Microsoft DP-600 Implementing Analytics Solutions Using Microsoft Fabric Exam Dumps and Practice Test Questions Set 6  Q101-120

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Question 101

You are designing an Azure Synapse Analytics solution with a large fact table and multiple small dimension tables. You need to optimize join performance and minimize data movement. Which strategy should you implement?

A) Hash-distribute the fact table on foreign keys and replicate small dimension tables

B) Round-robin distribute all tables

C) Replicate the fact table and hash-distribute dimension tables

D) Leave all tables unpartitioned

Answer: A) Hash-distribute the fact table on foreign keys and replicate small dimension tables

Explanation:

Hash distribution for large fact tables ensures that rows with the same foreign key are colocated with matching dimension rows on the same compute node. This reduces inter-node data movement during join operations and allows parallel execution across nodes, optimizing resource utilization. Replicating small dimension tables ensures that every compute node has a complete copy, eliminating unnecessary shuffling during joins. Round-robin distribution evenly distributes rows across nodes but does not align join keys, increasing network traffic and reducing performance. Replicating large fact tables is impractical due to storage and network requirements. Hash-distributing dimension tables is unnecessary for small tables since replication is more efficient. Leaving tables unpartitioned can create uneven workloads and slower query performance. Combining hash-distributed fact tables with replicated small dimensions is a best practice in distributed data warehousing, providing scalable, high-performance query execution and maintainability. It reduces latency, minimizes computational overhead, and supports large-scale analytics in Azure Synapse Analytics. This approach ensures efficient joins, better parallelism, and predictable performance across diverse workloads.

Hash-distributing the fact table on foreign keys and replicating small dimension tables is a best practice strategy for optimizing query performance in a distributed data warehouse environment such as Azure Synapse Analytics or similar MPP (Massively Parallel Processing) systems. Hash distribution spreads rows across compute nodes based on a hash of a specified column, often a foreign key. By hash-distributing the fact table on foreign keys that are frequently used in joins with dimension tables, data with the same key is located on the same compute node as the matching dimension row. This minimizes data movement, also known as shuffling, during query execution, which significantly reduces query latency and improves overall performance. Replicating small dimension tables on all nodes further eliminates the need for shuffling when joining with the fact table. Each compute node has a local copy of the dimension table, allowing joins to occur locally and efficiently. This combination ensures that queries involving joins between the fact and dimension tables execute faster, scaling well even as data volumes grow.

Round-robin distributing all tables is a simple and straightforward approach where rows are distributed evenly across nodes without considering any column values. While this can balance storage and compute load across nodes, it does not optimize for joins. Queries involving joins between a fact and dimension table often require moving large amounts of data between nodes if the corresponding keys do not reside on the same node. This data movement increases network overhead, adds latency, and reduces query performance, especially for large datasets. Round-robin distribution may be suitable for staging tables or temporary workloads but is suboptimal for production fact and dimension tables that are frequently joined.

Replicating the fact table and hash-distributing dimension tables is inefficient in most scenarios. Fact tables are typically very large, containing millions or billions of rows, and replicating them on every compute node would require excessive storage and memory. The replication process would also consume significant resources and could negatively impact load times and overall system performance. While hash-distributing dimension tables can improve some joins, the high cost of replicating the large fact table outweighs any benefits. This approach is impractical for large-scale production environments and is rarely recommended in distributed data warehouse design.

Leaving all tables unpartitioned results in all data being stored on a single node, which creates significant performance bottlenecks. Queries must process all data on one node, severely limiting parallelism and preventing the system from leveraging its distributed architecture. Large tables, especially fact tables with high row counts, would lead to slow query performance, long load times, and potential resource exhaustion. Without distribution or partitioning strategies, the system cannot scale effectively as the dataset grows, making it unsuitable for enterprise-grade analytics workloads.

Hash-distributing the fact table on foreign keys and replicating small dimension tables balances performance, storage, and scalability. The fact table is spread across nodes according to key values, minimizing inter-node data movement during joins, while small dimension tables are replicated to avoid shuffling altogether. This design enables high-performance query execution, efficient resource utilization, and scalability as data volumes increase. It aligns with best practices for distributed data warehouse optimization, ensuring that complex analytical queries execute quickly while maintaining the integrity and consistency of the data. Compared to round-robin distribution, replication of fact tables, or leaving tables unpartitioned, this approach provides the most practical and high-performance solution for joining large fact tables with smaller dimension tables in a massively parallel environment.

Question 102

You are building a predictive maintenance solution using Azure ML with streaming IoT data. The model must provide immediate alerts for potential equipment failures. Which deployment method should you choose?

A) Azure ML Real-Time Endpoint

B) Batch Endpoint

C) Azure Data Factory Pipeline

D) Power BI Dashboard

Answer: A) Azure ML Real-Time Endpoint

Explanation:

Azure ML Real-Time Endpoints deliver low-latency predictions, making them suitable for scenarios that require immediate response, such as predictive maintenance. Streaming IoT data can be ingested continuously via REST APIs, and the model provides predictions instantly, enabling automated alerts and quick operational response. Batch Endpoints are designed for periodic processing of large datasets and are not suitable for real-time decision-making. Azure Data Factory pipelines orchestrate ETL workflows and batch transformations but cannot perform real-time predictions. Power BI dashboards are visualization tools and cannot execute models for live scoring. Real-Time Endpoints also support autoscaling, monitoring, logging, and version control, providing robust production deployment. This deployment method allows proactive maintenance, reduces equipment downtime, and ensures timely intervention. Integration with Azure IoT Hub or Event Hub allows seamless streaming data ingestion. Real-Time Endpoints provide responsiveness, scalability, and reliability, which are essential for mission-critical predictive maintenance applications that require immediate insights and actions.

Azure ML Real-Time Endpoint is a service that allows trained machine learning models to be deployed as RESTful APIs for immediate predictions. Once a model is registered and deployed to a real-time endpoint, it can receive input data and return predictions instantly, which is critical for scenarios requiring low-latency responses. Real-time endpoints are ideal for use cases such as fraud detection, personalized recommendations, customer support chatbots, predictive maintenance, and dynamic pricing. The real-time nature of these endpoints ensures that decisions can be made immediately based on the latest available data. They also support autoscaling, which allows the system to handle varying volumes of requests efficiently without affecting performance. Additionally, logging and monitoring capabilities provide insights into request patterns, latency, and errors, enabling administrators to maintain high availability and optimize model performance.

Batch endpoints process data in bulk, handling large datasets asynchronously. They are suitable for scoring historical data, running periodic analytics, or generating reports. Batch processing is efficient for large volumes of data but introduces latency between data input and prediction output. It is not suitable for scenarios where immediate responses are required, as users or applications must wait until the batch job completes. Using batch endpoints for real-time decision-making would result in delayed responses and poor user experience, making them unsuitable for operational applications requiring instant predictions.

Azure Data Factory pipelines orchestrate data workflows, integrating and transforming data across multiple sources and destinations. While Data Factory can incorporate machine learning models as part of its workflow, it is typically designed for batch or scheduled processing. Pipelines do not provide the low-latency, on-demand predictions required for real-time applications. Using Data Factory to serve predictions would involve additional complexity, and the resulting latency would make it unsuitable for scenarios needing immediate feedback. It is more appropriate for ETL workflows, data preparation, and integration rather than direct model inference for operational applications.

Power BI dashboards are primarily visualization tools that allow stakeholders to explore data, track KPIs, and display insights from analytical models. Dashboards are reactive, relying on precomputed data or imported predictions, and do not execute model inference themselves. While they can display results from real-time endpoints or batch processes, they cannot provide immediate predictions directly. Dashboards are best used for monitoring, reporting, and analysis rather than serving predictions to applications that require instant decision-making.

In comparison, Azure ML Real-Time Endpoint is the correct solution for applications that require immediate, on-demand predictions. Unlike batch endpoints, it provides low-latency, synchronous inference. Unlike Azure Data Factory pipelines, it does not depend on scheduled batch processing and eliminates the complexity associated with orchestrating real-time workflows. Unlike Power BI dashboards, it generates predictions rather than merely displaying them. Real-time endpoints also support model versioning, scaling, authentication, and monitoring, ensuring that deployed models remain performant, secure, and maintainable.

Overall, for operational scenarios requiring instant model predictions, Azure ML Real-Time Endpoint is the most appropriate choice. It delivers low-latency, scalable, and secure inference, enabling applications to make data-driven decisions in real time. This ensures timely insights, improves user experience, and supports intelligent, automated processes in production environments, making it the preferred solution over batch endpoints, data pipelines, or dashboards for real-time prediction needs.

Question 103

You are designing a Power BI dataset that includes multiple large tables. Users frequently perform aggregations and drill-down analyses. Which approach optimizes report performance?

A) Create aggregation tables to precompute frequently used metrics

B) Enable DirectQuery for all tables

C) Remove calculated columns

D) Split the dataset into multiple PBIX files

Answer: A) Create aggregation tables to precompute frequently used metrics

Explanation:

Aggregation tables precompute frequently used metrics and summaries, allowing queries to return results quickly without scanning entire datasets. This improves performance for aggregation and drill-down operations, reducing latency and enhancing user experience. DirectQuery avoids importing data but may degrade performance because each visual sends live queries to the source system, which may not be optimized for analytical workloads. Removing calculated columns slightly reduces memory usage but does not address performance bottlenecks caused by scanning large datasets. Splitting datasets into multiple PBIX files increases administrative complexity and may create redundancy or inconsistencies. Aggregation tables provide a scalable and maintainable solution that balances performance and flexibility. Users can access precomputed metrics quickly while retaining the ability to drill into detailed data when necessary. Incremental refresh can further improve efficiency by updating only changed data. This approach follows best practices for high-performance Power BI reporting, ensuring fast response times, efficient resource usage, and scalability for complex analytics scenarios.

Question 104

You are implementing incremental data ingestion from on-premises SQL Server to Azure Data Lake using Azure Data Factory. The source tables include a last-modified timestamp column. Which method ensures efficient processing?

A) Use a watermark column to load only new or updated rows

B) Copy the entire table daily

C) Use full overwrite of existing files

D) Append all rows without considering timestamps

Answer: A) Use a watermark column to load only new or updated rows

Explanation:

A watermark column tracks the last processed timestamp or row, enabling incremental loading of only new or modified records. This approach reduces network traffic, storage consumption, and processing time, making ETL operations more efficient. Copying the entire table daily consumes excessive resources, increases runtime, and can result in redundant data. Full overwrites of existing files require additional storage and may introduce downtime or errors. Appending all rows without considering timestamps can cause duplicates and inconsistencies in downstream systems. Watermark-based incremental loading ensures timely and accurate ingestion while minimizing overhead. It simplifies monitoring and error handling because only relevant data is processed in each pipeline run. This method is considered best practice for large or frequently updated datasets, ensuring Azure Data Lake storage remains synchronized with on-premises sources. It also supports incremental refresh in downstream analytics, optimizing performance, maintainability, and reliability of the data pipeline.

Question 105

You are designing column-level security in Azure SQL Database. Users require access to most columns but must not see sensitive PII data. Which feature is most appropriate?

A) Dynamic Data Masking

B) Row-Level Security

C) Transparent Data Encryption

D) Always Encrypted

Answer: A) Dynamic Data Masking

Explanation:

Dynamic Data Masking (DDM) hides sensitive column values in query results for non-privileged users while allowing access to non-sensitive data. This ensures reporting and analytics users can interact with datasets without exposing confidential information. Row-Level Security controls access at the row level and cannot restrict access to specific columns. Transparent Data Encryption secures data at rest but does not prevent sensitive information from appearing in query results. Always Encrypted provides strong end-to-end encryption but requires client-side decryption, which can complicate analytics and reporting workflows. DDM is simple to implement, requires no application changes, and supports multiple masking patterns, including partial, randomized, and format-based masking. This approach balances usability and security, ensuring compliance with privacy regulations while maintaining access to non-sensitive data. DDM is widely recommended for scenarios where most data must remain accessible but sensitive columns need to be concealed, providing a maintainable solution for column-level security.

Dynamic Data Masking (DDM) is a database security feature that helps protect sensitive data by masking it in query results for non-privileged users while leaving the underlying data intact. This allows organizations to reduce the risk of accidental or unauthorized exposure of sensitive information such as social security numbers, credit card numbers, email addresses, or personal identifiers without modifying the application or database schema. Dynamic Data Masking operates in real time, meaning queries automatically return masked data based on rules defined at the database level. For instance, a phone number may appear as “XXX-XXX-1234” to unauthorized users, while full access remains available to authorized roles. DDM simplifies implementation because it does not require changes to existing queries or application code, and it supports role-based policies that specify which users can view the original data and which users see masked values. This makes it highly effective for enforcing data privacy and compliance requirements.

Row-Level Security (RLS) restricts access to rows in a table based on the identity of the user querying the data. This is particularly useful in multi-tenant applications or scenarios where users should only access data specific to their department, region, or role. However, RLS does not mask column-level sensitive information. Users with access to a row can see all column values, including confidential data. RLS focuses on controlling which rows a user can see, but it does not provide protection for the values within those rows. As a result, sensitive information can still be exposed if users have access to the relevant rows. Therefore, while RLS is valuable for data segmentation, it cannot replace the column-level obfuscation provided by Dynamic Data Masking.

Transparent Data Encryption (TDE) protects data at rest by encrypting the database files and backups. It ensures that if the physical storage media or backups are stolen, the data remains unreadable without proper credentials. TDE is critical for protecting stored data from unauthorized access at the file level, but it does not prevent sensitive information from being exposed to users who query the database legitimately. TDE secures the data in storage but does not mask it during normal application operations, so it does not solve the problem of limiting data visibility for specific users.

Always Encrypted provides end-to-end encryption for sensitive data both at rest and in transit. Data is encrypted in the database and can only be decrypted by applications or users with the proper encryption keys. This provides strong security but often requires changes in applications to handle encrypted columns correctly. There are also limitations on querying encrypted columns, such as restrictions on certain functions and operations, which can affect reporting and analytics. Dynamic Data Masking, by contrast, allows sensitive information to be obscured in query results without requiring application changes or complex encryption management, making it simpler and more flexible in scenarios where authorized users still need access to unmasked data.

Dynamic Data Masking is the most appropriate choice when the goal is to limit exposure of sensitive column values in real time while maintaining usability for authorized users. It masks data dynamically during query execution, reducing the risk of accidental disclosure. Unlike RLS, which controls access at the row level, DDM specifically masks column data. Unlike TDE, it secures data during use rather than just at rest. Unlike Always Encrypted, it does not require complex application modifications and allows for easier reporting and analytics. Dynamic Data Masking therefore provides an effective, practical, and scalable solution for protecting sensitive data in databases while supporting compliance and operational requirements.

Question 106

You are designing an Azure Synapse Analytics solution with a large fact table and multiple small dimension tables. You need to optimize query performance for join operations. Which strategy should you implement?

A) Hash-distribute the fact table on foreign keys and replicate small dimension tables

B) Round-robin distribute all tables

C) Replicate the fact table and hash-distribute dimension tables

D) Leave all tables unpartitioned

Answer: A) Hash-distribute the fact table on foreign keys and replicate small dimension tables

Explanation:

Hash distribution ensures that rows in a large fact table sharing the same foreign key are colocated with matching dimension rows on the same compute node. This minimizes inter-node data movement during joins, improving query performance and resource utilization. Replicating small dimension tables ensures that each node has a full copy, removing the need for data shuffling during joins. Round-robin distribution spreads rows evenly but does not align join keys, causing excessive network traffic and slower query execution. Replicating large fact tables is impractical due to storage and network requirements, while hash-distributing small dimension tables is unnecessary since replication is more effective for small tables. Leaving tables unpartitioned can result in uneven workloads, slower queries, and reduced performance. Combining hash-distributed fact tables with replicated small dimensions is considered best practice for distributed data warehouses, providing scalable, high-performance query execution, reduced computational overhead, and maintainable architecture. This strategy also allows for efficient parallel processing, predictable performance, and better resource utilization, especially for large-scale analytical workloads in Azure Synapse Analytics.

Question 107

You are building a predictive maintenance solution using Azure ML with streaming IoT data. The model must generate immediate alerts for potential equipment failures. Which deployment method should you use?

A) Azure ML Real-Time Endpoint

B) Batch Endpoint

C) Azure Data Factory Pipeline

D) Power BI Dashboard

Answer: A) Azure ML Real-Time Endpoint

Explanation:

Azure ML Real-Time Endpoints deliver low-latency predictions, which are crucial for real-time scenarios like predictive maintenance. Streaming IoT data can be continuously ingested via REST APIs, and the model provides instant predictions, enabling timely alerts and automated actions. Batch Endpoints are designed for periodic processing of large datasets and cannot provide immediate feedback. Azure Data Factory pipelines orchestrate ETL and batch transformations, but they are not suitable for real-time scoring. Power BI dashboards are visualization tools and cannot execute predictive models in real time. Real-Time Endpoints support autoscaling, logging, monitoring, and version control, ensuring robust and maintainable production deployments. This deployment enables proactive maintenance, reduces equipment downtime, and allows for timely intervention. Integration with Azure IoT Hub or Event Hub allows seamless ingestion of streaming data, providing a complete end-to-end solution. Real-Time Endpoints provide responsiveness, scalability, and reliability, ensuring mission-critical predictive maintenance applications can detect anomalies and respond immediately.

Question 108

You are designing a Power BI dataset that includes multiple large tables. Users frequently perform aggregations and drill-down analyses. Which approach optimizes report performance?

A) Create aggregation tables to precompute frequently used metrics

B) Enable DirectQuery for all tables

C) Remove calculated columns

D) Split the dataset into multiple PBIX files

Answer: A) Create aggregation tables to precompute frequently used metrics

Explanation:

Aggregation tables precompute frequently used metrics and summaries, allowing queries to retrieve results quickly without scanning entire datasets. This improves performance for aggregation and drill-down operations, reducing latency and enhancing user experience. DirectQuery avoids importing data but can degrade performance because each visual sends live queries to the source system, which may not be optimized for analytical workloads. Removing calculated columns slightly reduces memory usage but does not address performance issues caused by large dataset scans. Splitting datasets into multiple PBIX files increases administrative overhead and may introduce redundancy or inconsistencies. Aggregation tables provide a scalable and maintainable solution that balances performance and flexibility. Users can access precomputed metrics quickly while retaining the ability to drill into detailed data when needed. Incremental refresh can further improve efficiency by updating only changed data. This approach aligns with best practices for high-performance Power BI reporting, ensuring fast response times, efficient resource usage, and scalable analytics for large datasets.

Question 109

You are implementing incremental data ingestion from on-premises SQL Server to Azure Data Lake using Azure Data Factory. The source tables include a last-modified timestamp column. Which strategy ensures efficient processing?

A) Use a watermark column to load only new or updated rows

B) Copy the entire table daily

C) Use full overwrite of existing files

D) Append all rows without considering timestamps

Answer: A) Use a watermark column to load only new or updated rows

Explanation:

A watermark column tracks the last processed timestamp or row, enabling incremental ingestion of only new or modified records. This reduces network traffic, storage usage, and processing time, improving ETL efficiency. Copying the entire table daily consumes excessive resources, increases runtime, and can create redundant data. Full overwrite of existing files is resource-intensive and may cause downtime or errors during processing. Appending all rows without considering timestamps can introduce duplicates and inconsistencies. Watermark-based incremental loading ensures timely and accurate ingestion while minimizing overhead. It simplifies monitoring and error handling, as only relevant data is processed during each run. This method is considered best practice for large or frequently updated datasets, ensuring Azure Data Lake storage remains synchronized with source systems. Watermark-based loading also supports incremental refresh in downstream analytics, optimizing pipeline performance, maintainability, and reliability.

Question 110

You are designing column-level security in Azure SQL Database. Users require access to most columns but must not see sensitive PII data. Which feature is most appropriate?

A) Dynamic Data Masking

B) Row-Level Security

C) Transparent Data Encryption

D) Always Encrypted

Answer: A) Dynamic Data Masking

Explanation:

Dynamic Data Masking (DDM) hides sensitive column values in query results for non-privileged users while allowing access to non-sensitive data. This ensures reporting and analytics users can interact with datasets without exposing confidential information. Row-Level Security controls access at the row level and cannot restrict specific columns. Transparent Data Encryption secures data at rest but does not prevent sensitive information from appearing in query results. Always Encrypted provides strong end-to-end encryption but requires client-side decryption, which can complicate analytics and reporting. DDM is simple to implement, requires no application changes, and supports multiple masking patterns including partial, randomized, and format-based masking. This approach balances usability and security, ensuring compliance with privacy regulations while maintaining access to non-sensitive columns. DDM is widely recommended for scenarios where most data must remain accessible but sensitive columns need to be concealed, providing a practical, maintainable solution for column-level security.

Question 111

You are designing an Azure Synapse Analytics solution with large fact tables and small dimension tables. You need to optimize join performance for analytical queries. Which strategy is most appropriate?

A) Hash-distribute the fact tables on foreign keys and replicate small dimension tables

B) Round-robin distribute all tables

C) Replicate the fact tables and hash-distribute dimension tables

D) Leave all tables unpartitioned

Answer: A) Hash-distribute the fact tables on foreign keys and replicate small dimension tables

Explanation:

Hash distribution ensures that rows in a large fact table with the same foreign key are colocated with matching dimension rows on the same compute node. This minimizes inter-node data movement during joins, enabling parallel query execution and improving overall performance. Replicating small dimension tables ensures that each node has a complete copy, eliminating the need for data shuffling during joins. Round-robin distribution evenly spreads rows across nodes but does not align join keys, resulting in increased network traffic and slower joins. Replicating large fact tables is inefficient due to high storage and network usage. Hash-distributing dimension tables is unnecessary for small tables because replication is more effective. Leaving tables unpartitioned can lead to uneven workloads and degraded performance. Combining hash-distributed fact tables with replicated small dimensions is a widely recommended best practice for distributed data warehousing. This approach reduces latency, minimizes computational overhead, and ensures scalability, predictable performance, and maintainable architecture for analytical workloads in Azure Synapse Analytics.

Question 112

You are building a predictive maintenance solution using Azure ML with streaming IoT data. The model must provide immediate alerts for potential equipment failures. Which deployment method should you choose?

A) Azure ML Real-Time Endpoint

B) Batch Endpoint

C) Azure Data Factory Pipeline

D) Power BI Dashboard

Answer: A) Azure ML Real-Time Endpoint

Explanation:

Azure ML Real-Time Endpoints provide low-latency predictions, ideal for real-time scenarios such as predictive maintenance. Streaming IoT data can be continuously ingested via REST APIs, and the model generates predictions instantly, enabling timely alerts and automated interventions. Batch Endpoints are intended for large datasets processed periodically and cannot meet real-time requirements. Azure Data Factory pipelines orchestrate ETL and batch transformations, not real-time scoring. Power BI dashboards are visualization tools and cannot execute models in real time. Real-Time Endpoints also support autoscaling, monitoring, logging, and version control, ensuring robust and maintainable production deployment. This deployment approach enables proactive maintenance, reduces downtime, and ensures immediate response to equipment failures. Integration with Azure IoT Hub or Event Hub allows seamless streaming data ingestion. Real-Time Endpoints provide responsiveness, scalability, and reliability required for mission-critical predictive maintenance applications, supporting actionable insights and operational efficiency.

Question 113

You are designing a Power BI dataset that includes multiple large tables. Users frequently perform aggregations and drill-downs. Which approach optimizes report performance?

A) Create aggregation tables to precompute frequently used metrics

B) Enable DirectQuery for all tables

C) Remove calculated columns

D) Split the dataset into multiple PBIX files

Answer: A) Create aggregation tables to precompute frequently used metrics

Explanation:

Aggregation tables precompute metrics and summaries, allowing queries to return results quickly without scanning entire datasets. This enhances performance for aggregation and drill-down operations, reducing latency and improving user experience. DirectQuery avoids importing data but can reduce performance because each visual sends live queries to the source system, which may not be optimized for analytical workloads. Removing calculated columns slightly reduces memory usage but does not address the core performance issue caused by large dataset scans. Splitting datasets into multiple PBIX files increases administrative complexity and may introduce redundancy or inconsistencies. Aggregation tables provide a scalable and maintainable solution that balances speed and flexibility. Users can access precomputed metrics quickly while retaining the ability to drill into detailed data when needed. Incremental refresh further improves efficiency by updating only changed data. This approach follows best practices for high-performance Power BI reporting, ensuring fast response times, efficient resource usage, and scalable analytics for complex datasets.

Question 114

You are implementing incremental data ingestion from on-premises SQL Server to Azure Data Lake using Azure Data Factory. The source tables include a last-modified timestamp column. Which method ensures efficient processing?

A) Use a watermark column to load only new or updated rows

B) Copy the entire table daily

C) Use full overwrite of existing files

D) Append all rows without considering timestamps

Answer: A) Use a watermark column to load only new or updated rows

Explanation:

A watermark column tracks the last processed timestamp or row, allowing incremental loading of only new or modified data. This reduces network traffic, storage consumption, and processing time, enhancing ETL efficiency. Copying the entire table daily consumes significant resources, increases runtime, and may lead to redundant data. Full overwrites of existing files are resource-intensive and can cause downtime or processing errors. Appending all rows without considering timestamps can introduce duplicates and inconsistencies in downstream systems. Watermark-based incremental loading ensures accurate, timely ingestion while minimizing overhead. It simplifies monitoring and error handling, as only relevant data is processed in each run. This method is a best practice for large or frequently updated datasets, ensuring Azure Data Lake storage remains synchronized with source systems. Watermark-based ingestion also supports incremental refresh in downstream analytics, optimizing pipeline performance, maintainability, and reliability.

Question 115

You are designing column-level security in Azure SQL Database. Users require access to most columns but must not see sensitive PII data. Which feature is most appropriate?

A) Dynamic Data Masking

B) Row-Level Security

C) Transparent Data Encryption

D) Always Encrypted

Answer: A) Dynamic Data Masking

Explanation:

Dynamic Data Masking (DDM) conceals sensitive column values in query results for non-privileged users while allowing access to non-sensitive data. This ensures that reporting and analytics users can interact with datasets without exposing confidential information. Row-Level Security restricts access at the row level and does not control column visibility. Transparent Data Encryption protects data at rest but does not prevent sensitive information from appearing in queries. Always Encrypted provides end-to-end encryption but requires client-side decryption, complicating analytics workflows. DDM is easy to implement, requires no application changes, and supports multiple masking patterns such as partial, randomized, and format-based masking. This approach balances usability and security, ensuring compliance with privacy regulations while maintaining access to non-sensitive columns. DDM is recommended for scenarios where most data must remain accessible but sensitive columns must be concealed, providing a practical and maintainable solution for column-level security.

Question 116

You are designing an Azure Synapse Analytics solution with multiple large fact tables and small dimension tables. You need to optimize join performance for analytical queries. Which strategy is most appropriate?

A) Hash-distribute the fact tables on foreign keys and replicate small dimension tables

B) Round-robin distribute all tables

C) Replicate the fact tables and hash-distribute dimension tables

D) Leave all tables unpartitioned

Answer: A) Hash-distribute the fact tables on foreign keys and replicate small dimension tables

Explanation:

Hash distribution ensures that rows in large fact tables with the same foreign key are colocated with corresponding dimension rows on the same compute node. This reduces inter-node data movement during joins, which significantly improves query performance. Replicating small dimension tables ensures each node has a complete copy, eliminating additional shuffling during joins. Round-robin distribution spreads rows evenly but does not align join keys, leading to excessive network traffic and slower joins. Replicating large fact tables is inefficient because it requires high storage and network resources. Hash-distributing small dimension tables is unnecessary since replication is more effective for small datasets. Leaving tables unpartitioned results in uneven workloads, degraded performance, and slower queries. Combining hash-distributed fact tables with replicated small dimensions is a recommended best practice in distributed data warehousing. This approach enables parallel processing, predictable query performance, scalability, reduced latency, and maintainable architecture, making it ideal for large-scale analytical workloads in Azure Synapse Analytics.

Question 117

You are building a predictive maintenance solution using Azure ML with streaming IoT data. The model must provide immediate alerts for potential equipment failures. Which deployment method should you use?

A) Azure ML Real-Time Endpoint

B) Batch Endpoint

C) Azure Data Factory Pipeline

D) Power BI Dashboard

Answer: A) Azure ML Real-Time Endpoint

Explanation:

Azure ML Real-Time Endpoints provide low-latency predictions, essential for real-time predictive maintenance scenarios. Streaming IoT data can be continuously ingested via REST APIs, and the model returns predictions instantly, enabling timely alerts and automated interventions. Batch Endpoints process large datasets periodically and are unsuitable for immediate responses. Azure Data Factory pipelines orchestrate ETL and batch transformations but cannot perform real-time scoring. Power BI dashboards are visualization tools and cannot execute predictive models in real time. Real-Time Endpoints also support autoscaling, monitoring, logging, and version control, making them robust for production deployment. This deployment method enables proactive maintenance, reduces downtime, and ensures rapid operational response to equipment failures. Integration with Azure IoT Hub or Event Hub allows seamless streaming data ingestion. Real-Time Endpoints provide scalability, responsiveness, and reliability for mission-critical predictive maintenance applications, allowing organizations to detect anomalies and act immediately to prevent costly downtime.

Question 118

You are designing a Power BI dataset that combines multiple large tables. Users frequently perform aggregations and drill-downs. Which approach optimizes report performance?

A) Create aggregation tables to precompute frequently used metrics

B) Enable DirectQuery for all tables

C) Remove calculated columns

D) Split the dataset into multiple PBIX files

Answer: A) Create aggregation tables to precompute frequently used metrics

Explanation:

Aggregation tables store precomputed metrics and summaries, allowing queries to retrieve results quickly without scanning entire datasets. This improves performance for aggregation and drill-down operations, providing faster response times for end users. DirectQuery avoids importing data but may reduce performance because each visual sends live queries to source systems, which may not be optimized for analytical workloads. Removing calculated columns slightly reduces memory usage but does not address the performance issue caused by large dataset scans. Splitting datasets into multiple PBIX files increases administrative complexity and may create redundancy or inconsistencies. Aggregation tables provide a scalable and maintainable solution that balances performance and flexibility. Users can access precomputed metrics quickly while retaining the ability to drill into detailed data. Incremental refresh can further enhance efficiency by updating only changed data. This approach aligns with best practices for high-performance Power BI reporting, ensuring fast response times, optimized resource usage, and scalable analytics for complex datasets.

Question 119

You are implementing incremental data ingestion from on-premises SQL Server to Azure Data Lake using Azure Data Factory. The source tables include a last-modified timestamp column. Which method ensures efficient processing?

A) Use a watermark column to load only new or updated rows

B) Copy the entire table daily

C) Use full overwrite of existing files

D) Append all rows without considering timestamps

Answer: A) Use a watermark column to load only new or updated rows

Explanation:

A watermark column tracks the last processed timestamp or row, allowing incremental ingestion of only new or modified records. This reduces network usage, storage consumption, and processing time, improving ETL efficiency. Copying the entire table daily consumes excessive resources, increases runtime, and may lead to redundant data. Full overwrite of existing files is resource-intensive and can introduce downtime or errors during processing. Appending all rows without considering timestamps can create duplicates and inconsistencies in downstream systems. Watermark-based incremental loading ensures timely and accurate ingestion while minimizing overhead. It simplifies monitoring and error handling since only relevant data is processed per pipeline run. This method is considered best practice for large or frequently updated datasets, ensuring Azure Data Lake storage remains synchronized with on-premises sources. Watermark-based loading also supports incremental refresh in downstream analytics, optimizing pipeline performance, maintainability, and reliability.

Question 120

You are designing column-level security in Azure SQL Database. Users require access to most columns but must not see sensitive PII data. Which feature is most appropriate?

A) Dynamic Data Masking

B) Row-Level Security

C) Transparent Data Encryption

D) Always Encrypted

Answer: A) Dynamic Data Masking

Explanation:

Dynamic Data Masking (DDM) conceals sensitive column values in query results for non-privileged users while allowing access to non-sensitive data. This ensures reporting and analytics users can work with datasets without exposing confidential information. Row-Level Security controls access at the row level and does not provide column-level protection. Transparent Data Encryption secures data at rest but does not prevent sensitive information from appearing in query results. Always Encrypted provides end-to-end encryption but requires client-side decryption, which complicates analytics and reporting workflows. DDM is easy to implement, requires no changes to applications, and supports multiple masking patterns, including partial, randomized, and format-based masking. This approach balances usability and security, ensuring compliance with privacy regulations while keeping most data accessible and sensitive columns hidden. DDM is widely recommended for scenarios where most columns must remain available, but PII or sensitive data needs to be concealed, providing a maintainable solution for column-level security.