Microsoft DP-600 Implementing Analytics Solutions Using Microsoft Fabric Exam Dumps and Practice Test Questions Set 8  Q141-160

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Question 141

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 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 the same foreign keys are colocated with matching rows from dimension tables on the same compute node. This reduces inter-node data movement during joins, improving query performance and efficiency. Replicating small dimension tables ensures that each compute node has a complete copy, eliminating shuffling for joins and accelerating query execution. Round-robin distribution spreads rows evenly but does not align join keys, resulting in increased network traffic and slower performance. Replicating large fact tables is resource-intensive due to storage and network requirements. Hash-distributing small dimension tables is unnecessary since replication is more efficient for small datasets. Leaving tables unpartitioned can lead to uneven workloads, slower queries, and poor 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 data shuffling, queries execute faster, resource utilization is optimized, and large-scale analytical workloads perform efficiently. It ensures low-latency results, consistent performance, and scalability for enterprise analytics workloads in Azure Synapse Analytics.

Hash-distributing the fact table on foreign keys and replicating small dimension tables is a standard best-practice for optimizing performance in a distributed data warehouse environment such as Azure Synapse Analytics. Fact tables typically contain large volumes of data, often millions or billions of rows, and are frequently joined with smaller dimension tables to support analytical queries. Hash distribution spreads rows across compute nodes based on a hash of a specified column, usually a foreign key that links to a dimension table. This ensures that rows with the same key value are located on the same node as their corresponding dimension rows, minimizing the need for inter-node data movement, also called shuffling. Reducing shuffling is critical because network transfer is often the main bottleneck in distributed query execution. By keeping matching rows together, hash distribution improves query performance, reduces latency, and enables the system to scale efficiently.

Small dimension tables are replicated across all compute nodes. Replication means that each node has a complete copy of the dimension table, allowing joins to occur locally without transferring data between nodes. This approach eliminates shuffling for dimension tables and further enhances query performance. Replicating small tables is practical because their size is limited, so storage and memory usage remain manageable. Combined with hash distribution of the fact table, this strategy provides an efficient, scalable solution for joining large fact tables with smaller dimensions in an MPP (Massively Parallel Processing) environment.

Round-robin distribution assigns rows evenly across compute nodes without considering any column values. While this balances storage and compute load, it does not optimize for joins. When a query joins a fact table with a dimension table, rows may not be on the same node, forcing data to move between nodes. This data movement increases query latency and consumes network bandwidth, reducing overall performance. Round-robin distribution may be appropriate for staging or temporary tables where joins are minimal, but it is not ideal for production workloads involving large fact tables and frequent joins with dimension tables.

Replicating the fact table and hash-distributing dimension tables is generally inefficient. Fact tables are usually massive, and replicating them across nodes consumes excessive storage and memory. The replication process can be resource-intensive and slow to load, and the performance benefits are minimal compared to the cost. Hash-distributing dimension tables alone does not solve the main issue because large fact tables still require shuffling for joins. Therefore, this approach is impractical for large-scale production workloads.

Leaving all tables unpartitioned places all data on a single compute node, creating a significant performance bottleneck. Queries must process all data sequentially on one node, limiting parallelism and drastically slowing execution. Large tables, particularly fact tables, may overwhelm the resources of a single node, causing queries to fail or take unacceptably long. This approach does not leverage the advantages of a distributed architecture and is not suitable for enterprise-scale analytics.

Hash-distributing the fact table on foreign keys combined with replicating small dimension tables provides the optimal balance between query performance, storage efficiency, and scalability. It minimizes data movement, reduces network overhead, and allows complex analytical queries to execute quickly. Compared to round-robin distribution, replicating the fact table, or leaving tables unpartitioned, this strategy is the most effective solution for large-scale, high-performance distributed data warehouses, ensuring efficient joins and supporting enterprise-level analytics.

Question 142

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 suitable for real-time scenarios like predictive maintenance. Streaming IoT data can be ingested continuously via REST APIs, and the model returns predictions instantly, enabling immediate alerts and automated operational responses. Batch Endpoints process large datasets periodically and cannot provide immediate insights. 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 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 quickly, minimizing equipment downtime and operational disruption. Integration with Azure IoT Hub or Event Hub ensures seamless streaming data ingestion. Real-Time Endpoints provide the responsiveness, scalability, and reliability required for mission-critical predictive maintenance applications, supporting immediate operational decisions and enabling proactive maintenance strategies that reduce costs and improve system uptime.

Question 143

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 summaries and metrics, allowing queries to return results quickly without scanning the full dataset. This enhances performance for aggregation and drill-down operations, reducing latency and providing better user experience. DirectQuery avoids importing data but can decrease performance because each visual sends live queries to the source system, which may not handle large analytical workloads efficiently. Removing calculated columns slightly reduces memory usage but does not address the core bottleneck caused by scanning large tables. Splitting datasets into multiple PBIX files increases administrative complexity and can 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. Incremental refresh can further enhance efficiency by updating only changed data. This method follows 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 strategy for improving performance in Power BI reports and dashboards. Aggregation tables store precomputed summaries of detailed data at a higher level of granularity, such as totals per day, monthly averages, or product category summaries. By using these precomputed values, queries do not need to scan the entire fact table every time a visualization is rendered. This significantly reduces query execution time and enhances responsiveness, particularly for large datasets with millions of rows. Aggregation tables also allow users to drill down into detailed data when needed, maintaining analytical flexibility. Precomputing these metrics optimizes memory usage and CPU resources in the in-memory engine, enabling fast, interactive reports that improve user experience and productivity.

Enabling DirectQuery for all tables allows Power BI to query data directly from the underlying source instead of importing it into memory. While this ensures access to real-time data, it can severely impact performance. Each interaction, such as filtering, slicing, or drilling down, generates a query that executes against the source database. If the database is not optimized for high-volume queries, or if network latency exists, reports can become slow or unresponsive. Additionally, complex transformations and calculated columns may not be processed efficiently with DirectQuery. For frequently used metrics, this method does not provide the same speed and reliability as precomputed aggregation tables.

Removing calculated columns can reduce memory usage because calculated columns are stored for every row in memory. However, this approach focuses on memory optimization rather than improving query performance for frequently accessed metrics. Calculated columns often provide critical business logic and insights, and removing them indiscriminately may reduce report functionality and analytical capabilities. While optimizing or replacing calculated columns with measures can help, it does not eliminate repeated computations for frequently queried metrics and therefore does not solve the core performance issue.

Splitting the dataset into multiple PBIX files can improve maintainability by reducing individual file size and complexity. However, it introduces operational challenges for users who must navigate between multiple files to access related data. Splitting does not inherently improve the speed of queries for frequently used metrics within a single report. Aggregation tables centralize optimized metrics in one dataset, improving query performance without fragmenting data or adding workflow complexity.

Creating aggregation tables is the most effective solution because it precomputes the most commonly used metrics, reducing repeated calculations and query execution time. This optimization improves report responsiveness, lowers resource consumption, and enhances the interactive experience for end users. Unlike DirectQuery, which depends on the speed of external databases and network latency, aggregation tables provide consistent high performance. Unlike removing calculated columns, which may reduce functionality, aggregation tables retain analytical richness while improving speed. Splitting PBIX files is primarily a structural or organizational tactic and does not directly address query latency. By implementing aggregation tables, organizations can ensure that their Power BI reports deliver timely insights, scale efficiently with large datasets, and support enterprise-grade reporting and analytics.

Question 144

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 traffic, storage consumption, and processing time, improving ETL efficiency. Copying the entire table daily consumes excessive resources, increases runtime, and may create redundant data. Full overwrite of existing files is resource-intensive and can cause 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 because 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. Watermark-based ingestion also supports incremental refresh in downstream analytics, optimizing pipeline performance, maintainability, and reliability.

Question 145

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 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 provides strong end-to-end encryption but requires client-side decryption, complicating 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 columns 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.

Dynamic Data Masking (DDM) is a security feature in databases that protects sensitive information by masking it in query results for users who do not have appropriate privileges, while keeping the underlying data intact. This approach is designed to prevent accidental or unauthorized exposure of sensitive information, such as credit card numbers, social security numbers, email addresses, or other personally identifiable information. DDM operates in real time, automatically applying masking rules when queries are executed by non-privileged users. For example, a credit card number might appear as “XXXX-XXXX-1234” in query results for unauthorized users, while users with the required permissions see the full number. This dynamic masking allows organizations to maintain the usability of data for authorized users while limiting risk exposure, supporting compliance with regulations such as GDPR, HIPAA, and PCI-DSS. Implementing DDM is straightforward because it does not require modifications to the database schema or application code, and it supports role-based policies that control who can view masked or unmasked data. This makes it an efficient and practical solution for protecting sensitive data.

Row-Level Security (RLS) is a technique used to restrict access to specific rows in a database table based on the identity or role of the user executing the query. It is particularly useful in multi-tenant applications or scenarios where users should only see data relevant to their department, region, or role. However, RLS only controls which rows a user can access; it does not mask the data within those rows. Users who have access to a specific row can still view all columns, including sensitive information. While RLS is valuable for restricting access to subsets of data, it cannot prevent exposure of sensitive column-level values and does not replace the functionality of dynamic data masking.

Transparent Data Encryption (TDE) protects data at rest by encrypting the database files and backups. TDE prevents unauthorized access to physical storage or backups, ensuring that stolen database files cannot be read without proper credentials. However, TDE does not mask data when queried by authorized users. Users with legitimate access can still see all sensitive values, so TDE primarily addresses storage-level security rather than protecting data during query execution. While it is an important component of a comprehensive security strategy, it does not provide real-time protection against exposure of sensitive columns.

Always Encrypted provides 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 applications or users that have access to the encryption keys. This method provides strong security guarantees but often requires application changes to handle encrypted columns correctly. Queries on encrypted data can be restricted because certain operations and transformations are limited, which can impact reporting and analytics. Dynamic Data Masking, in contrast, provides flexible column-level masking without requiring application modifications, allowing authorized users to work with the full dataset while unprivileged users see masked results.

Dynamic Data Masking is the best choice when the goal is to limit exposure of sensitive column values in real time while maintaining usability for authorized users. It masks data during query execution, reducing the risk of accidental disclosure. Unlike RLS, which controls row access, DDM focuses on column-level obfuscation. Unlike TDE, it protects data during use rather than just at rest. Compared to Always Encrypted, DDM is simpler to implement and allows easier reporting and analytics while still providing effective data protection. Dynamic Data Masking supports compliance, reduces risk, and ensures sensitive information is not exposed to unauthorized users.

Question 146

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 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 with identical foreign keys are colocated with corresponding dimension table rows on the same compute node. This minimizes inter-node data movement during joins, which is critical for high-performance analytical queries. Replicating small dimension tables ensures that every node has a complete copy, eliminating the need for shuffling data during joins and enhancing query speed. Round-robin distribution evenly spreads data across nodes but does not align join keys, increasing network traffic and reducing query performance. Replicating large fact tables is resource-intensive, consuming significant storage and network bandwidth. Hash-distributing small dimension tables is unnecessary because replication is more effective for small datasets. Leaving tables unpartitioned can lead to uneven workloads, long query execution times, and poor performance. Combining hash-distributed fact tables with replicated small dimension tables is a best practice in distributed data warehousing. It supports parallel processing, predictable performance, scalability, and maintainable architecture. By colocating related data and minimizing shuffling, queries execute faster, resource utilization is optimized, and large-scale analytical workloads in Azure Synapse Analytics perform efficiently. This approach ensures low-latency results, consistent query behavior, and scalable enterprise analytics solutions.

Question 147

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 suitable for real-time scenarios like predictive maintenance. Streaming IoT data can be ingested continuously via REST APIs, and predictions are returned instantly, enabling immediate alerts and automated operational responses. Batch Endpoints process large datasets periodically and are not suitable for instant feedback or real-time monitoring. Azure Data Factory pipelines orchestrate ETL and batch transformations but cannot provide 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 allows organizations to detect anomalies and respond immediately, reducing equipment downtime and operational disruption. Integration with Azure IoT Hub or Event Hub ensures seamless streaming data ingestion. Real-Time Endpoints provide the responsiveness, scalability, and reliability required for mission-critical predictive maintenance applications, enabling immediate operational decisions, proactive maintenance, and reduced costs associated with unexpected equipment failures.

Question 148

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 store precomputed summaries and metrics, allowing queries to return results quickly without scanning the full dataset. This improves performance for aggregation and drill-down operations, providing faster response times and better user experience. DirectQuery avoids importing data but may reduce performance because each visual sends live queries to the source system, which may not efficiently handle large 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 overhead and may introduce redundancy or inconsistencies. Aggregation tables provide a scalable, maintainable solution that balances speed and flexibility. Users can access precomputed metrics quickly 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 quick response times, optimized resource usage, and scalable analytics for complex datasets.

Question 149

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 reduces network traffic, storage consumption, and processing time, improving ETL efficiency. Copying the entire table daily consumes excessive resources, increases runtime, and may produce 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 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 source systems. Incremental ingestion also supports downstream analytics’ incremental refresh, optimizing pipeline performance, maintainability, and reliability.

Question 150

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 interact 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 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 columns accessible and sensitive data hidden. DDM is recommended when users need access to most columns but PII must remain protected, providing a practical and maintainable solution for column-level security.

Question 151

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 the same foreign keys are colocated with matching rows from small dimension tables on the same compute node. This reduces inter-node data movement during join operations, which significantly improves query performance. Replicating small dimension tables ensures that each node contains a full copy of the dimension table, eliminating data shuffling during joins and accelerating query execution. Round-robin distribution spreads rows evenly but does not align join keys, which can lead to increased network traffic and slower query execution. Replicating large fact tables is resource-intensive and inefficient because it consumes significant storage and network bandwidth. Hash-distributing small dimension tables is unnecessary because replication works more efficiently for small datasets. Leaving tables unpartitioned can result in uneven workloads, longer query times, and degraded performance. Combining hash-distributed fact tables with replicated small dimensions is a best practice in distributed data warehousing, supporting parallel processing, predictable performance, scalability, and maintainable architecture. This strategy optimizes join locality, reduces shuffling, and ensures high-performance analytical queries in Azure Synapse Analytics for large-scale workloads.

Question 152

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, making them ideal for real-time predictive maintenance. Streaming IoT data can be ingested continuously via REST APIs, and predictions are returned instantly, enabling immediate alerts and automated operational responses. Batch Endpoints are designed for periodic processing of large datasets and cannot deliver immediate insights. Azure Data Factory pipelines orchestrate ETL and batch transformations but do not support 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 suitable for production deployment. This setup enables organizations to detect anomalies and respond quickly, minimizing equipment downtime and operational disruptions. Integration with Azure IoT Hub or Event Hub ensures seamless streaming data ingestion. Real-Time Endpoints provide the responsiveness, scalability, and reliability required for mission-critical predictive maintenance, enabling proactive actions and reducing costs associated with equipment failures.

Question 153

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 summaries and metrics, allowing queries to return results quickly without scanning the entire dataset. This improves performance for aggregation and drill-down operations, reducing latency and providing faster response times. DirectQuery avoids importing data but can reduce performance because each visual sends live queries to the source system, which may not efficiently handle large analytical workloads. Removing calculated columns slightly reduces memory usage but does not address performance issues caused by scanning large tables. Splitting datasets into multiple PBIX files increases administrative complexity 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 needed. 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 154

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 traffic, 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 can cause downtime or errors during processing. Appending all rows without considering timestamps can create duplicates and inconsistencies in downstream analytics. Watermark-based incremental loading ensures timely and accurate ingestion while minimizing overhead. It simplifies monitoring and error handling because only relevant data is processed per run. This method is considered a best practice for large or frequently updated datasets, ensuring Azure Data Lake storage remains synchronized with source systems. Incremental ingestion also supports downstream analytics’ incremental refresh, optimizing pipeline performance, maintainability, and reliability.

Question 155

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 does not provide protection for specific columns. Transparent Data Encryption secures data at rest but does not prevent sensitive data 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 columns accessible and sensitive data hidden. DDM is recommended for scenarios where users need access to most columns but PII must remain protected, providing a practical, maintainable solution for column-level security.

Question 156

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 should you implement?

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 stored on the same compute node as the corresponding rows from dimension tables. This reduces inter-node data movement during joins, which significantly improves query performance. Replicating small dimension tables ensures that each node contains a full copy, eliminating the need for data shuffling during joins and accelerating execution. Round-robin distribution spreads rows evenly but does not align join keys, increasing network traffic and slowing queries. Replicating large fact tables is inefficient due to high storage and network usage. Hash-distributing small dimension tables is unnecessary because replication works better for small tables. Leaving tables unpartitioned can result in uneven workloads, slower queries, and poor performance. Combining hash-distributed fact tables with replicated small dimensions is a best practice in distributed data warehousing. This approach supports parallel processing, predictable performance, scalability, and maintainable architecture. It ensures optimized join locality, reduced shuffling, and high-performance analytics for large-scale workloads in Azure Synapse Analytics. Queries execute faster, resource utilization is optimized, and enterprise analytics environments benefit from low-latency, scalable operations.

Question 157

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 suitable for real-time predictive maintenance. Streaming IoT data can be ingested continuously through REST APIs, and predictions are returned instantly, enabling immediate alerts and automated responses. Batch Endpoints are designed for large-scale periodic processing and cannot deliver immediate insights. Azure Data Factory pipelines handle ETL and batch orchestration but do not support real-time scoring. Power BI dashboards are visualization tools and cannot execute predictive models in real time. Real-Time Endpoints also offer autoscaling, monitoring, logging, and version control, making them ideal for production environments. This deployment allows organizations to detect anomalies and respond immediately, minimizing equipment downtime and operational disruption. Integration with Azure IoT Hub or Event Hub ensures seamless streaming data ingestion. Real-Time Endpoints provide the responsiveness, scalability, and reliability required for mission-critical predictive maintenance applications, enabling proactive decisions and reducing costs associated with equipment failures.

Question 158

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 store precomputed 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 providing faster response times. DirectQuery avoids importing data but may decrease performance because each visual sends live queries to the source system, which may not handle large analytical workloads efficiently. Removing calculated columns slightly reduces memory usage but does not address the main bottleneck caused by scanning large tables. Splitting datasets into multiple PBIX files increases administrative complexity and can 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 aligns with best practices for high-performance Power BI reporting, ensuring fast responses, optimized resource usage, and scalable analytics for complex datasets.

Question 159

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 reduces network traffic, storage usage, and processing time, enhancing 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 can cause 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 because only relevant data is processed per pipeline run. This approach is considered a best practice for large or frequently updated datasets, ensuring Azure Data Lake storage remains synchronized with source systems. Incremental ingestion also supports downstream analytics’ incremental refresh, optimizing pipeline performance, maintainability, and reliability.

Question 160

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 interact 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 provides strong 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 columns hidden. DDM is recommended when users need access to most columns but PII must remain protected, providing a practical, maintainable solution for column-level security.