the query against remote databases, retrieves results, and combines them locally. The query syntax appears identical to single-database queries from the application perspective.
Common elastic query scenarios include vertical partitioning where different tables reside in different databases, horizontal partitioning (sharding) where table rows are distributed across multiple databases based on a partition key, and reference data sharing where common lookup tables are centralized in one database and accessed from many others. The feature supports both rowstore and columnstore external tables.
Elastic queries have considerations regarding performance and functionality. Cross-database operations involve network latency for data transfer between databases. Complex queries with multiple joins across databases may perform slower than single-database equivalents. Certain T-SQL features are not supported in elastic queries, and administrators should review limitations before implementation. Despite constraints, elastic queries provide valuable data integration capabilities without requiring custom middleware or data duplication.
Option A is incorrect because linked servers are not supported in Azure SQL Database, though they are available in SQL Managed Instance. Option C is wrong because synonyms provide aliasing within a single database rather than cross-database query capabilities. Option D is incorrect because distributed transactions refer to maintaining transactional consistency across databases rather than querying across them.
Question 149:
A database administrator needs to configure maintenance windows for Azure SQL Database to control when updates and patches are applied. Which maintenance window option provides the most flexibility?
A) Default maintenance window
B) Custom maintenance window with specific days and times
C) Weekend-only maintenance window
D) Maintenance cannot be scheduled in Azure SQL Database
Answer: B
Explanation:
Custom maintenance window with specific days and times provides the most flexibility for controlling when updates and patches are applied to Azure SQL Database. This feature allows organizations to align database maintenance with business requirements, scheduling updates during low-usage periods to minimize impact on applications and users.
Custom maintenance windows enable administrators to specify preferred time slots when Azure performs maintenance activities including software updates, security patches, and infrastructure improvements. Organizations can choose from predefined maintenance windows or define custom windows specifying day of week and time ranges. For example, a maintenance window might be configured for Saturday 2:00 AM to 6:00 AM local time, ensuring updates occur during minimal business activity.
Azure respects configured maintenance windows for planned maintenance operations. When updates are ready for deployment, Azure schedules them within the specified window rather than applying them immediately. This predictability allows organizations to coordinate with application teams, inform users of potential brief disruptions, and ensure support staff are available during maintenance periods. Emergency security patches may occasionally require updates outside maintenance windows.
Maintenance windows apply to both Azure SQL Database and SQL Managed Instance. Different databases can have different maintenance window configurations, allowing fine-grained control across database estates. During maintenance, databases may experience brief connection resets similar to failover events, typically lasting seconds. Applications with connection retry logic handle these transitions transparently.
Option A is incorrect because the default maintenance window provides no control over timing and allows Azure to schedule maintenance at any time. Option C is wrong because while weekend maintenance is common, restricting to weekends only provides less flexibility than custom windows with specific time ranges. Option D is incorrect because maintenance window configuration is supported in Azure SQL Database.
Question 150:
An administrator needs to implement resource governance in Azure SQL Database to prevent individual queries from consuming excessive resources. Which feature should be configured?
A) Query Store
B) Resource Governor
C) Query timeout settings
D) DTU or vCore limits
Answer: C
Explanation:
Query timeout settings should be configured to prevent individual queries from consuming excessive resources in Azure SQL Database. These settings limit query execution duration, ensuring that long-running or inefficient queries automatically terminate before exhausting database resources or impacting other workloads.
Query timeout implementation occurs at multiple levels. Connection-level timeouts are configured in application connection strings using parameters like Connection Timeout for connection establishment and Command Timeout for query execution. Azure SQL Database also provides server-level query timeout enforcement through automatic query termination when queries exceed reasonable execution duration limits based on service tier and resource allocation.
Setting appropriate query timeouts prevents several problem scenarios. Poorly written queries with missing indexes or inefficient joins can execute for hours, consuming CPU and memory that should serve other operations. Application bugs causing infinite loops or repeated query execution can be contained through timeouts. Resource exhaustion attacks where malicious users intentionally submit expensive queries are mitigated through timeout enforcement.
Best practices recommend setting timeouts based on expected query performance characteristics. OLTP applications typically use short timeouts measured in seconds, while analytical workloads may require longer timeouts measured in minutes. Applications should implement error handling for timeout exceptions, logging timeout occurrences for later investigation. Frequent timeouts indicate underlying performance problems requiring query optimization or resource scaling.
Option A is incorrect because Query Store captures query performance data for analysis rather than governing resource consumption. Option B is wrong because Resource Governor, available in SQL Server on-premises and SQL Managed Instance, is not available in Azure SQL Database. Option D is incorrect because DTU or vCore limits constrain overall database resources but do not prevent individual query resource consumption.
Question 151:
A database administrator needs to configure Azure SQL Managed Instance to support Service Broker for asynchronous message processing. Which deployment option supports this feature?
A) Azure SQL Database
B) Azure SQL Managed Instance
C) Azure SQL Database Hyperscale
D) Azure Synapse Analytics
Answer: B
Explanation:
Azure SQL Managed Instance supports Service Broker for asynchronous message processing. Service Broker is an enterprise messaging feature providing reliable, asynchronous message queuing natively within SQL Server, and it is available in Managed Instance due to its high compatibility with SQL Server on-premises features.
Service Broker enables building distributed, asynchronous applications within the database engine. It provides message queuing with guaranteed delivery, transactional message processing, message routing, and priority-based processing. Applications send messages to queues, and Service Broker reliably delivers them to receiving services, even across database restarts or failures. This capability supports decoupled architectures where components communicate asynchronously.
Common Service Broker scenarios include batch processing workflows where tasks are queued and processed by background workers, data loading operations where ETL jobs communicate through messages, audit trail processing where audit events are queued for asynchronous processing, and integration with external systems through message-based interfaces. Service Broker maintains message ordering and transactional consistency.
Configuring Service Broker in Managed Instance involves creating message types defining message structure, contracts specifying which message types are allowed in conversations, queues storing messages, and services representing endpoints in message conversations. Applications use BEGIN DIALOG and SEND statements to create conversations and send messages, while receiving applications use RECEIVE statements to retrieve and process messages from queues.
Option A is incorrect because Azure SQL Database does not support Service Broker, as it focuses on simpler cloud-native features. Option C is wrong because Hyperscale is a deployment tier of Azure SQL Database and inherits the same feature limitations. Option D is incorrect because Azure Synapse Analytics is designed for analytics workloads and does not support Service Broker.
Question 152:
An organization needs to implement data classification in Azure SQL Database for compliance with data protection regulations. Which tool provides data discovery and classification capabilities?
A) Azure Purview
B) SQL Data Discovery and Classification
C) Azure Information Protection
D) Microsoft Defender for Cloud
Answer: B
Explanation:
SQL Data Discovery and Classification provides data discovery and classification capabilities built into Azure SQL Database for compliance with data protection regulations. This feature automatically discovers, classifies, labels, and reports on sensitive data within databases, supporting regulatory compliance requirements like GDPR, HIPAA, and PCI DSS.
SQL Data Discovery and Classification uses intelligent scanning to identify columns containing sensitive information. The classification engine recognizes patterns indicating personally identifiable information (PII), financial data, health records, credentials, and other sensitive data types. Once identified, administrators can review recommendations and apply classification labels indicating information type (like Social Security Number or Credit Card Number) and sensitivity level (like Highly Confidential or Public).
Classification metadata is stored in database extended properties and can be accessed programmatically or through Azure portal interfaces. The metadata integrates with other security features including dynamic data masking to automatically mask classified columns, Azure SQL Auditing to track access to sensitive data, and Microsoft Defender for SQL to provide advanced threat protection focused on sensitive data. This integration creates comprehensive data protection based on classification.
Classification reports provide visibility into sensitive data distribution across databases, supporting data governance initiatives and regulatory compliance documentation. Organizations can demonstrate where sensitive data resides, how it is protected, and who has access to it. Regular classification scans detect newly added sensitive data, ensuring classification remains current as schemas evolve.
Option A is incorrect because Azure Purview provides enterprise-wide data governance and catalog capabilities but SQL Data Discovery and Classification is the specific feature for database column classification. Option C is wrong because Azure Information Protection focuses on document and email protection rather than database column classification. Option D is incorrect because Microsoft Defender for Cloud provides security posture management but not the specific data classification capability.
Question 153:
A database administrator needs to configure performance baselines for Azure SQL Database to detect anomalies and performance degradation. Which feature automatically establishes baselines and detects deviations?
A) Query Performance Insight
B) Intelligent Insights
C) Azure Advisor
D) Manual baseline documentation
Answer: B
Explanation:
Intelligent Insights automatically establishes performance baselines and detects deviations in Azure SQL Database. This AI-powered feature uses built-in intelligence to continuously monitor database performance, learn normal patterns, detect anomalies, and provide diagnostics explaining performance issues without requiring manual configuration.
Intelligent Insights works by analyzing telemetry from Query Store, dynamic management views, and database metrics. Machine learning algorithms establish baselines for key performance indicators including query duration, wait times, error rates, and resource utilization. The system recognizes normal daily and weekly patterns accounting for business cycles and expected workload variations.
When performance deviates from established baselines, Intelligent Insights generates diagnostic reports explaining the root cause. Diagnoses include specific issues like increased query duration due to parameter sniffing, performance regression from plan changes, excessive resource wait times, tempdb contention, locking problems, or missing indexes. Each diagnosis includes affected queries, root cause analysis, and recommended remediation actions.
The diagnostic information is available through the Intelligent Insights diagnostic log, which can be streamed to Log Analytics, Event Hub, or Azure Storage. Organizations integrate these diagnostics with monitoring dashboards, automated alerting systems, or incident management workflows. The proactive detection enables faster resolution of performance issues before users report problems, improving application reliability and user experience.
Option A is incorrect because Query Performance Insight provides query performance visibility but does not automatically establish baselines or detect anomalies. Option C is wrong because Azure Advisor provides optimization recommendations but not continuous anomaly detection with root cause analysis. Option D is incorrect because manual baseline documentation requires ongoing maintenance and does not provide automated detection.
Question 154:
An administrator needs to configure Azure SQL Database to automatically pause during periods of inactivity to reduce costs. Which compute tier supports automatic pause and resume?
A) Provisioned compute tier
B) Serverless compute tier
C) DTU-based compute tier
D) All compute tiers support automatic pause
Answer: B
Explanation:
Serverless compute tier supports automatic pause and resume functionality in Azure SQL Database, enabling cost optimization for databases with intermittent usage patterns. This compute model automatically pauses databases during inactive periods, eliminating compute charges while retaining data storage, then automatically resumes when activity returns.
Serverless compute operates by monitoring database activity and tracking the idle time since the last connection or query. Administrators configure an auto-pause delay, typically ranging from one hour to seven days. When the database remains inactive for the configured duration, it automatically enters a paused state. During pause, organizations pay only for data storage, eliminating all compute costs associated with vCore allocation.
Resume occurs automatically when new connections arrive or queries are submitted. The resume process typically completes within seconds to a minute depending on database size. The first connection request after pause experiences delay while the database resumes, but subsequent connections proceed normally. Applications should implement retry logic to handle the temporary unavailability during resume operations.
Automatic pause and resume make serverless compute ideal for development and test environments used during business hours, infrequently accessed databases, new applications with unpredictable usage, and databases supporting scheduled workloads. The cost savings can be substantial when databases are unused for significant portions of the day or week, with organizations paying compute costs only during active periods.
Option A is incorrect because provisioned compute tier maintains allocated resources continuously without automatic pause capabilities. Option C is wrong because DTU-based compute models do not support automatic pause and resume. Option D is incorrect because only serverless compute tier provides automatic pause functionality.
Question 155:
A database administrator needs to implement Advanced Threat Protection for Azure SQL Database. Which security feature detects potential SQL injection attempts?
A) Firewall rules
B) Microsoft Defender for SQL
C) Always Encrypted
D) Dynamic Data Masking
Answer: B
Explanation:
Microsoft Defender for SQL detects potential SQL injection attempts as part of its comprehensive Advanced Threat Protection capabilities for Azure SQL Database. This security service uses advanced machine learning and behavioral analytics to identify suspicious activities and potential threats including SQL injection attacks, brute force attacks, and unusual data access patterns.
Microsoft Defender for SQL monitors database activities continuously, analyzing query patterns, authentication attempts, data access behaviors, and other telemetry. The machine learning models establish baselines for normal database activity and detect anomalies indicating potential security threats. When suspicious activity is detected, Defender generates security alerts with detailed information about the threat, affected resources, and recommended mitigation actions.
SQL injection detection specifically identifies attempts to inject malicious SQL code through application inputs. Defender recognizes patterns characteristic of injection attacks including unusual query structures, attempts to access system tables or views, privilege escalation attempts, and data exfiltration patterns. Alerts provide query text samples, source IP addresses, authentication details, and severity assessments helping security teams respond appropriately.
Additional threat protection capabilities include detection of unusual login locations indicating compromised credentials, brute force attacks attempting multiple failed authentication, potential data exfiltration through unusual data access patterns, and suspicious application behavior indicating compromised applications. Integration with Microsoft Sentinel enables centralized security monitoring across cloud and on-premises resources.
Option A is incorrect because firewall rules control network access but do not detect application-layer attacks like SQL injection. Option C is wrong because Always Encrypted protects data confidentiality but does not detect attacks. Option D is incorrect because Dynamic Data Masking obscures data in results but does not provide threat detection capabilities.
Question 156:
An organization needs to implement database-level encryption keys managed independently from Microsoft for Azure SQL Database. Which TDE configuration provides this capability?
A) Service-managed TDE
B) Customer-managed TDE using Azure Key Vault
C) Always Encrypted
D) Column-level encryption
Answer: B
Explanation:
Customer-managed TDE (Transparent Data Encryption) using Azure Key Vault provides the capability to implement database-level encryption with keys managed independently from Microsoft. This configuration gives organizations full control over encryption key lifecycle, access policies, and key rotation while maintaining the performance benefits of transparent data encryption.
Customer-managed TDE works by storing the TDE protector (asymmetric key) in Azure Key Vault rather than using Microsoft-managed keys. The TDE protector encrypts the database encryption key (DEK) that actually encrypts database pages. Azure SQL Database accesses the Key Vault using managed identity authentication to unwrap the DEK during database operations. Organizations control Key Vault access policies, determining which identities can use encryption keys.
This configuration provides several security and compliance benefits. Organizations maintain exclusive control over encryption keys, supporting regulatory requirements for customer-managed encryption. Key access can be revoked immediately, rendering databases inaccessible until access is restored. Audit logs in Key Vault track all key access operations, providing comprehensive visibility into encryption key usage. Key rotation can be performed independently of database operations.
Implementation requires creating an Azure Key Vault instance, generating or importing an RSA key, granting the SQL server managed identity access to the key vault, and configuring the database to use the customer-managed key as TDE protector. Organizations must ensure high availability of Key Vault because database operations require key access. Most organizations deploy Key Vault with redundancy across availability zones or regions.
Option A is incorrect because service-managed TDE uses Microsoft-managed keys rather than customer-controlled keys. Option C is wrong because Always Encrypted provides column-level encryption with client-side key management but is a different technology from TDE. Option D is incorrect because column-level encryption targets specific columns rather than providing transparent encryption of entire databases.
Question 157:
A database administrator needs to implement cross-region read replicas for Azure SQL Database to serve users in multiple geographic locations. Which feature should be configured?
A) Auto-failover groups
B) Active geo-replication
C) Database copy
D) Availability zones
Answer: B
Explanation:
Active geo-replication should be configured to implement cross-region read replicas for Azure SQL Database serving users in multiple geographic locations. This feature creates readable secondary replicas in different Azure regions, providing low-latency read access for geographically distributed users while also serving as disaster recovery resources.
Active geo-replication works by continuously replicating data from a primary database to up to four secondary databases in the same or different regions. Replication occurs asynchronously, allowing the primary to continue processing transactions without waiting for secondary acknowledgment. Secondary databases are readable, enabling applications to offload read-only workloads to nearby replicas, reducing latency for users in different geographic areas.
Each secondary replica maintains its own compute and storage resources independent of the primary database. Organizations can configure different service tiers for secondaries based on expected read workload. For example, a primary database in the US might serve write operations and US read traffic, while secondary replicas in Europe and Asia serve read-only operations for users in those regions.
Active geo-replication supports both disaster recovery and read scale-out scenarios. If the primary region becomes unavailable, one secondary can be promoted to primary, typically with minimal data loss due to asynchronous replication. For read scale-out, applications direct read-only queries to the nearest secondary replica using connection strings with ApplicationIntent=ReadOnly. This geography-aware architecture optimizes both performance and availability.
Option A is incorrect because while auto-failover groups use geo-replication, they focus on disaster recovery orchestration rather than read scale-out across regions. Option C is wrong because database copy creates independent databases without continuous synchronization. Option D is incorrect because availability zones provide redundancy within a region rather than cross-region replication.
Question 158:
An administrator needs to troubleshoot parameter sniffing issues affecting query performance in Azure SQL Database. Which Query Store feature provides visibility into parameter values used by cached plans?
A) Regressed queries report
B) Top resource consuming queries
C) Tracked queries with plan forcing
D) Query wait statistics
Answer: C
Explanation:
Tracked queries with plan forcing provides visibility into parameter values and enables resolution of parameter sniffing issues in Azure SQL Database Query Store. This feature allows administrators to identify queries affected by parameter sniffing, examine different execution plans generated for different parameter values, and force use of optimal plans to ensure consistent performance.
Parameter sniffing occurs when SQL Server compiles a query plan based on initial parameter values, and that plan performs poorly for subsequent executions with different parameters. Query Store captures multiple execution plans for the same query when parameter values cause different plan selections. Administrators can examine the Plan Summary view showing all plans for a query, their execution statistics, and the parameter values that influenced plan selection.
The tracked queries feature enables explicit monitoring of problematic queries. Administrators add queries to tracking, and Query Store provides detailed statistics about their execution including parameter values, chosen plans, execution counts, and resource consumption. When parameter sniffing causes performance problems, administrators can force a specific plan that performs well across parameter value ranges, overriding the optimizer’s plan selection.
Plan forcing instructs Query Store to use a specific execution plan regardless of parameter values or statistics changes. This resolves parameter sniffing by ensuring consistent plan usage. Forced plans remain in effect until administrators remove forcing or until the schema changes invalidate the plan. Query Store tracks forced plan performance, alerting administrators if forced plans regress.
Option A is incorrect because the regressed queries report identifies queries whose performance degraded but does not specifically address parameter sniffing or provide parameter visibility. Option B is wrong because top resource consumers identify expensive queries but not parameter-specific issues. Option D is incorrect because wait statistics show where queries spend time waiting but do not reveal parameter sniffing problems.
Question 159:
A database administrator needs to configure Azure SQL Database to replicate specific tables to Azure Synapse Analytics for analytical workloads. Which integration method provides this capability?
A) Elastic queries
B) Azure Data Factory with copy activity
C) Transactional replication
D) Database snapshots
Answer: B
Explanation:
Azure Data Factory with copy activity provides the capability to replicate specific tables from Azure SQL Database to Azure Synapse Analytics for analytical workloads. Data Factory is a cloud-based data integration service that orchestrates data movement and transformation between various data stores including relational databases, data warehouses, and data lakes.
Azure Data Factory copy activity transfers data from source to destination with support for incremental loading, scheduled execution, and transformation during transfer. Administrators create pipelines defining copy activities that extract data from Azure SQL Database tables and load them into Azure Synapse Analytics. The copy activity supports various loading patterns including full refresh where entire tables are replaced, incremental load based on watermark columns tracking change timestamps, and change data capture-based loading.
Implementation involves creating linked services that define connections to source Azure SQL Database and destination Azure Synapse Analytics, creating datasets that specify which tables to copy, and building pipelines with copy activities that orchestrate the data movement. Pipelines can include data transformation using mapping data flows, enabling data cleansing, aggregation, or format conversion during transfer.
Data Factory provides several advantages for analytical data replication including scheduled triggers that execute pipelines on recurring schedules, monitoring and alerting for pipeline execution status, performance optimization through parallel copying and staged copying via Azure Blob Storage, and integration with Azure DevOps for CI/CD deployment of data integration pipelines. This makes it ideal for building enterprise data warehousing solutions.
Option A is incorrect because elastic queries enable querying across databases but do not replicate data to separate analytics platforms. Option C is wrong because transactional replication is not supported between Azure SQL Database and Azure Synapse Analytics. Option D is incorrect because database snapshots create point-in-time copies within the same platform rather than replicating to analytical systems.
Question 160:
An organization needs to implement database ledger functionality in Azure SQL Database to provide tamper-evident audit trails for regulatory compliance. Which ledger table type allows updates and deletes while maintaining cryptographic verification?
A) Append-only ledger tables
B) Updatable ledger tables
C) Temporal tables
D) Memory-optimized tables
Answer: B
Explanation:
Updatable ledger tables allow updates and deletes while maintaining cryptographic verification in Azure SQL Database ledger functionality. This table type provides tamper-evident capabilities for databases requiring regulatory compliance and audit trail integrity while supporting normal DML operations including INSERT, UPDATE, and DELETE statements.
Updatable ledger tables work by maintaining a history table that automatically captures all row versions before modifications occur. Each transaction modifying ledger tables receives a sequence number and is included in cryptographic hash calculations that chain transactions together. This blockchain-inspired approach ensures that any tampering with historical data is detectable through hash verification because altering past records breaks the cryptographic chain.
The ledger system maintains database digests at regular intervals, which are cryptographic representations of the database state. Organizations can store these digests in immutable storage like Azure Blob Storage with immutability policies or in Azure Confidential Ledger. Later verification compares current database hashes against stored digests, detecting any unauthorized modifications to ledger tables or their history.
Updatable ledger tables support scenarios requiring full DML capabilities with tamper-evident audit trails including financial transaction systems where corrections and adjustments must be tracked, healthcare records requiring complete audit history with modification capabilities, and supply chain tracking where updates to shipment status must be verifiable. The ledger functionality balances operational flexibility with compliance requirements.
Option A is incorrect because append-only ledger tables only support INSERT operations, prohibiting updates and deletes. Option C is wrong because temporal tables track change history but do not provide cryptographic tamper-evidence. Option D is incorrect because memory-optimized tables focus on performance rather than providing ledger capabilities.
