Microsoft DP-300 Administering Microsoft Azure SQL Solutions Exam Dumps and Practice Test Questions Set 4 Q61-80

Visit here for our full Microsoft DP-300 exam dumps and practice test questions.

Question 61

You need to ensure that multiple databases with varying workloads share compute and storage efficiently while maintaining isolation. Which feature should you use?

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

Answer:  A) Elastic Pool

Explanation:

Elastic Pool is a feature in Azure SQL Database that is specifically designed to allow multiple databases to share a pool of compute and storage resources. This is particularly useful when you have databases with unpredictable or varying workloads. By sharing resources within the pool, databases can draw compute and storage capacity as needed, which improves cost efficiency by avoiding over-provisioning for individual databases. The pool maintains logical isolation between databases, so even though resources are shared, the performance of one database does not directly interfere with another. This makes Elastic Pools ideal for SaaS applications or environments where many small databases exist with fluctuating usage patterns.

The Business Critical tier provides high performance and low-latency storage for individual databases. It is designed to handle workloads that require fast transactional processing and high availability with features such as in-memory OLTP and multiple replicas. However, it is targeted at single databases and does not allow sharing of compute and storage resources across multiple databases. While this tier ensures optimal performance for individual workloads, it is not cost-efficient when dealing with multiple databases with varying usage, because each database must be sized independently, and idle resources in one database cannot be leveraged by others.

Hyperscale tier is built for extremely large databases, supporting rapid scaling of both storage and compute resources. It separates compute, log, and storage layers to allow independent scaling and very fast database growth. However, Hyperscale is designed for a single large database rather than managing multiple smaller databases. It does not offer the ability to pool resources across databases, and it is optimized for a different scenario—handling massive amounts of data with fast provisioning and storage expansion, rather than balancing costs across multiple databases with inconsistent workloads.

Read Scale-Out allows offloading read-only workloads from the primary database to one or more secondary replicas. This improves read performance by reducing the load on the primary, but it does not provide shared compute or storage resources among multiple databases. It is specifically for read-intensive workloads in Business Critical databases and does not address cost optimization across multiple databases.

Elastic Pool is the correct choice because it addresses the exact scenario of multiple databases with varying workloads. It allows databases to efficiently share compute and storage resources while keeping each database logically isolated. By consolidating resources, it reduces overall cost and improves utilization, making it suitable for environments where workloads are unpredictable and varied, which neither Business Critical, Hyperscale, nor Read Scale-Out is designed to do.

Question 62

You want to perform computations on encrypted data without exposing plaintext to the SQL Server or administrators. Which feature should you implement?

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

Answer:  A) Always Encrypted with secure enclaves

Explanation:

Always Encrypted with secure enclaves is a feature that enables computations to be performed on sensitive data while keeping it encrypted. It uses hardware-based secure enclaves on the server to allow operations like comparison, sorting, or pattern matching on encrypted values without exposing the underlying plaintext to SQL Server or administrators. This ensures that sensitive data remains confidential even from privileged users who have administrative access. This approach is crucial for applications requiring a high level of data privacy, such as financial or healthcare systems, where regulatory compliance mandates strict protection of sensitive information.

Transparent Data Encryption (TDE) encrypts the data at rest within the database. While it protects data files and backups from unauthorized access, the data is decrypted in memory when queried, meaning that SQL Server or administrators can access plaintext during query execution. TDE does not support performing computations on encrypted data in a way that keeps it secure from the database engine. Therefore, while it addresses data-at-rest protection, it does not meet the requirement of secure computation without exposing plaintext.

Dynamic Data Masking obscures sensitive data in query results by replacing it with masked values. While this can prevent users from seeing sensitive information in outputs, it does not actually encrypt the data in storage or during query execution. The underlying data remains accessible in plaintext to SQL Server and administrators. This makes it insufficient for scenarios that require cryptographic protection of data while allowing computation.

Row-Level Security (RLS) restricts access to specific rows based on user context, ensuring that only authorized users can see certain rows. However, RLS does not provide encryption or protection against exposure of data to the server or administrators. It is purely an access control mechanism and cannot enable computations on encrypted data.

Encrypted with secure enclaves is the correct choice because it allows computations on encrypted data without exposing plaintext, fulfilling the requirement of protecting sensitive information from the database engine and privileged users while still supporting application functionality.

Question 63 

You need to automatically detect and remediate query performance regressions in Azure SQL Database. Which feature should you enable?

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

Answer:  A) Automatic Plan Correction

Explanation:

Automatic Plan Correction is a feature designed to monitor query execution in real time and automatically detect performance regressions caused by changes in query execution plans. When a regression is detected, it enforces the previously known good plan without manual intervention, ensuring consistent query performance. This proactive approach is particularly valuable in environments where workloads are dynamic, and query plans can unexpectedly degrade due to statistics updates, schema changes, or other environmental factors.

Query Store captures historical execution statistics, query texts, and execution plans over time. It allows administrators to analyze trends and investigate regressions manually. While it is essential for understanding query behavior and diagnosing performance issues, Query Store does not automatically enforce corrective actions. Any remediation requires explicit intervention to force a plan or adjust queries, meaning it cannot address regressions automatically.

Intelligent Insights provides monitoring and diagnostic capabilities, offering recommendations for performance tuning and workload optimization. It can detect anomalies and suggest actions to improve performance, but it does not automatically implement corrective measures. Administrators must review the insights and apply any recommendations themselves. This makes it more of a diagnostic tool rather than an automated performance correction mechanism.

Extended Events is a low-overhead monitoring framework used to capture detailed diagnostic information about events occurring in the SQL Server engine. It is highly configurable and useful for troubleshooting complex issues, but it does not automatically detect or remediate performance regressions. Its role is purely observational, requiring interpretation and manual action based on the collected events.

Automatic Plan Correction is the correct solution because it directly addresses the requirement for continuous detection and remediation of query performance regressions. Unlike Query Store, Intelligent Insights, or Extended Events, it not only identifies regressions but also automatically applies the corrective plan, maintaining optimal performance without manual intervention.

Question 64

You want to offload read-only queries from a Business Critical Azure SQL Database to improve performance. Which feature should you use?

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

Answer:  A) Read Scale-Out

Explanation:

Read Scale-Out is a feature of the Business Critical tier in Azure SQL Database that allows secondary replicas to handle read-only queries. This offloads the read workload from the primary database, reducing contention and improving overall read performance without impacting write operations. It is particularly effective for reporting, analytics, or applications that generate a high volume of read queries alongside transactional workloads.

Auto-Failover Groups are designed for disaster recovery and high availability. They provide automated failover of a primary database to a secondary in case of a failure, but they do not provide offloading of read workloads. Their purpose is resilience rather than performance optimization for read-heavy operations.

Elastic Pool is intended for resource sharing among multiple databases to improve cost efficiency and manage workloads with varying utilization. While it helps balance compute and storage across databases, it does not provide the ability to offload reads from a primary database to a secondary replica, so it is not relevant to read scale-out scenarios.

Hyperscale replicas exist in the Hyperscale tier and allow scaling of storage and compute for very large single databases. They are not available in the Business Critical tier and are not intended to serve as read-only replicas for offloading queries in this context.

Read Scale-Out is the correct solution because it provides dedicated read replicas for handling read-only workloads in Business Critical databases, allowing separation of read and write operations, improving performance, and reducing load on the primary database.

Question 65

You want to monitor and capture execution plans for long-running queries to identify performance regressions over time. Which feature should you enable?

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

Answer:  A) Query Store

Explanation:

Query Store is designed to capture query execution statistics and execution plans over time. It records information about query text, performance metrics, and plan details, providing a historical perspective on query behavior. This allows administrators to track long-running queries, identify performance regressions, and analyze trends in execution patterns. By keeping a repository of query performance data, Query Store enables evidence-based decision-making for query tuning and optimization.

Extended Events is a lightweight event-handling framework that captures diagnostic information about server activity. While it can record query executions, it does not automatically correlate performance trends or maintain historical execution plan data in a structured way. Administrators must analyze the collected events manually, which makes it less suited for continuous monitoring of long-running queries and regression analysis.

SQL Auditing is focused on tracking database access and security-related actions, such as login attempts and changes to sensitive data. It does not capture detailed query performance metrics or execution plans, and its primary purpose is compliance and auditing rather than performance monitoring.

Intelligent Insights provides analysis of workloads and recommendations for optimization. While it highlights potential issues and suggests improvements, it does not maintain a history of execution plans or performance data over time. It is more of a diagnostic and advisory tool rather than a historical tracking solution.

Query Store is the correct choice because it captures and maintains historical execution plans and performance metrics, allowing systematic monitoring of long-running queries and identification of regressions over time. It directly supports analysis and optimization of query performance in a way that the other options do not.

Question 66 

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

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

Answer:  A) Row-Level Security

Explanation:

Row-Level Security is designed specifically to control access to individual rows in a table based on the characteristics of the user querying the database. It uses predicates, such as inline table-valued functions, to filter rows dynamically according to the user’s context, roles, or attributes. This means that users only see the data they are authorized to access without modifying queries or adding additional filtering logic in the application layer. Row-Level Security provides fine-grained access control and is fully integrated with the SQL Server security model, allowing administrators to enforce policies consistently across applications.

Dynamic Data Masking, on the other hand, does not actually restrict access to data. Instead, it obscures sensitive information in query results, such as replacing credit card numbers with masked values. Users with sufficient privileges can still see the original data, so it does not enforce true access control at the row level. While useful for preventing casual exposure of sensitive information, it cannot replace security policies that require restricting access based on user identity or roles.

Always Encrypted focuses on protecting sensitive data by encrypting it on the client side, ensuring that plaintext values are never revealed to the SQL Server engine or administrators. While this provides strong protection for sensitive columns such as social security numbers or credit card information, it does not filter or restrict access to specific rows based on user attributes. Encrypted data cannot be selectively accessed based on the querying user without decryption on the client side, so it is not suitable for row-level access control policies.

Transparent Data Encryption (TDE) secures the entire database at rest, encrypting data files and backups automatically. TDE is an essential tool for protecting data from unauthorized access at the storage level, but it does not affect user-level permissions, row visibility, or query results. It ensures that data is unreadable if the storage media is stolen, but once the database is online, users with appropriate access can query all rows. Row-Level Security is the only option that enforces fine-grained access control at the row level, making it the correct choice for scenarios requiring attribute-based access policies.

Question 67 

You need to maintain backup copies in a cost-effective, long-term storage solution to satisfy regulatory requirements. Which feature should you use?

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

Answer:  A) Long-Term Backup Retention

Explanation:

Long-Term Backup Retention is a feature in Azure SQL Database that allows you to store full database backups for extended periods, often years, depending on compliance and regulatory requirements. It enables organizations to meet retention policies for auditing, compliance, and disaster recovery purposes. By using Long-Term Backup Retention, you can restore a database to any point in time within the retention period, ensuring data can be recovered even after long durations. This feature is specifically optimized for cost-effective long-term storage in Azure Storage, making it suitable for organizations with strict regulatory obligations.

Geo-Redundant Backup Storage provides replication of backups to a secondary region to protect against regional outages. While it ensures business continuity and disaster recovery, it is primarily focused on maintaining availability rather than retaining backups for long durations. It does not address regulatory requirements for multi-year retention, which is a key differentiator from Long-Term Backup Retention.

Auto-Failover Groups are designed to provide high availability and automatic failover capabilities across multiple regions. They ensure that applications can quickly fail over to a secondary database in case of an outage, but they do not manage backup retention policies. While they are valuable for minimizing downtime, they are not intended for long-term storage of backups to satisfy regulatory compliance.

Transparent Data Encryption (TDE) encrypts data at rest and protects against unauthorized access to storage files. However, TDE does not manage backups or retention periods and therefore cannot satisfy regulatory requirements for long-term backup storage. In scenarios where compliance and cost-effective, multi-year backup retention are required, Long-Term Backup Retention is the feature explicitly designed to meet these needs, making it the correct choice.

Question 68 

You want to encrypt sensitive columns and allow applications to perform queries without revealing plaintext to administrators. Which feature should you enable?

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

Answer:  A) Always Encrypted

Explanation:

Always Encrypted is a security feature that protects sensitive data by encrypting it on the client side, ensuring that SQL Server never sees the plaintext values. This allows applications to perform operations such as searches or computations on encrypted columns without exposing the underlying data to database administrators or anyone with server-level access. Always Encrypted provides strong cryptographic protection while maintaining usability for authorized applications, making it ideal for scenarios where data privacy must be preserved at all times.

Transparent Data Encryption (TDE) encrypts the entire database on disk to prevent unauthorized access to the physical storage. While TDE protects data at rest, it does not prevent database administrators from querying sensitive data. Once the database is online, any user with sufficient permissions can read the plaintext data. TDE is therefore not suitable for scenarios requiring strict client-side privacy.

Column-Level Permissions control access to specific columns based on database roles or user accounts. Although this restricts visibility at the database level, it does not provide cryptographic security. Users with high-level permissions or attackers who compromise the database engine could still access the data. Column-Level Permissions alone cannot satisfy the requirement to prevent administrators from seeing plaintext.

Dynamic Data Masking obscures sensitive information in query results by applying masks, such as replacing the content with asterisks. It prevents casual exposure in applications but does not encrypt the underlying data, nor does it stop users with direct database access from seeing the real values. Always Encrypted is the only feature among the options that combines client-side encryption with the ability for applications to query data without exposing plaintext to SQL Server, making it the correct solution.

Question 69 

You want to monitor anomalous database activities and receive proactive alerts for potential threats. Which feature should you enable?

A) Threat Detection
B) Query Store
C) Automatic Plan Correction
D) SQL Auditing

Answer:  A) Threat Detection

Explanation:

Threat Detection continuously monitors database activity for patterns that indicate potential security risks, such as SQL injection attempts, brute-force login attempts, or unusual query activity. It uses machine learning and built-in heuristics to identify anomalies in real-time and sends alerts to administrators, enabling proactive mitigation of threats. Threat Detection provides actionable insights, including details about the suspicious activity, affected users, and recommended steps to remediate the issue.

Query Store captures historical query execution plans, runtime statistics, and performance metrics. While Query Store is excellent for performance monitoring and regression analysis, it does not provide security monitoring or alerting for anomalous activity. It focuses solely on query performance optimization, making it irrelevant for proactive threat detection.

Automatic Plan Correction monitors execution plan changes and automatically reverts them if they negatively impact query performance. This feature is purely related to performance management and does not address security concerns, so it cannot help in monitoring anomalous activities or generating alerts for potential threats.

SQL Auditing records database-level events, including login attempts, schema changes, and data modifications, providing a detailed audit trail. While auditing is critical for compliance and post-incident analysis, it does not proactively detect threats or send alerts in real-time. Auditing requires manual review to identify suspicious patterns, whereas Threat Detection actively monitors and notifies administrators immediately. Given these distinctions, Threat Detection is the correct choice for monitoring anomalous activities and generating proactive alerts.

Question 70 

You want to reduce compute costs for a database that is idle most of the day while ensuring it automatically scales when needed. Which deployment model should you select?

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

Answer:  A) Serverless compute tier

Explanation:

The Serverless compute tier is designed for workloads with intermittent or unpredictable usage. It automatically scales compute resources based on workload demand and pauses the database when it is idle, thereby minimizing costs. When activity resumes, the compute resources automatically resume, allowing seamless scaling without manual intervention. This feature is particularly effective for development, testing, or lightly used production databases that do not require a constant allocation of resources.

The Hyperscale tier is intended for very large databases that require high storage capacity and fast scaling for massive workloads. Although it provides excellent performance and near-instantaneous scaling of storage, it does not pause compute resources when the database is idle, which means it cannot reduce costs for databases that are inactive for most of the day.

The Business Critical tier provides high-performance resources and redundancy for mission-critical workloads. It offers predictable low-latency performance but maintains a fixed allocation of compute resources regardless of utilization. While it ensures high availability and consistent performance, it is not optimized for cost savings when the database experiences low or intermittent activity.

Elastic Pools allow multiple databases to share resources within a predefined pool. While this model is useful for optimizing costs across multiple databases with varying usage patterns, individual databases do not pause when idle, and resource allocation depends on the total pool configuration. In contrast, Serverless compute directly addresses the requirement for automatic scaling and cost reduction during idle periods, making it the optimal choice for scenarios with low or fluctuating database activity.

Question 71 

You need to provide multiple readable secondary replicas to offload reporting workloads from the primary database in Azure SQL Database. Which feature should you enable?

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

Answer:  A) Read Scale-Out

Explanation:

Read Scale-Out is a feature of Azure SQL Database that allows multiple readable secondary replicas to be provisioned for a database. These replicas are synchronized with the primary database, providing read-only endpoints that applications can use to execute reporting or analytics queries. By directing read-heavy workloads to these secondary replicas, the primary database remains dedicated to transactional operations, maintaining high performance and reducing contention for resources. This feature is particularly beneficial for scenarios with high reporting demand or complex analytical queries.

Auto-Failover Groups are designed to manage high availability and disaster recovery by providing automatic failover between primary and secondary databases. While they create a secondary in another region, the primary purpose is to ensure continuity in the event of a failure, not to offload read workloads. Using Auto-Failover Groups solely for reporting would not be cost-efficient and could complicate the failover strategy.

Elastic Pools allow multiple databases to share compute and storage resources within a pool. They optimize cost and resource allocation for databases with unpredictable usage patterns, but they do not create readable replicas. Elastic Pools are ideal for managing multiple databases efficiently, but they do not directly address the requirement to offload reporting workloads to secondary replicas.

Transparent Network Redirect is a feature that ensures client connections are automatically redirected to the appropriate server after a failover. While it simplifies client reconnections, it does not create secondary replicas or enable read-only workloads. Its purpose is focused on seamless failover rather than performance offloading.

Considering these options, Read Scale-Out is specifically designed to offload read-intensive workloads by providing multiple secondary replicas. It directly addresses the requirement of allowing reporting queries without impacting the primary database’s transactional performance, making it the correct choice.

Question 72 

You need to store auditing logs in a secure, durable, and centralized location for regulatory compliance. Which destination should you use?

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

Answer:  A) Azure Storage account

Explanation:

Azure Storage accounts provide a highly secure, durable, and scalable option for storing audit logs over long periods. They support retention policies and encryption at rest, meeting regulatory requirements for long-term storage of sensitive data. Azure Storage is cost-effective for storing large volumes of logs and ensures that the data remains tamper-resistant and available for audits or compliance verification.

Log Analytics workspace is primarily designed for querying, analyzing, and visualizing telemetry and diagnostic data. While it can store audit-related data and provide insights, it is not ideal for long-term archival or compliance purposes. Logs may be retained for limited periods unless specifically configured, making it less suitable as a central repository for regulatory audits.

Event Hubs is a high-throughput data streaming platform used for ingesting and processing event-driven data in real-time. It is excellent for streaming scenarios, analytics pipelines, or integrating with other services but is not meant for durable, long-term storage of logs. Logs in Event Hubs are transient and typically consumed by downstream systems, which does not satisfy compliance requirements for retention.

Power BI is a reporting and visualization tool. It can consume data from logs to create dashboards or visual reports, but it does not serve as a storage repository for raw audit logs. Using Power BI would meet reporting needs but fail to comply with regulatory storage requirements.

Given the need for secure, centralized, and durable storage of auditing logs, Azure Storage account is the most appropriate choice. It ensures logs are maintained in compliance with regulations, provides encryption and durability guarantees, and is cost-effective for long-term retention.

Question 73 

You need to ensure very large databases can scale storage independently of compute while handling multi-terabyte workloads. Which tier should you select?

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

Answer:  A) Hyperscale

Explanation:

Hyperscale is an Azure SQL Database tier designed specifically for very large databases requiring rapid and independent scaling of storage and compute resources. Its architecture separates storage from compute, allowing databases to grow to multiple terabytes without the constraints imposed by compute-bound storage limits. Hyperscale also provides fast backup and restore operations due to its page-oriented storage architecture, making it highly suitable for enterprise-grade large workloads.

The Business Critical tier provides high-performance storage and low-latency transaction processing but has fixed storage limits tied to compute capacity. While it supports workloads requiring low latency, it does not provide the same level of independent storage scalability as Hyperscale. Large-scale databases may hit storage limits more quickly in this tier.

General Purpose tier provides balanced compute and storage for most workloads but couples storage scaling with compute size. It is not optimized for very large, multi-terabyte databases and may face performance bottlenecks when scaling beyond certain limits.

Serverless is a compute model that allows automatic scaling of compute resources based on demand, pausing during inactivity to save costs. While flexible, Serverless does not provide independent scaling for storage and is not intended for extremely large databases with multi-terabyte requirements.

Considering these factors, Hyperscale is the only tier designed to independently scale storage for massive databases while maintaining high performance. This makes it the correct choice for handling large, multi-terabyte workloads efficiently.

Question 74 

You want to automatically redirect client connections after a failover without modifying the application connection strings. Which feature should you enable?

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

Answer:  A) Transparent Network Redirect

Explanation:

Transparent Network Redirect is designed to manage client connectivity during failover events automatically. When a failover occurs, this feature ensures that client applications are seamlessly redirected to the new primary endpoint without requiring any manual changes to connection strings. This reduces downtime and simplifies the application’s high-availability architecture.

Auto-Failover Groups provide high availability by maintaining a secondary database for automatic failover. While they handle the failover itself, applications typically require some form of redirection logic unless combined with Transparent Network Redirect. Without TNR, client applications may need manual intervention or connection retries after failover.

Read Scale-Out provides secondary readable replicas to offload reporting workloads. It does not manage failover redirection or client connectivity, focusing instead on performance optimization for read-heavy workloads.

Elastic Pools optimize resource sharing across multiple databases but do not provide failover or client redirection capabilities. They manage compute and storage allocation rather than connectivity after a failover event.

Transparent Network Redirect directly meets the requirement for automatic redirection post-failover, ensuring minimal disruption to client applications and eliminating the need for connection string modifications, making it the correct solution.

Question 75
 

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

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

Answer:  A) Accelerated Database Recovery

Explanation:

Accelerated Database Recovery (ADR) is a feature designed to improve database availability by drastically reducing the time required to recover from long-running transactions. It maintains persistent versioned transaction logs and a recovery version store, allowing transactions to be rolled back almost instantly. This is particularly beneficial for large databases with complex transactions, where traditional rollback can take a significant amount of time.

Auto-Failover Groups provide automatic failover capabilities between primary and secondary databases to ensure high availability. While they help maintain uptime, they do not specifically reduce recovery time for individual transactions and are not designed to speed up rollbacks or transaction recovery.

Transparent Data Encryption secures data at rest by encrypting the database files, log files, and backups. While critical for security and compliance, it has no impact on the speed of transaction recovery. ADR and TDE serve different purposes entirely.

Long-Term Backup Retention ensures that historical backups are preserved for compliance and disaster recovery purposes. While it allows recovery from older points in time, it does not optimize the speed of rolling back ongoing or long-running transactions.

Given the need to reduce recovery time for long-running transactions, Accelerated Database Recovery is uniquely suited to provide near-instant rollbacks and maintain high availability, making it the correct choice.

Question 76 

You want to perform analytics on read-only replicas without impacting the primary Azure SQL Database. Which feature should you enable?

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

Answer:  A) Read Scale-Out

Explanation:

Read Scale-Out is a feature in Azure SQL Database that allows you to offload read-only workloads, such as reporting and analytics, to secondary replicas. By doing this, the primary database is not impacted by potentially resource-intensive read operations, ensuring that transactional performance remains consistent. This is particularly beneficial in Business Critical tier databases where high performance and low latency are required for the primary workload. Read Scale-Out essentially enables better resource utilization by separating transactional and analytical operations.

Auto-Failover Groups are designed primarily for high availability and disaster recovery. They allow a group of databases to fail over automatically or manually to another region. While they maintain a secondary replica for disaster recovery purposes, their main intent is failover management rather than offloading read workloads. Therefore, using Auto-Failover Groups does not inherently improve the performance of read-heavy analytics tasks on the primary database.

Elastic Pool allows multiple databases to share a pool of compute and storage resources. While this optimizes cost and efficiency for databases with varying or unpredictable workloads, it does not create read-only secondary replicas. Consequently, analytics queries executed in an elastic pool still operate on the primary database, potentially impacting performance if workloads are heavy. Elastic Pools are more about resource sharing than read workload offloading.

Hyperscale tier is designed for very large databases and decouples storage from compute, allowing the database to scale to multiple terabytes. It improves scalability and performance for extremely large datasets but does not provide secondary replicas for offloading read-only workloads in Business Critical configurations. Therefore, it cannot meet the requirement of performing analytics without impacting the primary database.

The correct solution is Read Scale-Out because it specifically addresses the scenario of running read-intensive workloads on secondary replicas, ensuring that the primary database remains unaffected while still providing timely access to analytical data. This approach improves performance isolation and optimizes overall resource usage.

Question 77 

You want to implement row-level access policies based on user department for compliance. Which feature should you use?

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

Answer:  A) Row-Level Security

Explanation:

Row-Level Security (RLS) enables you to control access to rows in a table based on user characteristics, such as department or role. By defining security predicates, you can ensure that users only see the rows they are permitted to access. This is particularly useful for compliance scenarios where sensitive information must be restricted depending on organizational policies. RLS is applied at the database engine level, meaning it does not rely on application logic and provides consistent access control.

Dynamic Data Masking (DDM) is often confused with RLS, but it has a different purpose. DDM obscures sensitive data in query results, typically by replacing it with masked values. However, it does not prevent users from accessing rows themselves. A user might still see a row exists, but some values are hidden. This is useful for reducing exposure of sensitive data but does not enforce row-level access control.

Always Encrypted protects data at the column level by encrypting it both at rest and in transit, ensuring that only authorized applications with access to encryption keys can decrypt it. However, it does not filter which rows a user can access; it only ensures that encrypted data cannot be read without proper keys. Thus, it addresses data protection rather than access control.

Transparent Data Encryption (TDE) encrypts the entire database at rest to protect against unauthorized access to the underlying storage. TDE ensures that if someone gains access to the storage files directly, they cannot read the data, but it does not provide any user-level access restrictions within the database itself.

Row-Level Security is the correct choice because it provides precise control over which rows users can access based on attributes like department. This satisfies compliance requirements by restricting sensitive information at the row level without relying on masking or encryption alone.

Question 78 

You want to monitor Azure SQL Database for suspicious activity and generate alerts for potential security threats. Which feature should you enable?

A) Threat Detection
B) Query Store
C) Automatic Plan Correction
D) SQL Auditing

Answer:  A) Threat Detection

Explanation:

Threat Detection in Azure SQL Database continuously monitors database activity for anomalies and potential security threats, such as unusual login attempts, access from unfamiliar IP addresses, or SQL injection attacks. It provides proactive alerts in real time, helping administrators respond quickly to mitigate risks. Threat Detection can be integrated with email notifications or Azure Security Center to provide comprehensive monitoring across multiple databases.

Query Store is primarily used for tracking query performance over time. It captures query execution plans, runtime statistics, and historical data to help identify performance regressions. While Query Store is valuable for performance tuning and troubleshooting, it does not provide security monitoring or alerting capabilities.

Automatic Plan Correction focuses on maintaining consistent query performance by detecting regressions in execution plans and automatically forcing previously known good plans. While it helps ensure efficient database operations, it does not address security threats, anomalies, or user behavior monitoring.

SQL Auditing records database events such as logins, schema changes, and query execution details. Auditing provides a record of activities but does not automatically detect suspicious activity or generate alerts for potential threats. Administrators need to manually analyze logs to identify anomalies.

Threat Detection is the correct choice because it is designed specifically to identify unusual or potentially harmful activity and notify administrators immediately. This proactive monitoring is essential for maintaining the security of Azure SQL Database.

Question 79 

You want to store auditing logs in a centralized, secure location with long-term retention for compliance purposes. Which destination should you use?

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

Answer:  A) Azure Storage account

Explanation:

Azure Storage accounts offer durable and secure storage that is ideal for long-term retention of auditing logs. They provide the ability to enforce retention policies, encryption, and access controls, ensuring that auditing data remains compliant with regulatory standards. Storage accounts can handle large volumes of log data and provide persistence over extended periods.

Log Analytics workspace is designed for monitoring, querying, and analyzing data. While it can ingest auditing logs and provide insights, it is not intended for indefinite long-term storage. Retention periods in Log Analytics are typically limited and may require additional cost or configuration to store logs for compliance purposes.

Event Hubs is a real-time data ingestion service that streams events from multiple sources. Although Event Hubs can capture auditing data for processing or integration with other systems, it is not a persistent storage solution. Data stored in Event Hubs is transient and primarily used for event streaming rather than long-term archival.

Power BI is a reporting and visualization platform. It is suitable for creating dashboards and analyzing data but cannot provide secure, persistent storage for auditing logs. Data in Power BI is intended for visualization and is not designed to meet regulatory compliance requirements.

Azure Storage account is the correct choice because it provides secure, centralized, and durable storage capable of retaining logs for long periods, which is essential for compliance and auditing requirements.

Question 80 

You need to scale storage independently of compute for very large Azure SQL Databases while supporting multi-terabyte workloads. Which tier should you select?

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

Answer:  A) Hyperscale

Explanation:

The Hyperscale tier in Azure SQL Database is specifically designed for large databases that require the ability to scale storage independently of compute resources. This architecture decouples storage from the compute layer, allowing storage to grow seamlessly to accommodate multi-terabyte workloads without affecting compute performance. It is ideal for applications with unpredictable growth or high-volume data processing needs.

Business Critical tier provides high-performance storage and low-latency operations by tightly coupling storage and compute. However, storage limits are fixed, and scaling beyond those limits requires a tier upgrade. While it is excellent for transactional workloads, it does not offer the flexibility needed for multi-terabyte storage scaling.

General Purpose tier offers balanced compute and storage performance, suitable for most business applications. However, it does not decouple storage and compute, which limits the maximum database size compared to Hyperscale. Storage scaling is constrained, making it less appropriate for extremely large databases.

Serverless tier automatically scales compute based on workload demand but does not separate storage scaling from compute. It is suitable for intermittent workloads with unpredictable usage patterns, but like General Purpose, it cannot handle multi-terabyte storage efficiently.

Hyperscale is the correct choice because it allows independent storage expansion, supports extremely large databases, and provides the performance and flexibility required for massive, growing workloads.