Microsoft DP-300 Practice Test Questions, Exam Dumps

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DP-300 Microsoft Practice Test Questions and Exam Dumps

Question No 1:

You have 20 Azure SQL databases provisioned by using the vCore purchasing model. You plan to create an Azure SQL Database elastic pool and add the 20 databases.

Which three metrics should you use to size the elastic pool to meet the demands of your workload? Each correct answer presents part of the solution. NOTE: Each correct selection is worth one point.

A. total size of all the databases
B. geo-replication support
C. number of concurrently peaking databases * peak CPU utilization per database
D. maximum number of concurrent sessions for all the databases
E. total number of databases * average CPU utilization per database

Correct answers: A, C, E

Explanation:

To properly size an Azure SQL Database elastic pool, several important metrics need to be considered to ensure that the resources are sufficient to handle the peak demand of all the databases while still remaining cost-effective. The following explanations highlight why the selected options are correct:

A. total size of all the databases:
This is an important metric because the total storage required for all the databases must be considered when sizing the elastic pool. Elastic pools allocate storage based on the total size of the databases within the pool, so understanding the storage requirements of all the databases helps ensure the pool can accommodate them without running into storage limitations.
C. number of concurrently peaking databases * peak CPU utilization per database:
This metric helps to assess the CPU resource requirements of the databases when they reach their peak load. If several databases in the pool experience high usage at the same time, you must ensure that the elastic pool has enough CPU capacity to handle the peak demands. This calculation ensures that the pool can scale to meet the workload during peak times.
E. total number of databases * average CPU utilization per database:
This metric helps to calculate the overall CPU demand for the entire pool. The elastic pool is designed to handle multiple databases, and understanding the average CPU utilization of each database allows you to estimate the total CPU resources needed for all databases combined. This helps in sizing the pool with enough CPU capacity to meet typical workload demands.

Now, let’s review the incorrect options:

B. geo-replication support:
Geo-replication is not directly related to sizing the elastic pool itself. While geo-replication can be configured for high availability and disaster recovery, it doesn’t directly impact how you size the pool. Sizing the pool is based on workload metrics such as CPU, memory, and storage needs rather than replication.
D. maximum number of concurrent sessions for all the databases:
While the number of concurrent sessions might be a factor in performance tuning, it is not a key metric for sizing the elastic pool. Azure SQL elastic pools are primarily sized based on resource consumption (like CPU, storage, and I/O), not the exact number of sessions. Thus, this metric is less relevant than CPU and storage-based considerations.

The most important factors in sizing an elastic pool are understanding the total storage requirements, CPU resource usage during peak demand, and average CPU utilization across all databases. Therefore, the correct choices are A, C, and E.

Question No 2:

You have an Azure SQL database that contains a table named factSales. FactSales contains the columns shown in the following table. FactSales has 6 billion rows and is loaded nightly by using a batch process. You must provide the greatest reduction in space for the database and maximize performance.

Which type of compression provides the greatest space reduction for the database?

A. page compression
B. row compression
C. columnstore compression
D. columnstore archival compression

Correct answer: D

Explanation:

When dealing with large amounts of data like 6 billion rows, it is important to choose the most efficient compression method to reduce storage space and optimize performance. Let’s break down each of the compression options:

Option A: Page compression
Page compression is a type of compression in which the database compresses data at the page level (each page is 8KB). It offers significant space savings over row compression by using techniques like prefix and dictionary compression. However, page compression is most effective for data that has patterns that can be compressed efficiently, and it works better in transactional workloads than in analytics workloads.

Option B: Row compression
Row compression works by removing redundant data at the row level, which reduces the space taken by each row. While it reduces storage needs compared to uncompressed data, it does not provide as much space savings as page compression or columnstore compression, especially when dealing with large datasets.

Option C: Columnstore compression
Columnstore compression is specifically designed for large datasets in analytics and reporting workloads. It stores data in a column-based format, which is highly efficient for analytical queries. It offers great compression ratios, especially for tables with many rows and columns, like the factSales table in this case. The data is stored in columnar format, which makes queries that involve aggregations, filtering, and summarizations much faster. Columnstore compression is highly effective for tables with many rows (like 6 billion), and it offers significant space savings while improving performance for analytic queries.

Option D: Columnstore archival compression
Columnstore archival compression is a more aggressive form of columnstore compression that provides the greatest space reduction, specifically designed for large historical datasets that are read infrequently. It compresses the data even more efficiently than standard columnstore compression by using advanced algorithms to reduce storage space. Since factSales is a large table that is loaded nightly with a batch process, and it likely contains historical data that doesn’t change frequently after it’s loaded, columnstore archival compression is ideal for this use case. This compression type maximizes space savings and minimizes storage costs while maintaining acceptable query performance for large datasets.

Summary of Compression Types:

Page compression and row compression are both useful, but they are more suited to transactional systems rather than large-scale analytic tables.
Columnstore compression is great for large datasets like factSales, but columnstore archival compression offers the greatest reduction in space when dealing with historical, rarely updated data.

Thus, columnstore archival compression is the best choice for maximizing space reduction and optimizing performance in an analytics-heavy workload. Therefore, the correct answer is D.

Question No 3:

You have a Microsoft SQL Server 2019 database named DB1 that uses the following database-level and instance-level features:

Clustered columnstore indexes
Automatic tuning
Change tracking
PolyBase

You plan to migrate DB1 to an Azure SQL database. What feature should be removed or replaced before DB1 can be migrated?

A. Clustered columnstore indexes

B. PolyBase

C. Change tracking

D. Automatic tuning

Answer: B

Explanation:

When migrating a Microsoft SQL Server 2019 database to Azure SQL Database, it's essential to consider which features are supported by both SQL Server 2019 and Azure SQL Database. While many features are supported, some are either partially supported, not supported at all, or require specific configurations.

Let's break down each feature:

A. Clustered columnstore indexes:

Clustered columnstore indexes are supported in Azure SQL Database. These indexes can help optimize data storage and query performance, especially for large data sets and analytic workloads.

Therefore, you do not need to remove or replace clustered columnstore indexes when migrating to Azure SQL Database.

B. PolyBase:

PolyBase is not supported in Azure SQL Database. PolyBase allows you to query data from external data sources such as Hadoop or Azure Blob Storage, but this feature is not available in the Azure SQL Database service.
In Azure SQL Database, you would need to replace PolyBase with alternative solutions such as Azure Data Factory or Azure Synapse Analytics (formerly SQL Data Warehouse) for integrating and querying external data sources.
Therefore, PolyBase is the feature that needs to be removed or replaced before migration.

C. Change tracking:

Change tracking is supported in Azure SQL Database. This feature is used for tracking changes to rows in tables, which can be useful for synchronization, ETL processes, and data auditing.
Therefore, no removal or replacement is needed for change tracking.

D. Automatic tuning:

Automatic tuning is supported in Azure SQL Database. This feature includes automatic index creation, automatic index drop, and automatic query plan correction to optimize the performance of your database.
Therefore, you do not need to remove or replace automatic tuning.

The only feature from the list that needs to be removed or replaced before migrating to Azure SQL Database is PolyBase, as it is not supported in Azure SQL Database. Therefore, the correct answer is B.

Question No 4:

You have a Microsoft SQL Server 2019 instance in an on-premises datacenter. The instance contains a 4-TB database named DB1. You plan to migrate DB1 to an Azure SQL Database managed instance.

What should you use to minimize downtime and data loss during the migration?

A. Distributed availability groups
B. Database mirroring
C. Always On Availability Group
D. Azure Database Migration Service

Correct Answer: D

Explanation:

When migrating a large database like DB1 (4 TB) to an Azure SQL Database managed instance, minimizing downtime and preventing data loss is crucial. Let's review each option and explain why Azure Database Migration Service (D) is the best option.

A. Distributed availability groups

Distributed Availability Groups are used in high-availability and disaster recovery scenarios, especially when you have an availability group across multiple regions or data centers. However, they are not specifically designed for migration tasks. While they can provide high availability during operational changes, they do not directly help in migrating a database from an on-premises SQL Server instance to an Azure SQL Database managed instance. Thus, this is not the ideal choice for this scenario.

B. Database mirroring

Database mirroring is a technology that provides high-availability and disaster recovery solutions by maintaining a redundant copy of the database. While database mirroring can allow you to keep a copy of the database in sync, it is deprecated in newer versions of SQL Server and does not provide a seamless method for migration to an Azure SQL Database managed instance. Furthermore, database mirroring requires setting up both a mirror and a principal server, which would complicate the migration process. As a result, this is not the best option for minimizing downtime and data loss during migration.

C. Always On Availability Group

Always On Availability Groups are a feature of SQL Server that provides high-availability and disaster recovery through replication. While this technology could be used to replicate data in an on-premises environment, it requires a SQL Server cluster to be configured, which can be complex. For migrating a large database to an Azure SQL Database managed instance, Always On Availability Groups would not directly address the needs of migrating to Azure, especially since it is more suited for on-premises or hybrid cloud environments rather than direct migration to Azure SQL Database.

D. Azure Database Migration Service

The Azure Database Migration Service (DMS) is a fully managed service designed specifically for migrating databases from on-premises environments to Azure. It supports migrations for various database types, including SQL Server to Azure SQL Database managed instances. This service minimizes downtime and ensures data consistency during migration. DMS provides continuous data synchronization during the migration process, which helps to reduce downtime and avoid data loss. It is the ideal tool for this use case, especially with large databases such as the 4-TB database in this scenario.

To minimize downtime and data loss during the migration of DB1 to an Azure SQL Database managed instance, the most effective and purpose-built tool is the Azure Database Migration Service (D).

Question No 5:

You are designing a streaming data solution that will ingest variable volumes of data. You need to ensure that you can change the partition count after creation. Which service should you use to ingest the data?

A. Azure Event Hubs Standard
B. Azure Stream Analytics
C. Azure Data Factory
D. Azure Event Hubs Dedicated

Correct Answer: A

Explanation:

The correct choice is Azure Event Hubs Standard.

Azure Event Hubs allows you to ingest streaming data at scale, and it supports dynamic partition scaling. You can adjust the partition count for an Event Hub at any time after its creation, which is key when you are dealing with variable volumes of data. This feature is essential for adapting to fluctuating data ingestion rates.

Why the other options are not suitable:

Option B (Azure Stream Analytics):
Azure Stream Analytics is a real-time analytics service that processes streaming data. However, it is not a data ingestion service in itself. While Stream Analytics can ingest data from Event Hubs, it does not have the ability to adjust partition counts after creation. Stream Analytics focuses on processing, analyzing, and transforming the data rather than on ingesting it.

Option C (Azure Data Factory):
Azure Data Factory is a data integration service used for orchestrating and automating the movement of data across different storage and compute services. While Data Factory supports batch and streaming data pipelines, it is not the best choice for managing the partition count of streaming data. It doesn't offer the same flexibility in terms of partition management as Event Hubs does.

Option D (Azure Event Hubs Dedicated):
Azure Event Hubs Dedicated is a premium version of Event Hubs designed for high-throughput, mission-critical workloads. However, the partition count in Event Hubs Dedicated is fixed and cannot be changed after creation. Therefore, it does not meet the requirement of changing the partition count after setup.

In summary, Azure Event Hubs Standard is the correct service because it allows the partition count to be adjusted post-creation, making it the ideal choice for a solution where the volume of streaming data may vary over time.

Question No 6:

Which Azure service is most appropriate for hosting a fully managed relational database with built-in high availability and automated backups?

A. Azure Blob Storage
B. Azure SQL Database
C. Azure Virtual Machines
D. Azure Cosmos DB

Correct Answer: B. Azure SQL Database

Explanation

Azure SQL Database is a fully managed Platform-as-a-Service (PaaS) offering by Microsoft designed to host relational databases in the cloud, eliminating the overhead of server management, patching, backups, and high availability configuration. It is the ideal choice for modern applications that require scalability, resilience, and minimal administrative effort. For candidates preparing for the DP-300 certification, it is crucial to understand that Azure SQL Database abstracts much of the underlying infrastructure while still providing the full power of the SQL Server engine.

One of the key advantages of Azure SQL Database is its built-in high availability, which ensures that your databases remain accessible even during hardware failures or maintenance events. Microsoft accomplishes this through replication across multiple nodes within a region and through features like Auto-Failover Groups for cross-region redundancy. Additionally, the service includes automatic backups that are retained for up to 35 days (for standard tiers), enabling point-in-time restore with ease.

Another vital feature is dynamic scalability, both in terms of compute and storage. Users can choose between different purchasing models like vCore or DTU-based pricing depending on workload needs, and can scale resources up or down without downtime. This flexibility makes Azure SQL Database well-suited for varying workloads, from small departmental applications to large enterprise-scale systems.

In contrast, Azure Virtual Machines can also run SQL Server, but they follow the Infrastructure-as-a-Service (IaaS) model, requiring administrators to manage OS updates, patches, and backups manually—making them less attractive for teams seeking a hands-off approach. Azure Blob Storage is a storage solution for unstructured data and not suitable for relational database workloads. Azure Cosmos DB, while powerful for global, distributed NoSQL workloads, is not designed for traditional relational database requirements.

From an administration perspective, Azure SQL Database provides intelligent performance tuning, threat detection, auditing, and automatic indexing, which reduce the need for constant manual intervention. It integrates seamlessly with Azure Monitor and Log Analytics for observability, and supports T-SQL, SSMS, and Azure Data Studio for familiar database management experiences.

Understanding when and why to choose Azure SQL Database is foundational to the DP-300 exam and is critical knowledge for any Azure database administrator.

Question No 7:

You are configuring automatic tuning for an Azure SQL Database. Which option can be automatically implemented by Azure SQL Database under automatic tuning?

A. Index fragmentation analysis
B. Query store cleanup
C. Automatic index creation and dropping
D. Backup encryption key rotation

Correct Answer: C. Automatic index creation and dropping

Explanation

One of the standout features of Azure SQL Database is Automatic Tuning, which is designed to enhance performance without manual intervention. It uses built-in intelligence to continuously monitor and analyze workloads, then applies proven performance tuning recommendations automatically. This feature is particularly helpful in dynamic cloud environments where workload patterns frequently change and manual tuning becomes inefficient or impractical.

A key capability within automatic tuning is automatic index management, which includes creating and dropping indexes based on usage patterns. Azure SQL analyzes query performance and index usage metrics through the Query Store, then determines which indexes are beneficial and which are redundant. For example, if the system identifies a missing index that could significantly reduce I/O for a high-frequency query, it will recommend and implement its creation. Conversely, if it notices that an existing index hasn't been used for a significant period and consumes storage or slows down write operations, it may decide to drop that index.

This process not only saves time but also ensures optimal performance and resource efficiency, especially in large-scale databases where manual index management becomes burdensome. Users can configure automatic tuning at both the server and database levels, with options to allow, enforce, or disable specific tuning actions like “CREATE INDEX,” “DROP INDEX,” and “FORCE LAST GOOD PLAN.”

It’s important to differentiate this from other performance tasks. For instance, index fragmentation analysis (Option A) is part of traditional maintenance jobs but not included in Azure’s automatic tuning. Query store cleanup (Option B) is managed through retention policies, not automatic tuning. Backup encryption key rotation (Option D) pertains to security and key management, not performance tuning.

Understanding this automation is essential for DP-300 candidates, as the exam focuses on administering relational databases in Azure. It tests knowledge on configuring resources, monitoring performance, and maintaining security and availability. Knowing how automatic tuning simplifies database management while maintaining high performance gives candidates an edge, both in the exam and real-world administration.

Question No 8:

You need to automate regular index maintenance tasks on your Azure SQL Database. Which tool should you use to implement this solution with minimal overhead?

A. Azure Logic Apps
B. SQL Server Agent
C. Elastic Jobs in Azure SQL
D. Azure DevOps Pipelines

Correct Answer: C. Elastic Jobs in Azure SQL

Explanation

In the context of Azure SQL Database, particularly in environments where traditional tools like SQL Server Agent are not available (especially in single databases or elastic pools), Elastic Jobs offer a powerful and flexible solution for automating routine administrative tasks—such as index maintenance, statistics updates, and custom T-SQL executions. Elastic Jobs are designed to run across multiple Azure SQL databases from a single job agent, making them ideal for large-scale environments or multi-tenant database architectures.

Elastic Jobs are hosted on a dedicated job agent database within Azure SQL Database. Administrators can define jobs, which consist of one or more job steps, and specify targets (databases or groups of databases). These jobs can be scheduled or executed on demand. For example, you can schedule a job that runs weekly to rebuild fragmented indexes and update outdated statistics, improving overall query performance and ensuring data optimization across your databases.

Unlike SQL Server Agent, which is available in SQL Server on Azure Virtual Machines or Azure SQL Managed Instance, it is not available in single or elastic pool deployments of Azure SQL Database. This makes Elastic Jobs the most appropriate native solution in those cases.

Azure Logic Apps is a workflow automation tool more suitable for integrating services like Microsoft 365, Dynamics, or external APIs. It is not designed for direct database maintenance tasks. Azure DevOps Pipelines, while powerful for CI/CD scenarios, are overkill for routine database maintenance and lack the native database task integration that Elastic Jobs provide.

By using Elastic Jobs, you maintain centralized control, reduce management overhead, and maintain performance through consistent index and statistics management. Furthermore, Elastic Jobs support robust logging and retry mechanisms, so DBAs can review job histories and troubleshoot any failures effectively.

For the DP-300 certification exam, understanding how to perform automated maintenance in Azure SQL—especially when traditional tools are unavailable—is essential. Elastic Jobs demonstrate Microsoft’s shift toward cloud-native automation solutions that scale with cloud-first architectures, making them a vital component of an Azure Database Administrator’s toolkit.