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Question 61:
An Azure SQL Database is experiencing slow query performance. Which dynamic management view should be queried to identify the most resource-intensive queries?
A)dm_exec_requests
B)dm_exec_query_stats
C)dm_db_index_usage_stats
D)dm_os_wait_stats
Answer: B
Explanation:
The sys.dm_exec_query_stats dynamic management view (DMV) provides aggregated performance statistics for cached query plans, making it the optimal choice for identifying resource-intensive queries in Azure SQL Database. This DMV captures comprehensive execution metrics including total CPU time, logical reads, physical reads, writes, and execution counts for each query plan in the cache, enabling database administrators to quickly identify queries consuming the most resources and requiring optimization attention.
The view aggregates statistics since the query plan was added to the cache, providing both total resource consumption and average resource usage per execution. This dual perspective helps identify both frequently executed queries with moderate resource usage and infrequently executed queries with high individual resource consumption. Administrators can query this DMV to find queries with the highest total CPU time, most logical reads indicating memory pressure, or greatest number of physical reads suggesting missing indexes or inefficient query plans.
Common queries against sys.dm_exec_query_stats join with sys.dm_exec_sql_text to retrieve the actual query text and sys.dm_exec_query_plan to obtain execution plans for analysis. The DMV enables sorting by various metrics to identify top resource consumers from different perspectives, such as queries with highest average CPU per execution, queries performing the most logical reads, or queries with the longest total duration. This information guides performance tuning efforts by focusing on queries with the greatest optimization potential.
Option A is incorrect because sys.dm_exec_requests shows currently executing requests rather than historical performance statistics needed to identify patterns over time. Option C is wrong as sys.dm_db_index_usage_stats tracks index usage patterns but does not directly show query resource consumption. Option D is not correct because sys.dm_os_wait_stats shows wait statistics at the instance level but does not attribute waits to specific queries.
Regularly monitoring sys.dm_exec_query_stats helps proactively identify performance degradation before it impacts users significantly, establish baselines for normal query performance, and measure improvements after optimization efforts to verify that changes achieved desired performance gains.
Question 62:
Which Azure SQL Database feature automatically creates and manages indexes to improve query performance?
A) Query Store
B) Automatic tuning
C) Temporal tables
D) Always Encrypted
Answer: B
Explanation:
Automatic tuning in Azure SQL Database is an intelligent performance feature that continuously monitors database workloads and automatically creates, maintains, or drops indexes based on query patterns and performance impact analysis. This capability uses built-in intelligence from Azure SQL Database to identify opportunities for performance improvement through index optimization without requiring manual intervention from database administrators, making databases self-managing and continuously optimized.
The automatic tuning feature analyzes workload patterns using Query Store data to identify queries that would benefit from additional indexes. When a potential improvement is identified, the system creates the recommended index and monitors query performance to verify the index provides the expected benefits. If performance improves, the index is retained; if no improvement is observed or performance degrades, the index is automatically dropped. This verify-and-revert approach ensures that only beneficial changes persist in the database.
Automatic tuning also manages index maintenance by identifying and dropping unused or duplicate indexes that consume storage and impact write performance without providing query benefits. The feature can be configured with different modes including off (recommendations provided but not implemented), custom (administrators choose which recommendations to auto-implement), and inherit (settings inherited from server level). Administrators retain full control and can review all automatic actions through the Azure portal or system views.
Option A is incorrect because Query Store captures query performance data used by automatic tuning but does not itself create indexes. Option C is wrong as temporal tables provide system-versioned historical data tracking, not automatic index management. Option D is not correct because Always Encrypted protects sensitive data through encryption but does not manage indexes.
Enabling automatic tuning is recommended for most Azure SQL Database deployments as it continuously optimizes performance based on actual workload patterns, reduces administrative overhead for index management, and helps maintain consistent performance as workloads evolve over time without manual intervention.
Question 63:
What is the primary purpose of implementing resource governance in Azure SQL Database?
A) To encrypt data at rest
B) To control resource consumption and prevent resource exhaustion
C) To replicate data across regions
D) To compress database backups
Answer: B
Explanation:
Resource governance in Azure SQL Database controls and limits resource consumption including CPU, memory, log IO, data IO, and worker threads to prevent individual queries or workloads from exhausting resources and impacting other database operations. This capability is essential for maintaining predictable performance in multi-tenant environments, ensuring fair resource allocation among concurrent workloads, and preventing runaway queries from monopolizing database resources that should be shared across applications.
Azure SQL Database implements resource governance through service tiers and compute sizes that define maximum resource limits for each database. Within these boundaries, the database engine applies resource governor policies that manage how resources are allocated among concurrent requests. When resource limits are approached, the database throttles or queues additional requests to prevent resource exhaustion, ensuring that the database remains responsive even under heavy load.
Resource governance becomes particularly important in elastic pool scenarios where multiple databases share a common resource pool. The governance ensures that no single database in the pool can consume all available resources, protecting other databases from performance degradation caused by neighbors. Administrators can monitor resource governance through DMVs that show when throttling occurs, helping identify whether databases require larger service tiers or whether workload optimization could reduce resource consumption.
Option A is incorrect because encryption is a security feature separate from resource governance. Option C is wrong as geo-replication handles data replication across regions, not resource management. Option D is not correct because backup compression optimizes storage but is unrelated to runtime resource governance.
Understanding resource governance limits for each service tier helps capacity planning, guides selection of appropriate service tiers for workload requirements, and informs optimization efforts when applications approach resource limits rather than automatically assuming larger service tiers are needed.
Question 64:
Which authentication method provides the highest security for Azure SQL Database connections?
A) SQL authentication
B) Azure Active Directory authentication with multi-factor authentication
C) Windows authentication
D) Anonymous authentication
Answer: B
Explanation:
Azure Active Directory authentication with multi-factor authentication provides the highest security level for Azure SQL Database connections by combining centralized identity management, strong authentication mechanisms, and additional verification factors beyond passwords. This authentication approach addresses multiple security concerns including password vulnerabilities, credential theft, and unauthorized access attempts by requiring users to provide multiple forms of verification before accessing sensitive database resources.
AAD authentication integrates Azure SQL Database with the organization’s centralized identity platform, enabling consistent security policies, centralized user management, and unified auditing across all Azure resources. Users authenticate with their organizational credentials managed in Azure Active Directory rather than separate SQL logins that require independent password management. This integration supports modern authentication features including conditional access policies, identity protection, and privileged identity management.
Multi-factor authentication adds a second verification factor beyond passwords, requiring users to confirm their identity through additional methods like mobile app notifications, SMS codes, or hardware tokens. This dramatically reduces the risk of account compromise from stolen or guessed passwords because attackers need both the password and access to the second factor. MFA can be enforced through Azure AD conditional access policies that require additional authentication when accessing from untrusted locations or devices.
Option A is incorrect because SQL authentication relies solely on passwords without additional verification factors or centralized identity management. Option C is wrong as Windows authentication is not available for Azure SQL Database which operates in a cloud environment. Option D is not correct because anonymous authentication would provide no security and is not supported for Azure SQL Database.
Implementing AAD authentication with MFA requires configuring Azure AD integration for SQL Database servers, enrolling users in MFA through Azure AD, creating contained database users mapped to AAD identities, and potentially adjusting application connection strings to support AAD authentication modes.
Question 65:
An Azure SQL Database requires data to be retained for seven years for compliance purposes. Which backup retention option should be configured?
A) Point-in-time restore
B) Long-term retention (LTR)
C) Geo-redundant backup
D) Manual backup to blob storage
Answer: B
Explanation:
Long-term retention (LTR) is the Azure SQL Database backup feature specifically designed for compliance and regulatory requirements that mandate data retention beyond the standard point-in-time restore window, supporting retention periods of up to ten years. This capability enables organizations to meet legal, regulatory, and business requirements for long-term data retention while leveraging Azure’s durable storage infrastructure without managing backup processes manually.
LTR works by automatically capturing full database backups according to configured policies and storing them in Azure Blob storage with read-access geo-redundant storage for durability. Administrators configure retention policies specifying how long to retain weekly, monthly, and yearly backups, with Azure automatically managing the backup lifecycle including creation, retention, and eventual deletion when retention periods expire. For example, a policy might retain weekly backups for twelve weeks, monthly backups for twelve months, and yearly backups for seven years.
The backups stored through LTR are independent of the short-term point-in-time restore capability and do not count against the standard backup retention limits. Organizations can restore databases from LTR backups at any time within the retention period, creating new databases from historical backups for compliance audits, legal discovery, or data recovery from logical corruption discovered long after occurrence. LTR provides cost-effective compliance as backups are stored in cool storage tiers optimized for long-term retention.
Option A is incorrect because point-in-time restore typically provides retention of seven to thirty-five days depending on service tier, insufficient for seven-year requirements. Option C is wrong as geo-redundant backup provides geographic redundancy but does not extend retention periods. Option D is not correct because while manual backups are possible, LTR provides automated managed solution specifically designed for long-term retention requirements.
Implementing LTR requires configuring retention policies at the database or server level through Azure portal, PowerShell, or CLI, monitoring storage consumption as retained backups accumulate, and testing restore procedures to verify backups can be successfully restored when needed.
Question 66:
Which feature in Azure SQL Database automatically detects and alerts on potential security vulnerabilities?
A) Query Performance Insight
B) Advanced Data Security vulnerability assessment
C) Elastic database jobs
D) Temporal tables
Answer: B
Explanation:
Advanced Data Security vulnerability assessment is a built-in service that automatically scans Azure SQL databases for potential security vulnerabilities, misconfigurations, and deviations from security best practices, providing actionable recommendations for improving database security posture. This capability helps organizations proactively identify and remediate security issues before they can be exploited by attackers, supporting compliance requirements and reducing the risk of data breaches.
Vulnerability assessment performs comprehensive security scans that check for various issues including missing encryption, excessive permissions, exposed sensitive data, weak authentication configurations, missing auditing, insecure network access settings, and many other security concerns based on industry best practices and compliance frameworks. Scans can be scheduled to run automatically at regular intervals or triggered manually, with results presented through an intuitive dashboard showing security score and detailed findings.
Each identified vulnerability includes a description of the security issue, the potential risk it represents, specific affected database objects, and step-by-step remediation guidance for resolving the issue. The assessment tracks security posture over time, showing improvements as vulnerabilities are remediated and alerting when new issues are introduced. Results can be exported for compliance reporting and integrated with Azure Security Center for centralized security monitoring across all Azure resources.
Option A is incorrect because Query Performance Insight focuses on query performance monitoring, not security vulnerability detection. Option C is wrong as elastic database jobs execute administrative tasks across multiple databases but do not perform security assessments. Option D is not correct because temporal tables provide historical data tracking for auditing and recovery, not security vulnerability scanning.
Implementing vulnerability assessment as part of Advanced Data Security requires enabling the service for SQL servers or databases, configuring scan schedules and notification settings, reviewing assessment results regularly, prioritizing remediation based on risk levels, and establishing processes for addressing identified vulnerabilities.
Question 67:
What is the recommended approach for migrating an on-premises SQL Server database to Azure SQL Database with minimal downtime?
A) Backup and restore
B) Export BACPAC and import
C) Azure Database Migration Service with online migration
D) SQL Server replication
Answer: C
Explanation:
Azure Database Migration Service with online migration capability provides the recommended approach for migrating on-premises SQL Server databases to Azure SQL Database while minimizing downtime through continuous data synchronization. This managed service handles the complexities of database migration including schema conversion, data migration, synchronization of ongoing changes, and cutover coordination, enabling near-zero downtime migrations that minimize business impact.
The online migration process begins by creating an initial database snapshot that is migrated to Azure SQL Database, then continuously synchronizes changes from the source database using transaction log capture. This continuous synchronization ensures that the Azure database remains current with all changes occurring on the source database during the migration process. Applications continue operating against the source database without interruption while the migration progresses.
When the initial synchronization completes and both databases are in sync, administrators can schedule a cutover window for switching applications to the Azure database. The cutover requires only a brief maintenance window to allow final synchronization to complete, update application connection strings, and verify connectivity. The continuous synchronization approach minimizes downtime compared to offline migration methods that require extended outages for complete data transfer.
Option A is incorrect because backup and restore requires a complete outage during the backup, transfer, and restore process which can be lengthy for large databases. Option B is wrong as BACPAC export/import also requires extended downtime proportional to database size. Option D is not correct because while SQL Server transactional replication can enable minimal downtime migrations, it requires more manual configuration compared to the managed Azure Database Migration Service.
Successful online migration requires assessing database compatibility using Data Migration Assistant to identify any incompatibilities, planning the migration schedule including initial sync and cutover windows, testing the migration with non-production databases, and preparing rollback procedures in case issues arise during cutover.
Question 68:
Which Azure SQL Database feature provides query execution history and performance metrics for troubleshooting?
A) Dynamic Management Views
B) Query Store
C) SQL Profiler
D) Extended Events
Answer: B
Explanation:
Query Store is a built-in feature in Azure SQL Database that automatically captures and retains comprehensive query execution history including query text, execution plans, runtime statistics, and performance metrics over time. This capability provides database administrators with persistent historical data for troubleshooting performance issues, identifying query regressions, and understanding workload patterns without requiring trace tools or manual data collection.
Query Store operates by intercepting query executions and recording relevant information including the normalized query text, execution plans chosen by the query optimizer, execution statistics like duration, CPU time, logical reads, and physical reads, and wait statistics showing what resources queries waited for during execution. This data is aggregated over configurable time intervals and retained according to configured retention policies, creating a historical performance repository.
The feature enables several key troubleshooting scenarios including identifying performance regressions where query performance degrades after plan changes, forcing specific query plans to resolve regressions, analyzing top resource-consuming queries across historical periods, comparing query performance between time windows, and understanding the impact of index or statistics changes on query performance. The Azure portal provides intuitive visualizations of Query Store data for quick analysis.
Option A is incorrect because while DMVs provide real-time performance data, they show only current state and recent cached information without long-term historical persistence. Option C is wrong as SQL Profiler is not available for Azure SQL Database and requires capturing traces during specific time windows rather than continuous collection. Option D is not correct because Extended Events require explicit session configuration and management whereas Query Store operates automatically.
Query Store should be enabled for all production Azure SQL databases as it provides invaluable troubleshooting data with minimal overhead, enables automatic tuning features, and helps maintain consistent performance by detecting and resolving query plan regressions quickly.
Question 69:
What is the primary benefit of using elastic pools in Azure SQL Database?
A) Improved query performance
B) Cost optimization by sharing resources among multiple databases
C) Enhanced security through isolation
D) Automatic schema synchronization
Answer: B
Explanation:
Elastic pools in Azure SQL Database enable cost optimization by allowing multiple databases to share a common pool of resources including CPU, memory, and storage, with each database consuming resources as needed while staying within pool limits. This resource-sharing model is ideal for environments with multiple databases that have varying and complementary usage patterns, enabling overall resource costs to be lower than provisioning individual databases with sufficient capacity to handle their peak demands.
The cost advantage of elastic pools stems from the typical reality that databases rarely reach their peak resource consumption simultaneously. While individual databases may occasionally spike in resource usage, most databases remain idle or underutilized most of the time. By pooling resources, the collective resource allocation can be much smaller than the sum of individual database allocations while still providing adequate capacity because resource peaks are distributed across time and databases.
Elastic pools are particularly beneficial for SaaS applications where each customer database has unpredictable usage patterns, development and test environments with numerous databases that are not continuously active, and consolidation scenarios where many small databases from different applications share common infrastructure. Azure automatically handles resource distribution among pool members, ensuring fair allocation while preventing any single database from monopolizing pool resources.
Option A is incorrect because elastic pools provide cost optimization rather than inherent performance improvements, though adequate pool sizing maintains good performance. Option C is wrong as databases in pools share resources rather than being isolated, though they remain logically separate. Option D is not correct because elastic pools share resources but do not provide schema synchronization capabilities.
Effective elastic pool implementation requires analyzing database resource consumption patterns to right-size pools, monitoring pool resource utilization to identify when resizing is needed, and grouping databases with complementary usage patterns to maximize the cost benefits of resource sharing.
Question 70:
Which tool is specifically designed for assessing SQL Server database compatibility with Azure SQL Database before migration?
A) SQL Server Management Studio
B) Data Migration Assistant
C) Azure Data Studio
D) SQLPackage
Answer: B
Explanation:
Data Migration Assistant (DMA) is a specialized tool from Microsoft specifically designed for assessing SQL Server database compatibility with Azure SQL Database and identifying migration blockers, deprecated features, and compatibility issues before beginning migration projects. This assessment capability is crucial for planning successful migrations by identifying required code changes, feature replacements, and potential challenges early in the migration process rather than discovering issues during or after migration.
DMA performs comprehensive database analysis by examining database schemas, stored procedures, functions, triggers, and application code for features that are not supported or behave differently in Azure SQL Database. The assessment generates detailed reports categorizing issues by severity, explaining why each identified feature is problematic, and providing recommendations for remediation. Issues are classified as migration blockers that must be resolved before migration, warnings about features requiring modification, or informational notices about functionality changes.
Beyond compatibility assessment, DMA provides recommendations for performance improvements and new features in Azure SQL Database that could benefit the migrated database. The tool can assess multiple databases in batch mode for large-scale migration projects and export results for reporting and tracking remediation progress. DMA also performs feature parity analysis showing which SQL Server features used by the database have alternatives or replacements in Azure SQL Database.
Option A is incorrect because while SSMS is a comprehensive management tool, it lacks the specialized migration assessment capabilities of DMA. Option C is wrong as Azure Data Studio is a modern database tool but does not provide specific Azure SQL Database compatibility assessment. Option D is not correct because SQLPackage is a command-line utility for deploying database packages but does not perform compatibility assessments.
Running Data Migration Assistant assessments early in migration planning enables teams to understand the scope of required changes, estimate migration effort accurately, identify applications requiring modification, and develop remediation strategies before committing to migration timelines.
Question 71:
What is the purpose of configuring a failover group for Azure SQL Database?
A) To improve query performance
B) To provide automatic failover for geo-replicated databases
C) To compress database storage
D) To schedule maintenance windows
Answer: B
Explanation:
Failover groups in Azure SQL Database provide automatic failover capabilities for geo-replicated databases, enabling applications to automatically connect to secondary replica databases in different Azure regions when primary databases become unavailable due to regional outages or disasters. This capability is essential for implementing high availability and disaster recovery solutions that minimize downtime and data loss while maintaining transparent connectivity for applications.
Failover groups create a named group containing one or more databases that are geo-replicated from a primary server in one region to a secondary server in another region. The group provides read-write and read-only listener endpoints that applications use for connectivity. The read-write listener automatically directs connections to the current primary database, whether that is the original primary or the secondary that became primary after failover. This automatic redirection means applications do not require connection string changes during failover events.
When automatic failover is enabled and the failover group detects that the primary region is unavailable, it automatically promotes the secondary database to primary role, updates listener endpoints to direct traffic to the new primary, and allows applications to reconnect seamlessly. Administrators can also trigger manual failover for planned maintenance or disaster recovery drills. The failover group coordinates failover for all databases in the group simultaneously, maintaining consistency across related databases.
Option A is incorrect because failover groups provide disaster recovery capabilities rather than performance optimization. Option C is wrong as compression is a separate storage optimization feature. Option D is not correct because maintenance windows are scheduled separately from failover group configuration.
Implementing failover groups requires provisioning primary and secondary servers in different Azure regions, adding databases to the failover group, configuring failover policy settings, updating application connection strings to use listener endpoints, and testing failover procedures to verify smooth failover and fallback operations.
Question 72:
Which Azure SQL Database security feature encrypts data in memory, in transit, and at rest?
A) Transparent Data Encryption
B) Always Encrypted
C) Dynamic data masking
D) Row-level security
Answer: B
Explanation:
Always Encrypted is a comprehensive encryption feature in Azure SQL Database that protects sensitive data by encrypting it in memory on the client side, during transmission, and while stored in the database, ensuring that encrypted data never appears in plaintext within the database system. This client-side encryption approach provides the highest level of data protection by ensuring that database administrators, cloud operators, and other highly privileged users cannot access sensitive plaintext data even with elevated permissions.
Always Encrypted operates by encrypting sensitive column data on the client side using encryption keys that never leave the client application. The encrypted data is transmitted over network connections and stored encrypted in database columns, with all encryption and decryption operations occurring within the trusted application boundary. The database server stores and processes only encrypted values, performing operations like storage and retrieval without accessing plaintext data.
The feature supports two types of encryption: deterministic encryption that produces the same encrypted value for identical plaintext enabling equality comparisons and lookups, and randomized encryption providing stronger security by producing different encrypted values for identical plaintext but limiting database operations to retrieval only. Applications require updated drivers and configuration to handle Always Encrypted columns, with the driver automatically encrypting data before sending to the database and decrypting results returned from queries.
Option A is incorrect because Transparent Data Encryption protects data at rest and during backup but not in memory or during processing. Option C is wrong as dynamic data masking obscures sensitive data in query results but does not encrypt data. Option D is not correct because row-level security controls which users can access which rows but does not provide encryption.
Implementing Always Encrypted requires careful planning for key management using Azure Key Vault or certificate stores, identifying columns requiring protection, updating applications to handle encrypted columns, and understanding query limitations imposed by encryption on deterministic and randomized columns.
Question 73:
What is the recommended isolation level for reducing blocking in Azure SQL Database OLTP workloads?
A) Serializable
B) Repeatable read
C) Read committed snapshot isolation (RCSI)
D) Read uncommitted
Answer: C
Explanation:
Read Committed Snapshot Isolation (RCSI) is the recommended isolation level for reducing blocking in Azure SQL Database OLTP workloads because it eliminates most reader-writer blocking by using row versioning instead of shared locks for read operations. This isolation level provides the same consistency guarantees as the default read committed isolation but with significantly better concurrency characteristics, making it ideal for transaction processing systems where maximizing throughput is important.
RCSI operates by maintaining versions of modified rows in tempdb, allowing read operations to access committed row versions that existed at the start of each statement without acquiring shared locks. This means readers do not block writers and writers do not block readers, dramatically reducing contention in busy OLTP systems. Transactions still acquire appropriate locks for modifications to prevent write-write conflicts and maintain data consistency.
Azure SQL Database enables RCSI by default for all new databases, recognizing its benefits for typical cloud workloads. Applications transparently benefit from reduced blocking without requiring code changes as RCSI maintains standard read committed semantics. The trade-off is additional tempdb space consumption for row versioning and slightly increased CPU overhead for version management, but these costs are generally outweighed by concurrency improvements in OLTP scenarios.
Option A is incorrect because serializable isolation provides the highest consistency but with the most blocking, unsuitable for high-concurrency OLTP. Option B is wrong as repeatable read also uses locks that cause blocking and is stricter than typically needed. Option D is not correct because read uncommitted allows dirty reads that compromise data consistency and is unsuitable for most applications.
Understanding RCSI behavior helps developers design applications that leverage the concurrency benefits while recognizing that long-running read queries may encounter snapshot conflicts if data they are reading is modified and the version chain is cleaned up during query execution.
Question 74:
Which Azure SQL Database feature allows executing queries across multiple databases using a single connection?
A) Temporal tables
B) Elastic query
C) JSON support
D) Memory-optimized tables
Answer: B
Explanation:
Elastic query in Azure SQL Database enables cross-database querying where applications can execute T-SQL queries that access tables and views spanning multiple Azure SQL databases using a single connection. This capability is essential for scenarios requiring data integration across distributed databases, reporting across multiple tenant databases, or accessing reference data stored separately from transactional databases without requiring complex application-level data aggregation logic.
Elastic query uses external data sources and external tables to define connections to remote databases and expose their tables as if they were local. Administrators create external data source definitions specifying connection information for remote databases, then create external table definitions that map to tables in those remote databases. Once defined, applications can query external tables using standard T-SQL syntax, with the database engine transparently executing queries against remote databases and returning results.
The feature supports two primary scenarios: vertical partitioning where different tables reside in different databases requiring cross-database joins, and horizontal partitioning (sharding) where rows of large tables are distributed across multiple databases based on sharding keys. Vertical partitioning queries enable joins between local and remote tables, while horizontal partitioning queries can execute against sharded tables with the database engine coordinating query execution across multiple shard databases.
Option A is incorrect because temporal tables provide historical data tracking within a single database, not cross-database querying. Option C is wrong as JSON support enables JSON data handling but not cross-database access. Option D is not correct because memory-optimized tables improve performance within a database but do not provide cross-database query capabilities.
Implementing elastic query requires careful consideration of performance implications as cross-database queries involve network communication and remote query execution, proper security configuration ensuring appropriate permissions on remote databases, and understanding query limitations compared to local table access.
Question 75:
What is the primary purpose of implementing data masking in Azure SQL Database?
A) To improve query performance
B) To hide sensitive data from unauthorized users while maintaining data usability
C) To encrypt data at rest
D) To compress database storage
Answer: B
Explanation:
Dynamic data masking in Azure SQL Database hides sensitive data from unauthorized users by obscuring data in query results while maintaining the original data unchanged in the database. This security feature enables organizations to limit exposure of sensitive information like credit card numbers, social security numbers, email addresses, or phone numbers to users who do not have business need to access full unmasked values, reducing the risk of data exposure through application vulnerabilities or unauthorized access.
Data masking operates by applying masking rules to designated sensitive columns, defining how data should be obscured for users who lack unmask permissions. When masked users query tables containing masked columns, the database automatically applies masks to returned data, showing partial information, random values, or default values instead of actual sensitive data. Privileged users with unmask permission receive unmasked data as normal, maintaining full data access for authorized personnel.
Azure SQL Database provides several built-in masking functions including default masking that shows X characters for strings and zero for numbers, email masking that shows only the first letter and domain, custom string masking allowing specification of exposed characters, and random number masking for numeric data. Masking rules are defined at the column level and automatically apply to all queries regardless of application or tool used to access data, providing consistent protection.
Option A is incorrect because data masking provides security by limiting data exposure rather than improving performance. Option C is wrong as encryption protects stored data whereas masking protects data in query results. Option D is not correct because compression reduces storage space but masking addresses security concerns.
Implementing dynamic data masking requires identifying columns containing sensitive data that should be masked, selecting appropriate masking functions for each column type, defining which users or roles should have unmask permission for legitimate business needs, and testing that masked values meet security requirements while maintaining application functionality.
Question 76:
Which Azure SQL Database deployment option provides the ability to pause compute to save costs when the database is not in use?
A) DTU-based service tier
B) Serverless compute tier
C) Hyperscale service tier
D) Business Critical service tier
Answer: B
Explanation:
The serverless compute tier for Azure SQL Database provides automatic compute scaling and the ability to pause databases during periods of inactivity, enabling significant cost savings for databases with intermittent usage patterns. This deployment option is ideal for development, testing, and production databases that experience variable workloads with idle periods where paying for continuously allocated compute resources would be inefficient.
Serverless databases automatically scale compute resources up or down within configured minimum and maximum vCore ranges based on workload demand, ensuring adequate performance during active periods while reducing compute allocation during quiet times. The compute cost is billed per-second based on actual vCores used, providing fine-grained cost control compared to provisioned compute where specific capacity is allocated and billed continuously regardless of utilization.
The auto-pause feature automatically pauses the database after a configured period of inactivity, stopping compute billing while maintaining the database storage. Storage costs continue during pause periods but compute costs cease entirely. When new connection attempts arrive at a paused database, it automatically resumes within seconds and begins processing requests. Organizations can configure auto-pause delays from one hour to seven days, or disable auto-pause for databases requiring continuous availability.
Option A is incorrect because DTU-based tiers use fixed resource allocations that are billed continuously without pause capabilities. Option C is wrong as Hyperscale is designed for large databases with high performance requirements and does not support auto-pause. Option D is not correct because Business Critical tier provides maximum performance and availability without auto-pause functionality.
Serverless compute is most beneficial for databases with usage patterns like development databases active only during business hours, departmental applications with predictable inactive periods, or PoC environments that can tolerate brief resume delays, while continuously active production systems should use provisioned compute tiers.
Question 77:
What is the recommended approach for scaling Azure SQL Database to handle increased workload temporarily?
A) Vertical scaling by increasing service tier
B) Horizontal scaling through sharding
C) Read scale-out with read replicas
D) Adding more indexes
Answer: A
Explanation:
Vertical scaling by increasing the service tier is the recommended approach for temporarily handling increased Azure SQL Database workload because it provides immediate additional resources including CPU, memory, and IO capacity through a simple online operation that typically completes in seconds to minutes. This scaling approach is ideal for accommodating temporary workload spikes, seasonal demand increases, or unexpected traffic surges without requiring application changes or complex infrastructure modifications.
Azure SQL Database supports online service tier changes where databases remain online and accessible during the scaling operation with minimal disruption. The scale-up process adds compute and memory resources to handle increased concurrent connections, more complex queries, or higher transaction volumes. Similarly, scaling down after peak periods reduces costs by returning to lower resource allocations appropriate for normal workload levels. Both scale-up and scale-down operations can be performed through Azure portal, PowerShell, CLI, or REST APIs.
The ability to dynamically adjust service tiers makes Azure SQL Database well-suited for workloads with varying resource requirements over time. Organizations can implement automated scaling using Azure Automation or Logic Apps that monitor performance metrics and trigger scaling operations based on resource utilization thresholds, ensuring adequate performance during peak periods while optimizing costs during normal operations. Scripts can scale up in anticipation of known busy periods and scale down afterward.
Option B is incorrect because horizontal scaling through sharding requires significant application changes and is more suitable for permanent architectural needs than temporary capacity increases. Option C is wrong as read scale-out addresses read-heavy workloads but does not increase write capacity. Option D is not correct because while indexes improve specific query performance, they do not provide general capacity increases for handling more concurrent workload.
Planning for workload variations should include monitoring resource utilization to understand scaling needs, establishing scaling thresholds that balance performance and cost, testing scaling operations in non-production environments, and potentially automating scaling for predictable patterns like daily or weekly cycles.
Question 78:
Which Azure SQL Database feature provides millisecond query performance for analytics workloads?
A) Row store indexes
B) Columnstore indexes
C) Spatial indexes
D) XML indexes
Answer: B
Explanation:
Columnstore indexes provide exceptional query performance for analytics workloads by storing data in columnar format optimized for data warehousing and reporting scenarios, delivering queries that execute in milliseconds rather than seconds or minutes compared to traditional row store indexes. This technology is specifically designed for analytical queries that scan large volumes of data, perform aggregations across many rows, and access only subset of columns, making it ideal for reporting, business intelligence, and data analysis workloads.
Columnstore indexes organize data by columns rather than rows, storing each column’s data together and applying aggressive compression that typically reduces storage by five to ten times compared to row storage. This columnar organization means analytical queries that access few columns read only relevant columns instead of entire rows, dramatically reducing IO requirements. The compression further reduces data read from storage and enables more data to fit in memory for faster access.
Azure SQL Database supports both clustered columnstore indexes where the entire table is stored in columnar format, and nonclustered columnstore indexes that provide a secondary columnstore representation alongside row store tables. Clustered columnstore is ideal for pure analytics tables, while nonclustered columnstore enables analytics on operational tables without impacting OLTP performance. The database engine automatically chooses batch mode execution for queries against columnstore indexes, processing operations on batches of rows simultaneously for massive parallelization.
Option A is incorrect because row store indexes are optimized for OLTP workloads with high transaction rates but provide slower analytics performance. Option C is wrong as spatial indexes optimize geometric and geographic queries but do not provide general analytics performance improvements. Option D is not correct because XML indexes optimize XML query performance but are not designed for high-performance analytics workloads.
Implementing columnstore indexes requires identifying analytical workload patterns that scan many rows, evaluating whether workloads are pure analytics suitable for clustered columnstore or mixed OLTP/analytics requiring nonclustered columnstore, and monitoring query performance improvements to validate that columnstore provides expected benefits.
Question 79:
What is the primary benefit of using Azure SQL Database managed instance?
A) Lower cost than Azure SQL Database
B) Near 100% compatibility with on-premises SQL Server
C) Automatic query tuning only
D) Unlimited database size
Answer: B
Explanation:
Azure SQL Database managed instance provides near 100% compatibility with on-premises SQL Server, supporting features and capabilities not available in Azure SQL Database single databases or elastic pools. This high compatibility makes managed instance the optimal choice for lift-and-shift migrations where organizations want to move SQL Server workloads to Azure with minimal application changes while gaining cloud benefits like automated patching, backups, and high availability.
Managed instance supports SQL Server features critical to many enterprise applications including cross-database queries using three-part naming, SQL Agent for job scheduling, Service Broker for reliable messaging, Database Mail for email notifications, linked servers for accessing external data sources, CLR assemblies for .NET code execution, and native backup/restore to Azure blob storage. These features enable existing applications designed for on-premises SQL Server to run in managed instance with little or no modification.
The deployment model provides a dedicated virtual network subnet where managed instances operate with private IP addresses, enabling integration with on-premises networks through VPN or ExpressRoute connections. This network isolation meets security and compliance requirements while enabling hybrid scenarios where applications span on-premises and cloud environments. Managed instance also supports instance-level collation settings, multiple databases per instance, and cross-database transactions.
Option A is incorrect because managed instance typically costs more than single databases due to its dedicated compute resources and additional features. Option C is wrong as automatic query tuning is available in multiple Azure SQL offerings, not unique to managed instance. Option D is not correct because database size limits exist though they are substantial, with maximum storage up to 16TB depending on configuration.
Choosing managed instance is appropriate for migrations requiring high SQL Server compatibility, applications using unsupported features in Azure SQL Database, scenarios requiring cross-database queries within an instance, or environments needing SQL Agent scheduling capabilities without requiring application modifications.
Question 80:
Which Azure SQL Database monitoring metric indicates that queries are waiting for data to be read from disk?
A) CPU percentage
B) Physical data read percentage
C) Log write percentage
D) DTU percentage
Answer: B
Explanation:
Physical data read percentage in Azure SQL Database indicates the proportion of time that queries are waiting for data to be physically read from storage devices, signaling that the database is experiencing IO bottlenecks where data cannot be served from memory cache and must be retrieved from disk. High physical read percentages suggest that the buffer pool does not contain sufficient data to satisfy query demands, causing performance degradation as queries wait for relatively slow disk IO operations compared to memory access.
This metric is calculated as the percentage of maximum available data read IO bandwidth being consumed by physical read operations. When the percentage approaches 100%, the database is saturating its available read IO capacity and queries will experience delays waiting for data reads to complete. This situation commonly occurs with insufficient memory for caching data, queries scanning large amounts of data not previously cached, or missing indexes forcing table scans instead of efficient index seeks.
Addressing high physical read percentages involves multiple approaches depending on root causes. Scaling to higher service tiers increases both memory for caching and maximum IO throughput. Query optimization including adding appropriate indexes reduces the volume of data that must be scanned. Redesigning data access patterns to access smaller data sets or implementing data archiving for historical data reduces working set size. Implementing read scale-out distributes read workload across replicas reducing primary database IO load.
Option A is incorrect because CPU percentage indicates processor utilization rather than storage IO waits. Option C is wrong as log write percentage measures transaction log write IO, not data read operations. Option D is not correct because DTU percentage is a blended metric combining CPU, memory, and IO rather than specifically indicating physical read waits.
Monitoring physical data read percentage alongside other metrics like memory usage, missing index recommendations, and query statistics helps diagnose whether high physical reads stem from insufficient memory, missing indexes, or inefficient queries, guiding appropriate remediation strategies.
