Microsoft DP-600 Exam Dumps & Practice Test Questions

Question 1:

Contoso’s Research division needs to implement version control for their semantic models and reports to support collaboration and track changes efficiently. 

What is the most appropriate approach to meet both data analytics and general operational requirements?

A. Save all semantic models and reports in Azure Data Lake Gen2 storage
B. Configure the Research workspaces to use GitHub for version control
C. Configure the Research division’s workspaces to integrate with Azure Repos
D. Use Microsoft OneDrive to store and share all models and reports

Correct Answer: C

Explanation:

To support version control and collaboration for the semantic models and reports created by Contoso’s Research division, integrating Azure Repos into their Fabric environment is the most effective and streamlined solution. Azure Repos is a core component of Azure DevOps and provides enterprise-grade source control, including branching, pull requests, change tracking, and rollback features. These capabilities are essential for collaborative development and governance of analytics assets such as Power BI reports and semantic models.

Azure Repos integrates directly with Microsoft Fabric, allowing semantic models and workspace configurations to be versioned without external tools or complex setup. This ensures minimal overhead while meeting Contoso's requirement for low-maintenance implementation. The version control system will allow different teams or contributors to work on isolated branches, test changes independently, and merge them through a controlled process.

Let's break down why the other options are less suitable:

• Option A (Azure Data Lake Gen2): While ideal for large-scale data storage, it does not offer native version control or branching features, which are essential for tracking changes and collaboration in analytics workflows.

• Option B (GitHub): Although GitHub supports version control, its integration with Microsoft Fabric is less seamless compared to Azure Repos. Using Azure Repos provides a more native and efficient development experience within the Microsoft ecosystem.

• Option D (OneDrive): OneDrive is a general-purpose file storage platform and lacks robust version control features such as branching, conflict resolution, and pull requests. It is not built for collaborative model development.

By using Azure Repos, Contoso ensures compatibility, structured version control, and ease of use—all critical for managing semantic models and analytic assets effectively in Fabric.

Question 2:

Contoso wants to organize the Research division’s workspaces to allow department-specific filtering in the OneLake data hub while keeping administrative overhead low and adhering to the principle of least privilege. 

Which solution should they implement?

A. Tag each workspace with metadata for departmental classification
B. Create a dedicated Fabric domain named "Research"
C. Combine all Research division workspaces into a single workspace collection
D. Assign custom security groups to each Research workspace

Correct Answer: B

Explanation:

To achieve effective department-level filtering in the OneLake data hub while minimizing complexity, the best solution is to create a Fabric domain called "Research" and assign all relevant workspaces—such as Productline1ws and Productline2ws—to this domain. Domains in Microsoft Fabric provide a logical grouping mechanism for organizing workspaces and data assets by business function, team, or department. They enable better discoverability, simplified access control, and consistent governance practices across distributed teams.

When a domain like "Research" is created, all included workspaces are automatically associated with that department. This allows users to filter assets in the OneLake data hub by domain, making it easy to locate and manage data assets that belong to the Research division. Importantly, this method adheres to the principle of least privilege by enabling administrators to apply domain-level access controls, ensuring users only have access to the data relevant to their department.

Let’s consider why the other options are less suitable:

• Option A (Metadata tags): While metadata tagging can help with classification and discovery, it does not support built-in filtering or access control in OneLake in the same streamlined way as Fabric domains.

• Option C (Workspace collections): Currently, Fabric does not offer workspace collections as a formal grouping mechanism, so this approach is not supported.

• Option D (Security groups): Although security groups help manage permissions, they do not provide a mechanism for grouping or filtering workspaces by department within the OneLake UI.

By using a Fabric domain, Contoso simplifies administrative management and enables intuitive filtering and access control, making this solution both efficient and scalable for enterprise environments.

Question 3:

Contoso needs a strategy to logically organize the Research division’s workspaces to meet their business and technical requirements.

What should be included in your recommendation?

Answer:

• Use Fabric Workspaces grouped by product line

• Enable OneLake Data Hub filtering by department

• Integrate version control with branching support (e.g., Azure Repos)

• Utilize on-demand capacity with per-minute billing

Explanation:

Contoso’s goal is to organize the Research division’s workspaces in a way that supports efficient data management, version control, and cost-effective resource allocation. The solution involves several key components.

First, Fabric Workspaces should be used to logically group the workspaces according to product lines within the Research division. This grouping aligns with Contoso’s organizational structure, enabling easier management and navigation. It also ensures that data relevant to each product line is kept isolated but accessible under the same umbrella, facilitating collaboration without overlap.

Next, OneLake Data Hub filtering is crucial. By logically grouping workspaces under a department-based hierarchy, filtering by the Research division becomes straightforward. This helps users quickly access data related to their specific domain, improving efficiency and compliance with organizational data governance policies.

Version control is a critical part of managing semantic models and reports, especially in collaborative environments. Integrating version control with branching support via tools like Azure Repos allows multiple team members to work simultaneously, track changes, and roll back if necessary. This structured approach to versioning is essential for maintaining consistency and avoiding conflicts across the Research division’s analytics artifacts.

Finally, to optimize costs and ensure scalability, Contoso should leverage on-demand capacity with per-minute billing. This setup allows the Research division to pay only for compute resources when they’re actually used, which is ideal for fluctuating workloads common in analytics tasks. It balances performance needs with budget considerations.

In summary, grouping workspaces by product line, enabling efficient data filtering, implementing robust version control, and using on-demand compute capacity collectively satisfy Contoso’s requirements for managing the Research division’s data assets effectively and efficiently.

Question 4:

Contoso is refreshing the Orders table in the Online Sales department. The goal is to minimize the number of rows added during the refresh, while also meeting semantic model requirements.

Which solution should be implemented?

A. Use an Azure Data Factory pipeline with a stored procedure to get the maximum OrderID from the destination lakehouse.
B. Use an Azure Data Factory pipeline with a stored procedure to get the minimum OrderID from the destination lakehouse.
C. Use an Azure Data Factory pipeline with a dataflow to get the minimum OrderID from the destination lakehouse.
D. Use an Azure Data Factory pipeline with a dataflow to get the maximum OrderID from the destination lakehouse.

Answer: A

Explanation:

To efficiently refresh the Orders table with minimal added rows, Contoso needs an incremental data loading strategy that identifies only new or updated records since the last refresh. The maximum OrderID in the destination lakehouse acts as a marker for the most recently processed order, which makes it possible to load only orders with an OrderID greater than this maximum, thus minimizing data redundancy.

Using an Azure Data Factory (ADF) pipeline that executes a stored procedure to retrieve the maximum OrderID is the most efficient way to implement this. Stored procedures are optimized to quickly query the maximum value from a database, which keeps the pipeline lightweight and focused.

The alternative options are less effective:

• Retrieving the minimum OrderID (options B and C) is counterproductive because the minimum value reflects the oldest order, which does not help in incremental refresh. It would cause unnecessary processing of already ingested data.

• Using a dataflow to retrieve maximum or minimum values (options C and D) adds unnecessary complexity since dataflows are designed for transformations rather than simple value retrieval. This could increase processing time and resource consumption.

By focusing on the maximum OrderID, the pipeline can pull only new rows, optimizing the refresh process to be both fast and cost-efficient. This approach respects semantic model requirements by ensuring only relevant, new data is added during each refresh, reducing the risk of duplicated or excessive rows, which can degrade query performance and increase storage costs.

Therefore, Option A offers the best balance of performance, efficiency, and simplicity for this scenario.

Question 5:

Contoso has data for Productline1 stored in Lakehouse1 within a Fabric workspace for the Research division. You need to access this data via Fabric notebooks.

Which syntax should you use to load the Research division’s Productline1 data?

A.spark.read.format("delta").load("Tables/productline1/ResearchProduct")
B. spark.sql("SELECT * FROM Lakehouse1.ResearchProduct")
C. external_table('Tables/ResearchProduct')
D. external_table(ResearchProduct)

Answer: A

Explanation:

When accessing data stored in a Fabric Lakehouse using notebooks, it’s important to use the appropriate syntax and methods that align with the underlying data format and compute engine. Contoso’s Productline1 data for the Research division is stored in Lakehouse1, which uses the Delta Lake format — a transactional storage layer that provides ACID guarantees and is optimized for big data scenarios.

The correct way to load this data in a Fabric notebook (which leverages Apache Spark) is by using the Spark DataFrame API with the Delta format reader:

This syntax explicitly tells Spark to read the data in Delta Lake format from the specified path. This method supports efficient data loading, transaction consistency, and time travel capabilities that Delta Lake provides.

Other options have limitations or inaccuracies:

• Option B uses Spark SQL syntax to query the data, but Fabric notebooks generally prefer the DataFrame API for direct data loading from storage paths, not querying through SQL namespaces without proper registration.

• Options C and D use external_table(), which typically registers or queries external tables linked via catalog metadata. However, for direct data access in notebooks, especially when reading raw Delta Lake files, this is less appropriate.

Therefore, Option A is the most suitable syntax for directly accessing Productline1 data in Lakehouse1 within Fabric notebooks. It leverages Delta Lake’s strengths, aligns with Spark’s best practices, and meets Contoso’s requirements for seamless, efficient data access.

Question 6:

You are building a machine learning model in Azure Machine Learning and need to find the optimal hyperparameters for a complex classification algorithm. You want to automate this process and distribute the search across a compute cluster. 

Which of the following approaches should you use?

A. Configure an Azure ML HyperDrive run using random sampling on the parameter space and target the compute cluster.
B. Use Automated ML to automatically select model type and hyperparameters without specifying a compute target.
C. Manually loop through hyperparameter combinations in a Python script running on your local machine.
D. Execute a grid search inside a single Azure ML notebook using the default CPU compute instance.

Correct Answer: A

Explanation:

When you need to tune hyperparameters for a complex model at scale in Azure Machine Learning, HyperDrive is the recommended solution. HyperDrive orchestrates distributed hyperparameter search across multiple runs on a specified compute target—such as an Azure ML compute cluster—allowing you to explore many configurations in parallel, accelerate experimentation, and find the best-performing set of parameters efficiently.

Here’s why Option A is most appropriate:

1. Scalability and Parallelism
   HyperDrive can dispatch dozens or hundreds of training runs in parallel to nodes within your compute cluster. By specifying a sampling algorithm—such as random sampling—you cover the hyperparameter space more broadly and probabilistically, often with fewer runs than a full grid search. The compute cluster ensures each run has dedicated resources, reducing total tuning time.

2. Built-in Early Termination
   HyperDrive supports early-termination policies (e.g., median stopping) that automatically halt poorly performing runs, reallocating resources to promising configurations. This further improves efficiency by preventing time wasted on bad trials.

3. Integration with Azure ML
   You define a HyperDriveConfig object in your experiment pipeline that references your training script, parameter sampler, and compute target. Azure ML handles job scheduling, parameter injection, run tracking, and metrics aggregation in the studio UI or via the Python SDK.

The other options fall short for several reasons:

• Option B (Automated ML): While AutoML is great for model selection and hyperparameter tuning, it abstracts away the algorithm choice and seldom allows fine-grained control over specific hyperparameter ranges or custom metrics. For a known algorithm where you want full control, HyperDrive is more suitable.

• Option C (Manual loop on local machine): Running loops locally is slow, error-prone, and fails to leverage Azure’s scalable compute resources. It also lacks built-in logging, parallelism, and early stopping features.

• Option D (Grid search on CPU instance): Performing a grid search inside a single notebook on your CPU compute instance is limited by single-node performance, which drastically increases tuning time. Moreover, grid search is often inefficient compared to random or Bayesian sampling, especially in high-dimensional spaces.

In summary, HyperDrive with random sampling distributed across an Azure ML compute cluster provides a powerful, efficient, and fully managed hyperparameter tuning solution that accelerates model development at scale.

Question 7:

After training a forecasting model in Azure Machine Learning, you need to deploy it as a real-time inference endpoint that can handle production-level traffic with low latency. Which deployment target should you choose?

A. Azure Container Instances (ACI)
B. Azure Kubernetes Service (AKS)
C. Azure Functions
D. Azure Batch inference

Correct Answer: B

Explanation:

Deploying a machine learning model for production-grade, low-latency predictions requires a robust, scalable serving infrastructure. Azure Kubernetes Service (AKS) is the optimal choice when you expect variable or high traffic volumes and need high availability, auto-scaling, and strict latency requirements.

Here’s why Option B (AKS) is the best fit:

1. Scalability
   AKS allows you to scale out (add nodes) and scale up (larger node sizes) based on CPU, memory, or custom metrics. You can configure horizontal pod autoscaling to match incoming request volume, ensuring your endpoint maintains consistent low-latency response times under load.

2. High Availability
   By running multiple replicas of your model container across nodes, AKS ensures no single point of failure. Kubernetes automatically reschedules pods if a node fails or a pod crashes, preserving endpoint availability.

3. Custom Networking and Security
   AKS supports Virtual Network integration, private endpoints, and custom ingress controllers. This enables secure deployment within your Azure Virtual Network, fine-grained traffic control, and advanced TLS termination setups—requirements common in enterprise production environments.

4. Monitoring and Logging
   AKS integrates with Azure Monitor and Log Analytics, allowing you to collect telemetry from your prediction service—metrics like request count, latency, and resource utilization. This observability is essential to maintain SLA commitments and troubleshoot performance issues.

Let’s compare the other options:

• Option A (ACI): While ACI is quick to deploy and suitable for development or low-scale workloads, it has no built-in autoscaling and may struggle with high-volume, low-latency requirements. ACI also does not offer the resilience features of Kubernetes.

• Option C (Azure Functions): Serverless functions can host simple model code but are best for event-driven or lightweight workloads. Cold start latency and limited execution time make them less suitable for consistently low-latency, high-throughput model serving.

• Option D (Azure Batch inference): Batch inference handles large datasets in offline mode and is not designed for real-time API calls. While ideal for periodic, bulk scoring jobs, it fails to meet the low-latency requirement for interactive applications.

In conclusion, AKS provides the performance, scalability, and enterprise features required for deploying a real-time, production-ready ML inference endpoint in Azure Machine Learning.

Question 8:

Litware, Inc. plans to implement a proof of concept (PoC) in their AnalyticsPOC workspace using Microsoft Fabric. They require a data store that supports T-SQL or Python for querying, can handle semi-structured and unstructured data, supports row-level security (RLS), and facilitates data transformations. 

Which type of data store should Litware select for their AnalyticsPOC workspace to best meet these needs?

A. Data lake 

B. Warehouse
C. Lakehouse
D. External Hive metastore

Explanation:

To determine the most suitable data store type for Litware’s proof of concept (PoC), it’s important to consider their technical and security requirements in detail.

First, Litware’s data is diverse—it includes structured, semi-structured, and unstructured data. A traditional data warehouse (Option B) is optimized for structured data but struggles with semi-structured or unstructured data. Conversely, a data lake (Option A) can store large volumes of raw data in various formats but lacks the management and performance capabilities needed for analytical queries and enforcement of fine-grained security like row-level security (RLS).

The lakehouse architecture (Option C) bridges this gap by combining the strengths of both data lakes and data warehouses. It supports the flexibility of raw data storage with the robust query performance and schema enforcement of warehouses. Importantly, lakehouses provide native support for T-SQL and Python, enabling data engineers and scientists to use familiar tools for querying and transformation.

Regarding row-level security, lakehouses can implement RLS policies to ensure users only access data they’re authorized to see. This aligns with Litware’s security model, which requires least privilege access for different teams.

Additionally, lakehouses support advanced data transformation workflows using tools like Apache Spark and Delta Lake, facilitating cleansing, merging, and dimensional modeling essential for the PoC.

Lastly, lakehouses offer scalability and cost-efficiency, important for handling Litware’s large data volumes during their PoC phase without incurring excessive costs.

An external Hive metastore (Option D) serves as a metadata catalog but is not a standalone data store and does not fulfill the querying or security requirements directly.

Therefore, given the combination of data diversity, security needs, transformation capabilities, and tool compatibility, a lakehouse is the best fit for Litware’s AnalyticsPOC workspace.

Question 9:

You need to design a high-availability solution for an Azure SQL Database that will ensure automatic failover across regions in case of a regional outage. Which feature should you configure?

A. Automated backups with geo-redundant storage
B. SQL Database failover groups
C. Elastic pool replication
D. Active Geo-Replication

Answer:  B

Explanation:

Designing a resilient, region-spanning high-availability (HA) solution for Azure SQL Database involves more than simply backing up data; it requires an orchestrated failover mechanism that maintains application connectivity, minimizes data loss, and simplifies management. Two primary replication technologies are available in Azure SQL: Active Geo-Replication and Failover Groups. While both enable cross-region disaster recovery, Failover Groups add automation and policy configuration that make them the recommended choice for fully managed HA across regions.

Active Geo-Replication (Option D) allows you to create up to four readable secondary databases in the same or different Azure regions. You must manually fail over to a secondary instance when the primary becomes unavailable, and you are responsible for updating connection strings or Application Configuration to point to the new primary. Although powerful for read-scale and disaster recovery, it lacks built-in automated failover capabilities and centralized management policies.

In contrast, SQL Database Failover Groups (Option B) are designed to simplify cross-region HA by grouping multiple databases—sometimes entire elastic pools—into a single entity that fails over together. Once configured, the failover group creates a geo-replicated secondary in a paired region, monitors the primary continuously, and automatically initiates failover if the primary becomes unavailable. You define failover policies that specify grace periods to allow transient failures to resolve before triggering a failover. Applications connect via a single listener endpoint (a DNS record) that transparently redirects clients to the current primary after failover, removing the need for manual connection-string updates in your application code.

Let’s briefly consider the other options:

• Option A (Automated backups with geo-redundant storage): While you should always enable geo-redundant backups to protect against data loss, backups alone are intended for point-in-time restores and cannot provide seamless automatic failover in real time. They are a critical part of any recovery strategy but not sufficient for near-zero downtime.

• Option C (Elastic pool replication): Elastic pools allow multiple databases to share resources, but they do not inherently provide cross-region HA or automatic failover. Replication must be configured at the individual database or failover-group level.

By choosing Failover Groups, you combine the capabilities of geo-replication with built-in automation, a single listener endpoint, configurable failover policies, and group-level management. This meets the requirement for a turnkey, region-to-region HA solution with minimal application changes and reduced administrative overhead.

Question 10:

You must restrict sensitive data exposure in an Azure SQL Database by preventing unauthorized users from viewing certain columns (for example, Social Security numbers), while still allowing legitimate applications to retrieve masked values. 

Which pair of features should you implement?

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

Answer: C

Explanation:

Protecting sensitive data in Azure SQL Database requires a layered approach that addresses data at rest, in transit, and in use. Two complementary features—Always Encrypted and Dynamic Data Masking—work together to reduce the risk of unauthorized data exposure while maintaining application functionality.

Always Encrypted ensures that sensitive columns are encrypted on the client side before being sent to the database and remain encrypted at rest. Encryption keys are never revealed to the database engine, ensuring that administrators and attackers who gain unauthorized access to the database cannot decrypt the data. Applications that hold the encryption keys can perform queries and retrieve decrypted data, but any user or service without key access sees only ciphertext. This fulfills the requirement that only authorized applications should view clear text data.

Dynamic Data Masking (DDM) hides sensitive column values in query results for non-privileged users by applying transformation rules on the fly. For example, a default masking rule might display a Social Security number as “XXX-XX-1234” to unauthorized users. DDM simplifies security by applying masking without changing application code, and legitimate applications or roles granted the “UNMASK” permission can bypass masking and retrieve full data. DDM is implemented at the database engine level, making it easy to deploy across multiple columns with predefined masking functions (default, email, custom string, random).

By combining Always Encrypted with Dynamic Data Masking, you achieve the following:

• Data at rest and in use is encrypted and protected from unauthorized administrative or breach scenarios.

• Query-level masking ensures that casual or unauthorized query attempts (e.g., via ad hoc tools or user roles) only see masked values.

• Client-side decryption by permitted applications ensures legitimate data retrieval without exposing encryption keys within the database.

The other feature pairings do not meet both requirements:

• Option A (Always Encrypted + Transparent Data Encryption): TDE encrypts data at rest only and does not prevent unauthorized query-time access; it doesn’t mask data for casual queries.

• Option B (Dynamic Data Masking + Row-Level Security): Row-Level Security controls which rows are visible to users, not column values; if you need to hide sensitive columns while showing rows, it is insufficient alone.

• Option D (Transparent Data Encryption + Row-Level Security): Neither feature masks column data in query results; they focus on at-rest encryption and row filtering.

Therefore, Dynamic Data Masking and Always Encrypted together create a robust solution to prevent unauthorized column value viewing while allowing legitimate application access to decrypted data.