Microsoft DP-700 Implementing Data Engineering Solutions Using Microsoft Fabric Exam Dumps and Practice Test Questions Set 6 Q76-90

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Question 76

You need to ingest large volumes of log data continuously into Microsoft Fabric and allow analysts to query it in near real-time. Which approach should you use?

A) Batch ingestion into Lakehouse with Power BI import
B) Eventstream ingestion into KQL database with DirectQuery
C) Dataflow scheduled refresh into Warehouse
D) Spark notebook output to CSV

Correct Answer: B)

In contemporary analytics environments, ensuring timely access to data is critical for operational decision-making, especially when dealing with high-volume streaming sources such as IoT devices, application logs, or telemetry systems. Traditional batch-oriented ingestion methods, while reliable for historical or aggregated reporting, often introduce latency that limits the ability to perform near real-time analysis. For example, batch ingestion into a Lakehouse followed by Power BI import relies on scheduled refreshes. Data is only available after each batch load completes and the dataset is refreshed, which can introduce significant delays between when data is generated and when it is available for analysis. This latency makes it difficult for organizations to respond promptly to operational events or monitor fast-moving processes effectively.

Dataflow scheduled refreshes operate on a similar batch model. They are optimized for periodic updates of curated datasets but cannot provide continuous access to streaming data. While incremental refresh in Dataflows can reduce processing time by updating only new or modified records, the underlying batch-oriented nature still prevents near real-time availability. For analytics that require immediate insight, relying solely on Dataflow refreshes is insufficient. Analysts may work with slightly stale data, which can hinder timely decision-making and reduce confidence in operational dashboards.

Another common approach involves using Spark notebooks to process and output data to CSV files. While Spark is highly flexible for transforming and enriching large datasets, writing results to CSV introduces additional latency. The files must then be ingested manually or through scheduled pipelines into downstream analytics systems. This manual step not only delays the availability of the data but also increases operational overhead and the risk of errors. Furthermore, CSV-based workflows are not optimized for real-time queries; they often require full reloads or additional processing before analysts can access the data, limiting responsiveness and scalability.

Eventstream ingestion provides a solution to these limitations by enabling continuous streaming of data directly into a KQL (Kusto Query Language) database. This method allows log and event data to flow in near real time, ensuring that fresh information is always available for analysis. By capturing events as they occur, organizations can monitor operations, detect anomalies, and respond to changing conditions almost instantly. This continuous ingestion model eliminates the latency inherent in batch processing and supports analytics workloads that require immediate insight.

When combined with Power BI DirectQuery, this architecture delivers an end-to-end real-time analytics solution. DirectQuery allows analysts to query the KQL database directly without creating additional copies of the data. Dashboards and reports reflect the latest available information immediately, maintaining low latency while leveraging the scale and performance of the underlying KQL database. This approach also preserves governance and security controls, as data remains in a centralized, managed environment rather than being duplicated across multiple systems.

Overall, this combination of eventstream ingestion and DirectQuery provides a highly efficient architecture for handling high-volume, near real-time data. It minimizes latency, reduces operational overhead, and ensures that analysts have immediate access to fresh, query-ready data. By contrast, batch ingestion, scheduled refreshes, and manual CSV processes introduce delays and additional complexity, making them less suitable for dynamic, streaming analytics environments. This integrated streaming architecture ensures organizations can achieve both scalability and responsiveness while maintaining governance and performance across their analytics ecosystem.

Question 77

A data engineering team wants to perform Python-based feature engineering on terabyte-scale datasets in Microsoft Fabric. Which compute environment is most appropriate?

A) Warehouse T-SQL
B) Spark notebooks
C) Dataflow Gen2
D) KQL queries

Correct Answer: B)

Warehouse T-SQL is optimized for relational queries but does not support Python-based distributed computations efficiently. Dataflow Gen2 provides low-code transformations and incremental refreshes but lacks the ability to execute large-scale Python computations. KQL queries are optimized for analytics over logs and streaming events and cannot perform Python-based feature engineering. Spark notebooks are designed for distributed computing, supporting Python, PySpark, and Scala. They efficiently process terabyte-scale datasets in parallel, enabling complex feature engineering, caching intermediate results, and dynamically scaling compute resources. Spark notebooks integrate with Lakehouse tables, pipelines, and Delta Lake storage, making them ideal for large-scale, Python-based data processing workflows.

Question 78

You need to provide analysts with curated datasets in Power BI that enforce row-level security and reusable measures. Which Fabric feature should be implemented?

A) Direct access to Lakehouse tables
B) Warehouse semantic model
C) CSV exports to Excel
D) KQL database dashboards

Correct Answer: B)

In enterprise analytics environments, providing direct access to raw Lakehouse tables may seem convenient for users who need immediate data access, but it introduces significant risks. When analysts query raw tables directly, sensitive information can be inadvertently exposed, potentially violating data governance policies and compliance requirements. Additionally, direct access increases the likelihood of inconsistent analysis because each user may interpret raw data differently, applying their own logic to calculations and aggregations. Performance can also suffer when multiple analysts run complex queries on large, uncurated datasets simultaneously, creating contention for resources and slowing down the system for everyone.

Exporting data to CSV files is a common workaround for sharing information or enabling offline analysis. While CSV exports provide a snapshot of data at a specific point in time, they are inherently static. Once exported, the data does not automatically update, and analysts may be working with outdated information. CSV files also lack interactivity, meaning users cannot perform dynamic filtering, slicing, or drill-down analysis. Beyond this, CSV exports do not support row-level security or centralized governance, which limits their suitability for regulated environments or datasets containing sensitive information. Moreover, sharing multiple CSV copies can lead to version control challenges and inconsistencies across teams.

KQL dashboards, particularly those built on Kusto databases, are optimized for streaming, log, and telemetry analytics. They excel at near real-time monitoring and event-driven insights, providing operational intelligence for IoT systems, application logs, or high-frequency telemetry data. However, KQL dashboards are not designed to support the kinds of reusable, business-friendly measures or semantic modeling needed for enterprise reporting. They do not provide the abstraction layer required for analysts to explore curated data consistently across multiple reports, nor do they enforce governance policies such as row-level security or standardized metrics. As a result, while KQL is powerful for operational monitoring, it is less suited for interactive, enterprise-scale analytics on curated datasets.

A Warehouse semantic model addresses these challenges by providing a governed, secure, and reusable layer on top of curated datasets. Semantic models abstract the complexity of raw tables, allowing analysts to interact with data through well-defined measures, dimensions, and relationships without needing to access the underlying raw data. Row-level security ensures that users see only the data they are authorized to access, while standardized, reusable measures guarantee consistency across reports. Analysts can build dashboards and perform interactive exploration in Power BI while leveraging optimized query performance, eliminating concerns about direct access to sensitive tables.

In addition to providing security and consistency, semantic models facilitate enterprise-wide governance. They integrate seamlessly with Lakehouse and pipeline workflows, ensuring that curated datasets are consistently available and aligned with organizational standards. By maintaining a single source of truth for measures, calculations, and relationships, semantic models reduce discrepancies between reports and promote reliable, high-performance reporting across teams. Analysts can confidently explore data, build insights, and scale analytics efforts without compromising governance, compliance, or performance.

while direct Lakehouse access, CSV exports, and KQL dashboards provide specific operational or exploratory benefits, they fall short in delivering secure, consistent, and reusable analytical datasets. Warehouse semantic models provide a robust solution, offering a governed abstraction layer with reusable measures, row-level security, and optimized performance. This approach enables interactive analytics, ensures enterprise-wide consistency, and protects sensitive data while supporting high-quality reporting across multiple dashboards and teams.

Question 79

A Lakehouse table receives frequent micro-batches, resulting in millions of small files that degrade query performance. What is the recommended solution?

A) Incremental refresh in Dataflow
B) Auto-optimize and file compaction
C) Export data to CSV
D) KQL database views

Correct Answer: B)

Managing performance in a Lakehouse environment requires careful consideration of both storage structure and query execution. One persistent challenge is the accumulation of small files, which can severely impact query latency, metadata management, and overall system efficiency. Although several tools and approaches exist to optimize data processing and refresh performance, many do not address the underlying issues caused by fragmented storage.

Incremental refresh in Dataflow is a commonly used technique to improve execution efficiency. By updating only new or modified records rather than refreshing entire datasets, incremental refresh reduces processing time, minimizes resource usage, and accelerates Dataflow operations. This method is particularly useful for recurring or scheduled Dataflows where full refreshes are unnecessary. However, while incremental refresh optimizes the performance of Dataflows themselves, it does not solve the problem of small-file accumulation in Lakehouse tables. As data is ingested and processed, numerous small files can still be generated, which fragment storage, increase metadata overhead, and degrade query performance over time.

Exporting data to CSV is another frequently used approach, often intended for offline analysis, sharing datasets, or feeding downstream processes. Although CSV files are easy to handle and compatible with a wide range of tools, each export creates additional files in storage. Over time, these small files accumulate, further increasing metadata overhead and slowing query planning and execution. Large datasets exacerbate this problem, as the storage system must manage a growing number of fragmented files, making queries slower and less efficient. CSV exports may also require additional operational effort to maintain consistency and manage updates, adding further complexity.

KQL database views offer abstraction at the query level, allowing analysts to access and manipulate data through logical views without interacting with underlying tables. These views help standardize queries, improve maintainability, and simplify access for reporting purposes. However, KQL views do not optimize the physical storage or consolidate small files. Queries executed through views still scan fragmented files, which means metadata overhead remains high, and query performance is not improved. While views enhance usability and manageability, they do not address the root cause of small-file issues in Lakehouse environments.

Auto-optimize provides a comprehensive solution for this challenge by automatically merging small files into larger, optimized files. This consolidation reduces the total number of files, lowers metadata overhead, and improves query latency. Delta Lake maintains its performance characteristics even as new data is ingested continuously, ensuring that queries remain efficient and predictable without requiring manual intervention. When combined with partitioning, which segments data into logical blocks, and Z-ordering, which clusters related data within partitions, auto-optimize further enhances query performance. Partitioning allows queries to scan only relevant sections of data, while Z-ordering optimizes selective access patterns and improves resource utilization.

This combination creates a high-performance, scalable environment capable of handling continuously ingested datasets efficiently. Queries run faster, metadata management is simplified, and storage resources are utilized more effectively. Auto-optimize with partitioning and Z-ordering ensures that Lakehouse tables remain performant, even as data volumes grow and ingestion rates increase, making it the most effective approach for resolving small-file performance issues.

while incremental refresh, CSV exports, and KQL views provide operational benefits or abstraction, they do not address the fragmentation and metadata challenges that affect query performance. Auto-optimize, enhanced by partitioning and Z-ordering, provides a robust, enterprise-ready solution that consolidates files, reduces overhead, and enables high-performance analytics on large-scale, continuously ingested datasets.

Question 80

You need to track data lineage, transformations, and dependencies across Lakehouse, Warehouse, and KQL databases for compliance in Microsoft Fabric. Which service should you implement?

A) Dataflow monitoring
B) Microsoft Purview
C) Warehouse audit logs
D) Power BI lineage

Correct Answer: B)

In modern enterprise data environments, gaining complete visibility into how data flows, transforms, and is consumed across various platforms is essential for governance, compliance, and operational efficiency. While several tools provide monitoring and lineage capabilities, they typically function in isolation, offering limited perspectives and leaving critical gaps in enterprise-wide data management.

Dataflow monitoring is one such tool that provides detailed execution logs for individual Dataflows. These logs capture execution times, refresh history, and errors, which are invaluable for operational oversight and troubleshooting. Data engineers and analysts can use these logs to identify failed refreshes, track performance, and optimize scheduling. However, the scope of Dataflow monitoring is limited to individual Dataflows. It does not provide insight into how data moves between datasets, transformations applied during processing, or dependencies across other services and platforms. Consequently, while it is useful for operational visibility within a specific workflow, it cannot deliver the enterprise-wide context necessary for comprehensive governance or impact analysis.

Warehouse audit logs provide another layer of operational insight by recording query activity and user interactions within a relational data warehouse. These logs enable organizations to track who queried which tables, what data was accessed, and when the queries were executed. They are particularly useful for security monitoring and auditing compliance within a single Warehouse component. Despite these benefits, Warehouse audit logs are confined to a single component and cannot track cross-service dependencies or transformations. For large, distributed enterprises, this means that critical lineage and operational context outside the Warehouse remain invisible.

Power BI lineage adds further value by mapping the relationships between datasets, reports, and dashboards within the Power BI environment. Analysts and report authors can visualize which datasets feed into specific reports, understand dependencies, and perform impact analysis if changes occur. This functionality supports consistency and governance at the reporting layer but does not extend to upstream transformations in Lakehouse tables, KQL databases, or other data pipelines. As a result, Power BI lineage provides a limited, platform-specific view rather than a holistic, enterprise-level picture of data flow and dependencies.

Microsoft Purview fills these gaps by providing a unified, enterprise-wide data governance solution. Purview catalogs datasets across Lakehouse, Warehouse, KQL databases, and semantic models, creating a central repository of metadata. It tracks data lineage end-to-end, capturing transformations, dependencies, and usage across all platforms. Purview also enforces governance policies, supports auditing, and ensures regulatory compliance. By integrating with multiple services, it allows organizations to monitor and manage data flow comprehensively, providing insights into where data originates, how it is transformed, and how it is consumed downstream.

This enterprise-wide visibility enables better decision-making, ensures consistent application of policies, and reduces risk associated with inconsistent or ungoverned data. Organizations can maintain accurate lineage, monitor compliance, and establish a single source of truth across all datasets, pipelines, and reports. Purview transforms data governance from a fragmented set of tools into a cohesive, integrated framework, making it possible to maintain control over complex data ecosystems at scale.

while Dataflow monitoring, Warehouse audit logs, and Power BI lineage provide localized insights, they fall short of delivering a full, enterprise-wide view. Microsoft Purview addresses these limitations by combining lineage tracking, transformation recording, governance, auditing, and compliance enforcement across all Fabric services. This ensures comprehensive visibility, consistent policy enforcement, and reliable oversight of data flow and usage across the organization.

Question 81

You need to implement a data pipeline in Microsoft Fabric that can handle both batch and streaming sources while ensuring retries, fault tolerance, and orchestration of dependent tasks. Which solution should you use?

A) Dataflow Gen2
B) Synapse Pipelines
C) Spark notebooks
D) KQL database ingestion rules

Correct Answer: B)

Dataflow Gen2 provides low-code transformations and incremental refresh but does not orchestrate multiple dependent tasks, handle retries, or manage fault tolerance for complex pipelines. Spark notebooks are excellent for distributed computation and transformations but cannot orchestrate multiple batch and streaming sources with retry logic by themselves. KQL database ingestion rules are designed for ingesting streaming or event-based data into KQL databases but are limited to single-sink operations and cannot coordinate batch workloads. Synapse Pipelines provide a complete orchestration framework capable of handling both batch and streaming sources. They manage dependencies, implement retry and error-handling mechanisms, allow scheduling, and provide monitoring. By integrating with Dataflows, Spark notebooks, Lakehouse, Warehouse, and KQL databases, Synapse Pipelines ensure reliable, fault-tolerant data ingestion and transformation workflows across the enterprise.

Question 82

A company wants to provide analysts with curated datasets in Power BI while enforcing row-level security and reusable measures. Which feature should be implemented?

A) Direct Lakehouse access
B) Warehouse semantic model
C) CSV exports to Excel
D) KQL database dashboards

Correct Answer: B)

In enterprise analytics environments, providing direct access to Lakehouse tables might initially appear convenient for analysts who want immediate insights. However, exposing raw tables comes with significant risks. Direct access bypasses governance controls and exposes sensitive or uncurated data, which can result in policy violations and potential regulatory noncompliance. Moreover, when multiple users access raw datasets independently, the risk of inconsistent analysis rises, as different interpretations or calculations may be applied across teams. The lack of standardization and control in this scenario undermines the reliability and trustworthiness of analytical outputs, making it difficult for organizations to enforce enterprise-wide standards.

Many organizations attempt to mitigate these risks by exporting curated data to CSV files for offline analysis or sharing. While CSV exports offer a quick and familiar way to work with data, they introduce other limitations. These exports produce static snapshots that cannot reflect real-time updates, meaning analysts often work with outdated information. Additionally, CSV files lack interactivity, preventing users from performing dynamic filtering, slicing, or drill-down analysis. They also fail to support row-level security or governance controls, creating potential exposure risks and inconsistencies in analysis. Reusing measures or maintaining consistency across multiple CSV exports is cumbersome, as each file exists as a separate copy with its own potential variations. This fragmented approach does not scale well for enterprise analytics and can lead to operational overhead and confusion among analysts.

KQL dashboards, designed primarily for streaming and log analytics, provide a more dynamic view of incoming data. They excel at near real-time monitoring and event-driven analysis, making them ideal for operational or telemetry-based use cases. However, KQL dashboards are not designed to support enterprise analytics that rely on reusable, business-friendly measures or semantic modeling. They do not provide a structured abstraction layer over curated datasets, and they lack the governance features necessary for consistent, secure reporting. Without semantic modeling, users must build calculations and metrics individually, increasing the risk of inconsistencies and reducing the efficiency of report development.

Warehouse semantic models address these limitations by creating a secure, governed, and reusable layer over curated datasets. These models abstract raw tables into business-friendly dimensions, measures, and relationships, allowing analysts to explore and analyze data interactively without needing direct access to underlying raw tables. Row-level security ensures that sensitive data is appropriately protected, while reusable measures guarantee that calculations remain consistent across multiple reports and dashboards. By centralizing definitions and metrics, semantic models provide a single source of truth, which reduces errors, promotes consistency, and enforces enterprise-wide standards.

In addition to improving governance and security, semantic models enhance performance and scalability. Queries against curated models are optimized, reducing load on underlying storage while delivering high-performance analytics for large datasets. Analysts can interactively explore data in Power BI, create dashboards, and generate insights confidently, knowing that the data they access is curated, secure, and compliant with enterprise policies. This combination of governance, consistency, and performance makes semantic models an essential component of enterprise analytics, providing a robust, reusable framework for reporting and decision-making.

while direct Lakehouse access, CSV exports, and KQL dashboards serve limited purposes, they are insufficient for enterprise-grade analytics. Warehouse semantic models provide a secure, governed, and reusable abstraction layer that ensures row-level security, consistent metrics, and optimized performance, enabling analysts to interactively explore curated datasets while maintaining compliance, efficiency, and trustworthiness across the organization.

Question 83

You need to optimize query performance on a Lakehouse table that receives frequent micro-batches of data, resulting in millions of small files. Which solution is most effective?

A) Incremental refresh in Dataflow
B) Auto-optimize and file compaction
C) Export to CSV
D) KQL database views

Correct Answer: B)

Incremental refresh improves Dataflow performance but does not resolve small-file accumulation in Lakehouse tables. Exporting to CSV adds more files, increasing metadata overhead and reducing performance. KQL database views abstract queries but do not optimize the underlying file layout. Auto-optimize merges small files into larger optimized files, reduces metadata overhead, improves query latency, and maintains Delta Lake performance. Combined with Z-ordering and partitioning, auto-optimize ensures efficient query execution and better resource utilization on continuously ingested datasets. This approach directly addresses performance degradation caused by small-file accumulation.

Question 84

A data engineering team wants to implement a medallion architecture in Fabric. Raw data is in JSON format, cleaned data must enforce schema, and curated data should support analytics. Which storage format is most suitable?

A) CSV
B) Parquet
C) Delta Lake
D) JSON

Correct Answer: C)

CSV is row-based, lacks ACID compliance, schema enforcement, and versioning, making it unsuitable for medallion architecture. Parquet provides columnar storage and query performance but does not natively support ACID transactions or incremental merges. JSON is flexible for raw data but inefficient for analytics and lacks ACID transactions. Delta Lake combines columnar storage with ACID transactions, schema enforcement, time travel, and incremental updates via MERGE. It efficiently supports raw, cleaned, and curated layers, ensures historical tracking, reliability, and high-performance querying. Delta Lake integrates with Lakehouse pipelines and enables enterprise-scale medallion architecture implementation with consistency and reliability.

Question 85

You need to track data lineage, transformations, and dependencies across Lakehouse, Warehouse, and KQL databases in Microsoft Fabric for auditing and compliance. Which service should be used?

A) Dataflow monitoring
B) Microsoft Purview
C) Warehouse audit logs
D) Power BI lineage

Correct Answer: B)

Dataflow monitoring provides logs for individual Dataflows but cannot capture enterprise-wide lineage or transformation tracking. Warehouse audit logs track query activity within a single Warehouse and do not cover Lakehouse or KQL databases. Power BI lineage tracks datasets and reports but does not provide end-to-end lineage across all Fabric services. Microsoft Purview offers enterprise-wide governance, catalogs datasets, tracks lineage, records transformations and dependencies, enforces policies, and supports auditing and compliance. It integrates with Lakehouse, Warehouse, KQL databases, and semantic models, providing full visibility into data flow, usage, and governance across the organization.

Question 86

You need to ingest large volumes of streaming IoT data into Microsoft Fabric while enabling near real-time analytics. Which approach is most suitable?

A) Batch ingestion into Lakehouse with Power BI import
B) Eventstream ingestion into KQL database with DirectQuery
C) Dataflow scheduled refresh into Warehouse
D) Spark notebook outputs to CSV

Correct Answer: B)

Batch ingestion into Lakehouse with Power BI import introduces latency because data is only available after each batch load, which is unsuitable for real-time analytics. Dataflow scheduled refresh also uses batch processing, leading to delayed availability of streaming data. Spark notebook outputs to CSV require manual ingestion and cannot handle continuous data streams efficiently. Eventstream ingestion allows continuous streaming of IoT data into a KQL database, ensuring low-latency availability. DirectQuery from Power BI enables analysts to query the data immediately without duplicating datasets, providing near real-time dashboards. This solution efficiently handles high-volume streaming data, ensures scalability, and maintains governance while providing low-latency insights for analysts.

Question 87

A data engineering team needs to perform distributed Python-based feature engineering on terabyte-scale datasets in Fabric. Which compute environment is most appropriate?

A) Warehouse T-SQL
B) Spark notebooks
C) Dataflow Gen2
D) KQL queries

Correct Answer: B)

Warehouse T-SQL is optimized for relational queries and cannot efficiently execute Python-based computations at scale. Dataflow Gen2 supports low-code transformations but does not handle large-scale Python-based feature engineering. KQL queries are designed for analytics over log or streaming data and do not support Python-based distributed processing. Spark notebooks are designed for distributed computation and support Python, PySpark, and Scala. They allow processing of terabyte-scale datasets in parallel, caching intermediate results, scaling compute dynamically, and integrating seamlessly with Lakehouse tables and pipelines. Spark notebooks are ideal for large-scale feature engineering, enabling efficient computation and distributed data processing workflows.

Question 88

You need to provide analysts with curated datasets in Power BI that enforce row-level security, reusable measures, and semantic modeling. Which feature should you implement?

A) Direct Lakehouse access
B) Warehouse semantic model
C) CSV exports to Excel
D) KQL database dashboards

Correct Answer: B)

Direct access to Lakehouse tables exposes raw data, risking governance violations and performance issues. CSV exports are static and lack interactivity, reusability, or row-level security. KQL dashboards focus on log or streaming analytics and do not support reusable measures or semantic modeling. A Warehouse semantic model provides a secure abstraction over curated datasets, enforcing row-level security, relationships, and reusable measures. Analysts can explore curated datasets interactively without accessing raw data, ensuring consistency, governance, and performance across multiple reports. Semantic models provide enterprise-wide standardization and are optimized for analytics in Power BI.

Question 89

A Lakehouse table receives frequent micro-batches, resulting in millions of small files that degrade query performance. What is the most effective solution?

A) Incremental refresh in Dataflow
B) Auto-optimize and file compaction
C) Export data to CSV
D) KQL database views

Correct Answer: B)

In modern Lakehouse architectures, efficiently managing data storage and query performance is essential, especially as datasets grow in size and complexity. One persistent challenge in these environments is the accumulation of small files, which can degrade query performance, increase metadata overhead, and place unnecessary strain on the storage and query engine. While several approaches exist to manage data ingestion and processing, each has limitations when it comes to addressing the small-file problem at scale.

Incremental refresh in Dataflow is a widely used feature that improves execution performance by refreshing only new or changed data rather than the entire dataset. This approach reduces processing time and system load during refresh operations, making it a useful optimization for recurring Dataflows. However, incremental refresh does not address the root cause of small-file accumulation in Lakehouse tables. As data is ingested and processed, numerous small files can still be generated, fragmenting storage and increasing the burden on query planning and metadata management. This fragmentation ultimately limits the effectiveness of incremental refresh in improving overall query performance.

Exporting data to CSV files is another common practice, often used to share snapshots or feed downstream processes. While exporting allows for offline access or integration with external systems, it introduces additional small files with each export. Over time, the growing number of files contributes to metadata overhead, complicates query execution, and slows down analytical workloads. Large datasets exacerbate this problem, as the system must manage an increasingly fragmented storage structure, making queries slower and less efficient.

KQL database views offer query abstraction and can simplify access to data by providing a logical layer over the physical tables. Views are useful for structuring queries, maintaining consistency, and managing dependencies in analytical workflows. However, they do not optimize the underlying storage or consolidate small files. Queries executed against these views still operate over the fragmented dataset, meaning that metadata overhead and file management challenges persist. While views improve usability and maintainability at the query level, they are not a solution to small-file performance issues in Lakehouse environments.

Auto-optimize provides a comprehensive solution to these challenges. This functionality automatically merges small files into larger, optimized files, reducing metadata overhead and improving query latency. By consolidating fragmented storage, auto-optimize allows queries to scan fewer files, which significantly improves performance and efficiency. Delta Lake maintains its performance characteristics even as data is continuously ingested, ensuring that the storage remains optimized without requiring manual intervention.

When combined with partitioning and Z-ordering, auto-optimize delivers further enhancements. Partitioning organizes data into logical segments, enabling queries to target only relevant partitions, reducing scan times and improving efficiency. Z-ordering clusters related data within partitions, improving the speed of selective queries and optimizing resource utilization. Together, these techniques create a high-performance environment that can handle continuously ingested data, scale efficiently, and maintain consistent query performance.

while incremental refresh in Dataflows, CSV exports, and KQL views offer operational and organizational benefits, they do not directly address small-file accumulation. Auto-optimize, complemented by partitioning and Z-ordering, provides a robust, enterprise-ready solution. It consolidates files, reduces metadata overhead, improves query latency, and maintains Delta Lake performance, ensuring that Lakehouse environments remain efficient, scalable, and capable of supporting high-performance analytics on large, continuously ingested datasets.

Question 90

You need to track data lineage, transformations, and dependencies across Lakehouse, Warehouse, and KQL databases for auditing and compliance. Which service should you implement?

A) Dataflow monitoring
B) Microsoft Purview
C) Warehouse audit logs
D) Power BI lineage

Correct Answer: B)

In complex enterprise data environments, understanding how data moves, transforms, and is consumed across systems is critical for governance, compliance, and operational efficiency. While several tools provide monitoring or lineage capabilities, most operate in isolation and offer only partial visibility, leaving gaps in enterprise-wide data management.

Dataflow monitoring is one such tool that provides detailed execution logs for individual Dataflows. These logs capture refresh status, execution times, and errors, which are valuable for operational oversight and troubleshooting. Analysts and data engineers can use this information to understand which Dataflows ran successfully, identify failures, and optimize refresh performance. However, Dataflow monitoring is inherently limited to the scope of individual Dataflows. It does not track data lineage across multiple datasets or systems, nor does it record the transformations applied along the data lifecycle. As a result, while Dataflow monitoring supports operational visibility, it cannot provide the comprehensive, enterprise-wide context needed to manage dependencies, transformations, and governance consistently.

Warehouse audit logs offer another perspective by recording query activity and user interactions within a relational data warehouse component. These logs are useful for security monitoring, tracking user activity, and ensuring compliance at the table or component level. They can help organizations understand who queried what data and when, and they support auditing requirements. Despite these benefits, audit logs are restricted to a single Warehouse instance and cannot capture lineage or dependencies across distributed datasets or other services. This limitation means that organizations cannot rely solely on Warehouse audit logs to gain a complete view of data movement and transformations across the enterprise.

Power BI lineage provides insight into dependencies within the Power BI ecosystem. Analysts can see which datasets feed into specific reports and dashboards, and how changes in one dataset may impact downstream reports. This capability is valuable for impact analysis, maintaining report consistency, and understanding relationships within Power BI. Nevertheless, Power BI lineage is confined to the reporting layer and does not extend to upstream sources, such as Lakehouse tables or KQL databases. It does not capture transformations, ingestion pipelines, or cross-service dependencies, leaving gaps in visibility for enterprise-scale governance.

Microsoft Purview addresses these challenges by providing a unified, enterprise-wide data governance solution. Purview catalogs datasets across Lakehouse, Warehouse, KQL databases, and semantic models, providing a single source of truth for metadata and lineage. It tracks data lineage end-to-end, records transformations and dependencies, and enforces governance policies consistently across all platforms. Purview also supports auditing, compliance reporting, and operational oversight, allowing organizations to monitor data usage, detect policy violations, and maintain regulatory compliance. By integrating with multiple services, Purview provides full visibility into how data flows, transforms, and is consumed, ensuring that organizations can manage their data ecosystem efficiently and securely.

while Dataflow monitoring, Warehouse audit logs, and Power BI lineage offer localized insights into execution, query activity, and report dependencies, they provide only a partial view of enterprise data operations. Microsoft Purview delivers a holistic solution, combining lineage, transformations, governance, and auditing across all Fabric services. This integration ensures consistent enterprise-wide visibility, supports compliance, and enables organizations to maintain control over data flows and transformations at scale.

Microsoft DP-700 Implementing Data Engineering Solutions Using Microsoft Fabric Exam Dumps and Practice Test Questions Set 6 Q76-90

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