Microsoft DP-600 Implementing Analytics Solutions Using Microsoft Fabric Exam Dumps and Practice Test Questions Set 7  Q121-140

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Question 121

You are designing an Azure Synapse Analytics solution with a large fact table and several small dimension tables. You need to optimize join performance for analytical queries. 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 with the same foreign key are colocated with the matching rows from dimension tables on the same compute node. This reduces the need for inter-node data movement during join operations, which significantly improves query performance. Replicating small dimension tables allows each node to have a complete copy, eliminating data shuffling for joins with small tables. Round-robin distribution evenly distributes rows across nodes but does not align the join keys, leading to increased network traffic and slower performance. Replicating large fact tables is inefficient due to high storage and network overhead. Hash-distributing dimension tables is unnecessary because small tables are best replicated. Leaving tables unpartitioned can lead to uneven workloads, slower queries, and poor performance. Combining hash-distributed fact tables with replicated small dimensions is considered a best practice in distributed data warehousing. It ensures efficient parallel processing, predictable performance, scalability, reduced latency, and maintainable architecture for large-scale analytical workloads in Azure Synapse Analytics. This approach optimizes join locality, reduces computational overhead, and provides a highly performant solution for enterprise analytics scenarios.

Hash-distributing the fact table on foreign keys and replicating small dimension tables is a best-practice approach for optimizing performance in a distributed data warehouse environment such as Azure Synapse Analytics. In this approach, the fact table, which typically contains millions or billions of rows, is distributed across compute nodes using a hash function on one or more foreign key columns. These foreign keys are usually the columns most frequently used in joins with dimension tables. Hash distribution ensures that rows with the same key value are located on the same compute node as their corresponding dimension rows. This minimizes the need for inter-node data movement, also known as shuffling, during query execution. Reducing data movement significantly improves performance because network transfer is often the largest bottleneck in distributed queries. Small dimension tables are replicated across all compute nodes. Replication allows joins between the fact table and dimension tables to happen locally on each node, eliminating the need for shuffling the dimension data across the network. This combination ensures that queries involving joins execute quickly and efficiently while maintaining scalability for large datasets.

Round-robin distributing all tables evenly spreads the rows across compute nodes without considering any key values. This strategy ensures balanced storage and workload across nodes but does not optimize for joins. When a query joins a fact table with a dimension table, rows may not be located on the same node, forcing the system to move large amounts of data between nodes to complete the join. This shuffling increases query latency and reduces overall performance. While round-robin distribution can be suitable for staging tables or temporary datasets where joins are minimal, it is not ideal for production fact and dimension tables that require frequent joins.

Replicating the fact table and hash-distributing dimension tables is highly inefficient in most scenarios. Fact tables are usually very large, and replicating them across all compute nodes would consume excessive storage and memory, leading to operational challenges and potential performance degradation. While hash-distributing smaller dimension tables may reduce some shuffling, the overhead of replicating a massive fact table outweighs any benefits. This approach is rarely recommended for production environments where performance and resource efficiency are critical.

Leaving all tables unpartitioned places all data on a single node, creating a significant performance bottleneck. Queries must process all data sequentially on one node, which prevents the system from leveraging its distributed architecture. This severely limits parallelism, slows query execution, and can cause resource exhaustion when working with large tables. For enterprise-grade data warehouses, unpartitioned tables are impractical for production workloads and cannot scale effectively as data volumes increase.

Hash-distributing the fact table on foreign keys and replicating small dimension tables balances performance, storage, and scalability. The fact table is distributed to reduce inter-node data movement during joins, while replication of small dimension tables allows local joins on each node, optimizing query execution. This strategy ensures that complex analytical queries run efficiently, supports large-scale datasets, and maintains operational stability. Compared to round-robin distribution, replicating the fact table, or leaving tables unpartitioned, hash distribution with replication provides the best balance of query performance and resource utilization in a massively parallel processing environment. It is a standard practice for optimizing fact and dimension table joins and is widely recommended for high-performance data warehouse solutions.

Question 122

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

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 suitable for real-time scenarios such as predictive maintenance. Streaming IoT data can be continuously sent via REST APIs, and the model provides predictions immediately, enabling timely alerts and automated intervention. Batch Endpoints are intended for processing large datasets periodically and are not suitable for real-time decision-making. Azure Data Factory pipelines orchestrate ETL and batch transformations but cannot execute real-time predictions. Power BI dashboards are visualization tools and cannot perform live scoring. Real-Time Endpoints also support autoscaling, logging, monitoring, and version control, making them robust for production deployments. This deployment enables proactive maintenance, reduces equipment downtime, and ensures timely operational response. 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, allowing organizations to detect anomalies and respond immediately, preventing failures and minimizing costs.

Question 123

You are designing a Power BI dataset that combines multiple large tables. Users perform frequent 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 metrics and summaries, allowing queries to return results quickly without scanning entire datasets. This improves performance for aggregation and drill-down operations, providing faster response times for users. DirectQuery avoids importing data but can reduce performance because each visual sends live queries to the source system, which may not be optimized for large analytical workloads. Removing calculated columns slightly reduces memory usage but does not address the performance bottleneck caused by large dataset scans. Splitting datasets into multiple PBIX files increases administrative complexity and may lead to redundancy or inconsistencies. Aggregation tables provide a scalable and maintainable solution that balances performance and flexibility. Users can quickly access precomputed metrics while still having the ability to drill into detailed data. Incremental refresh further improves efficiency by updating only the data that has changed. 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.

Creating aggregation tables to precompute frequently used metrics is a highly effective method for improving performance in Power BI. Aggregation tables summarize detailed data into precomputed results at higher levels of granularity, such as daily totals, monthly averages, or product category summaries. By storing these precomputed values, queries can access smaller datasets instead of scanning the entire fact table for each report or visualization. This approach significantly reduces query execution time, enhances responsiveness, and improves the overall user experience, especially for large datasets containing millions of rows. Aggregation tables also allow drill-down capabilities so that detailed data remains available if needed. By optimizing memory and computational resources, this method enables faster, interactive reports while maintaining analytical richness.

Enabling DirectQuery for all tables allows Power BI to query the underlying data source in real time instead of importing data into memory. While this ensures that reports reflect the most up-to-date data, it can negatively impact performance. Every interaction in the report, such as filtering, slicing, or drill-down, generates queries that must execute on the source database. If the database is not optimized for high query volumes or if network latency is significant, reports may respond slowly. Additionally, complex transformations and calculated columns may not be efficiently processed using DirectQuery, further affecting performance. For scenarios requiring rapid access to frequently queried metrics, DirectQuery does not provide the same efficiency as precomputed aggregation tables.

Removing calculated columns can reduce memory usage since calculated columns are stored in memory for every row in a dataset. However, this approach addresses memory optimization rather than improving query performance for frequently used metrics. Calculated columns are often essential for analytical purposes, and removing them indiscriminately may reduce the quality and usability of reports. Optimizing calculated columns or converting them into measures may help, but this method does not address repeated computational overhead for metrics that are queried frequently.

Splitting the dataset into multiple PBIX files can help manage large datasets and improve maintainability, but it introduces complexity for users. Navigating between multiple files may disrupt workflow, and combining data across multiple PBIX files can increase query overhead. While splitting datasets can reduce individual file size, it does not directly improve the speed of queries for commonly used metrics within a single report. Aggregation tables offer a more centralized and efficient solution, improving query performance without fragmenting data.

Creating aggregation tables is the most effective strategy because it precomputes frequently queried metrics, reducing the need for repeated calculations. This optimization enhances performance, lowers memory and CPU usage, and improves the end-user experience in interactive reports. Unlike enabling DirectQuery, which relies on the speed and availability of external data sources, or removing calculated columns, which may reduce functionality, aggregation tables maintain the richness of the dataset while providing faster responses. Splitting PBIX files may help with organization but does not solve query latency issues. Aggregation tables enable Power BI to deliver high-performance dashboards for large-scale analytics, ensuring that users can access critical metrics quickly and efficiently.

Question 124

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 the pipeline to ingest only new or modified records. This approach reduces network usage, storage consumption, and processing time, enhancing ETL efficiency. Copying the entire table daily consumes excessive resources, increases processing time, and may introduce redundant data. Full overwrites of existing files are resource-intensive and may result in downtime or errors. Appending all rows without considering timestamps can introduce duplicates and inconsistencies in downstream systems. Watermark-based incremental loading ensures timely and accurate ingestion while minimizing overhead. It simplifies monitoring and error handling, as only relevant data is processed in each pipeline run. This method is considered a best practice for large or frequently updated datasets, ensuring that 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 125

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 restricts access at the row level and does not provide protection for individual columns. 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, complicating analytics workflows. DDM is easy 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 allowing 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.

Dynamic Data Masking (DDM) is a database security feature that helps protect sensitive information by masking it in query results for users who do not have appropriate permissions, while leaving the underlying data intact. This feature is designed to prevent accidental or unauthorized exposure of sensitive data such as Social Security numbers, credit card details, email addresses, or other personally identifiable information. Dynamic Data Masking operates in real time, ensuring that queries executed by non-privileged users automatically return masked values according to predefined rules. For example, a credit card number may be displayed as “XXXX-XXXX-1234” for non-authorized users while authorized users see the full number. This mechanism allows organizations to enforce data privacy policies and comply with regulatory requirements without modifying application code or altering the underlying database schema. DDM is straightforward to implement, supports role-based policies, and can be applied selectively to columns containing sensitive data, making it an efficient way to reduce risk and maintain usability for authorized users.

Row-Level Security (RLS) restricts access to specific rows in a table based on the user’s identity or role. It is commonly used in multi-tenant applications or scenarios where users should only access data relevant to their department, region, or role. While RLS controls which rows a user can see, it does not mask sensitive column values within those rows. Users with access to a row can still view all the column values, including confidential information. RLS is effective for filtering data at the row level but does not provide column-level obfuscation, meaning it cannot prevent exposure of sensitive data if the user has access to the relevant rows.

Transparent Data Encryption (TDE) encrypts the database at rest by securing the physical files and backups. TDE protects data from unauthorized access to storage media or database files, ensuring that the database remains unreadable if stolen. However, TDE does not prevent exposure of sensitive information during normal queries executed by authorized users. Users with read permissions can still access unencrypted data through standard queries. TDE is primarily designed for storage-level security, protecting data when it is at rest, but it does not provide dynamic masking for query results or limit visibility to sensitive columns.

Always Encrypted is a feature that ensures end-to-end encryption of sensitive data both at rest and in transit. Data is encrypted in the database and can only be decrypted by authorized applications or users with the appropriate keys. While it offers strong security for highly sensitive information, it often requires application changes to handle encrypted columns correctly, and there are limitations on querying encrypted data. Some operations, such as certain comparisons and transformations, cannot be performed directly on encrypted columns. Dynamic Data Masking, in contrast, allows organizations to mask data in real time without modifying applications, providing flexibility while still protecting sensitive column values.

Dynamic Data Masking is the optimal solution when the goal is to limit exposure of sensitive data while maintaining usability for authorized users. It provides column-level protection in real time without requiring major changes to applications or database schema. Unlike RLS, which controls row visibility, DDM focuses on masking sensitive data within columns. Unlike TDE, it protects data during use rather than just at rest. Compared to Always Encrypted, it is simpler to implement and allows non-privileged users to safely query and interact with masked data while authorized users retain full access. Dynamic Data Masking reduces risk, supports compliance, and ensures sensitive information is not inadvertently exposed in query results.

Question 126

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

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 sharing the same foreign key are colocated with corresponding dimension rows on the same compute node. This minimizes inter-node data movement during join operations, which improves query performance and reduces computational overhead. Replicating small dimension tables ensures every compute node has a full copy, eliminating the need for data shuffling during joins. Round-robin distribution evenly spreads rows across nodes but does not align join keys, causing increased network traffic and slower query execution. Replicating large fact tables is resource-intensive and inefficient due to storage and network requirements. Hash-distributing dimension tables is unnecessary for small tables because replication is more effective. Leaving tables unpartitioned can result in uneven workloads, degraded performance, and longer query times. Combining hash-distributed fact tables with replicated small dimension tables is a best practice in distributed data warehousing, providing parallel processing, predictable performance, scalability, and maintainable architecture. This strategy ensures efficient joins, reduced latency, and optimal resource utilization for large-scale analytical workloads in Azure Synapse Analytics.

Question 127

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 select?

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 ideal for real-time scenarios like predictive maintenance. Streaming IoT data can be continuously ingested through REST APIs, and the model returns predictions immediately, enabling timely alerts and automated interventions. Batch Endpoints are intended for large datasets processed periodically and cannot provide immediate responses. Azure Data Factory pipelines orchestrate ETL and batch transformations but are not designed for real-time scoring. Power BI dashboards are visualization tools and cannot perform predictive model execution in real time. Real-Time Endpoints also support autoscaling, monitoring, logging, and version control, making them robust for production deployments. This deployment allows organizations to detect anomalies and respond immediately, reducing equipment downtime and ensuring operational efficiency. Integration with Azure IoT Hub or Event Hub enables seamless streaming data ingestion. Real-Time Endpoints provide scalability, responsiveness, and reliability required for mission-critical predictive maintenance applications.

Question 128

You are designing a Power BI dataset that combines 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 metrics and summaries, allowing queries to return results quickly without scanning the entire dataset. This approach 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 the source system, which may not be optimized for analytical workloads. Removing calculated columns slightly reduces memory usage but does not solve the primary bottleneck caused by scanning large tables. Splitting datasets into multiple PBIX files increases administrative complexity and may create redundancy or inconsistencies. Aggregation tables provide a scalable, maintainable solution that balances performance and flexibility. Users can quickly access precomputed metrics while retaining the ability to drill into detailed data. Incremental refresh further enhances efficiency by updating only changed data. This approach follows best practices for high-performance Power BI reporting, ensuring fast response times, optimized resource usage, and scalable analytics for complex datasets.

Question 129

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 ingestion of only new or modified records. This approach reduces network traffic, storage consumption, and processing time, enhancing ETL efficiency. Copying the entire table daily consumes excessive resources, increases runtime, and may result in redundant data. Full overwrite of existing files is resource-intensive and can introduce downtime or processing errors. 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 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 source systems. Watermark-based ingestion also supports incremental refresh in downstream analytics, optimizing pipeline performance, maintainability, and reliability.

Question 130

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 restricts access at the row level and does not provide protection for specific columns. Transparent Data Encryption secures 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, which can complicate analytics 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 keeping most data accessible and sensitive columns hidden. DDM is recommended for scenarios where users need access to most data but PII must remain protected, providing a maintainable solution for column-level security.

Question 131

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

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 large fact tables sharing the same foreign key are colocated with corresponding dimension rows on the same compute node. This minimizes inter-node data movement during join operations, improving query performance and resource efficiency. Replicating small dimension tables ensures each node has a complete copy, eliminating data shuffling for joins. Round-robin distribution spreads data evenly but does not align join keys, resulting in increased network traffic and slower query execution. Replicating large fact tables consumes excessive storage and network bandwidth, making it impractical. Hash-distributing small dimension tables is unnecessary because replication provides faster joins for small tables. Leaving tables unpartitioned can cause uneven workloads, degraded performance, and longer query execution times. Combining hash-distributed fact tables with replicated small dimensions is a best practice in distributed data warehousing, enabling parallel processing, predictable performance, scalability, reduced latency, and maintainable architecture. This strategy ensures efficient joins, optimized resource utilization, and high-performance analytics in Azure Synapse Analytics for large-scale workloads.

Question 132

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 select?

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, which is critical for real-time predictive maintenance. Streaming IoT data can be continuously ingested via REST APIs, and the model returns predictions immediately, enabling timely alerts and automated responses. Batch Endpoints are designed for processing large datasets periodically and cannot provide instant feedback. Azure Data Factory pipelines orchestrate ETL and batch transformations but are not suitable for 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 deployments. This deployment allows organizations to detect anomalies and respond immediately, reducing equipment downtime and ensuring operational efficiency. Integration with Azure IoT Hub or Event Hub enables seamless streaming data ingestion. Real-Time Endpoints provide responsiveness, scalability, and reliability required for mission-critical predictive maintenance applications, supporting immediate operational decisions and reducing costs associated with unexpected equipment failures.

Question 133

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 store precomputed metrics and summaries, allowing queries to return 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 can reduce performance because each visual sends live queries to the source system, which may not be optimized for large analytical workloads. Removing calculated columns slightly reduces memory usage but does not solve the primary bottleneck caused by scanning large datasets. Splitting datasets into multiple PBIX files increases administrative overhead and may introduce redundancy or inconsistencies. Aggregation tables provide a scalable, 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 further enhances 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 134

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 ingestion of only new or modified data. This reduces network traffic, storage consumption, 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 result in downtime or errors during processing. Appending all rows without considering timestamps can introduce 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 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 135

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 work with datasets without exposing confidential information. Row-Level Security restricts access at the row level and cannot provide column-level protection. Transparent Data Encryption secures data at rest but does not prevent sensitive information from appearing in queries. Always Encrypted provides strong end-to-end encryption but requires client-side decryption, complicating analytics and reporting. DDM is easy 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 keeping most data accessible and sensitive columns hidden. DDM is recommended when users need access to most columns but PII must remain protected, providing a maintainable solution for column-level security.

Question 136

You are designing an Azure Synapse Analytics solution with a large fact table and multiple small dimension tables. You need to optimize join performance for analytics queries. 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 large fact tables with identical foreign keys are stored on the same compute node as corresponding dimension rows. This reduces inter-node data movement during joins, which is critical for performance optimization. Replicating small dimension tables ensures that each node contains a full copy, eliminating shuffling during joins and accelerating query execution. Round-robin distribution evenly spreads rows across nodes but does not align join keys, resulting in increased network traffic and slower performance. Replicating large fact tables is inefficient because it consumes high storage and network bandwidth. Hash-distributing small dimension tables is unnecessary since replication works more efficiently for small tables. Leaving tables unpartitioned can lead to uneven workloads, long query execution times, and suboptimal performance. Combining hash-distributed fact tables with replicated small dimensions is a best practice in distributed data warehousing. This strategy supports parallel processing, predictable performance, scalability, and maintainable architecture. By colocating related data and reducing shuffling, queries are executed faster, resource utilization is optimized, and large-scale analytical workloads in Azure Synapse Analytics perform efficiently. It ensures low-latency results, consistent query behavior, and scalability, making it ideal for enterprise analytics environments where timely insights are critical.

Question 137

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 select?

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 are designed to provide low-latency predictions for real-time scenarios. Streaming IoT data can be continuously ingested via REST APIs, and predictions are returned instantly, enabling immediate alerts and automated operational responses. Batch Endpoints process large datasets periodically and cannot provide instantaneous insights. Azure Data Factory pipelines handle ETL and batch processing but are not intended for real-time scoring. Power BI dashboards are for data visualization and cannot execute predictive models in real time. Real-Time Endpoints also support autoscaling, monitoring, logging, and version control, making them suitable for production deployment. This setup allows organizations to detect anomalies and respond promptly, minimizing equipment downtime and operational disruption. Integration with Azure IoT Hub or Event Hub ensures seamless ingestion of streaming data. Real-Time Endpoints provide the responsiveness, scalability, and reliability required for predictive maintenance, allowing immediate operational decisions and supporting proactive maintenance strategies that reduce costs and improve system uptime.

Question 138

You are designing a Power BI dataset with 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 store precomputed summaries and metrics, allowing queries to retrieve results quickly without scanning full datasets. This improves performance for aggregation and drill-down analyses, providing faster response times and better user experience. DirectQuery avoids data import but may reduce performance since each visual sends live queries to the source system, which may not handle high analytical loads efficiently. Removing calculated columns slightly reduces memory usage but does not solve performance issues caused by scanning large tables. Splitting datasets into multiple PBIX files increases administrative overhead and may cause redundancy or inconsistencies. Aggregation tables are scalable, maintainable, and provide an efficient solution balancing speed and flexibility. Users can access precomputed metrics instantly while retaining the ability to drill into detailed data. Incremental refresh can further optimize performance by updating only changed data. This approach aligns with best practices for high-performance Power BI reporting, ensuring quick responses, optimized resource usage, and scalable analytics for complex datasets.

Question 139

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 generate redundant data. Full overwrite of existing files is resource-intensive and may lead to downtime or errors during processing. Appending all rows without considering timestamps can cause duplicates and inconsistencies in downstream analytics. 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 run. This method is considered best practice for large or frequently updated datasets, ensuring that Azure Data Lake storage remains synchronized with source systems. Incremental ingestion also supports downstream analytics’ incremental refresh, optimizing pipeline performance, maintainability, and reliability.

Question 140

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 restricts access at the row level and does not provide column-level protection. Transparent Data Encryption secures data at rest but does not prevent sensitive data from appearing in queries. Always Encrypted offers 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 including partial, randomized, and format-based masking. This approach balances usability and security, ensuring compliance with privacy regulations while keeping most columns accessible and sensitive data hidden. DDM is recommended for scenarios where users need access to most data but PII must be protected, providing a maintainable solution for column-level security.