Microsoft DP-700 Implementing Data Engineering Solutions Using Microsoft Fabric Exam Dumps and Practice Test Questions Set 7 Q91-105
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Question 91
You need to implement a medallion architecture in Microsoft Fabric. Raw JSON data is ingested, 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) Delta Lake
Explanation:
In modern enterprise data architectures, choosing the appropriate storage format is essential to support reliability, performance, and maintainability, particularly when implementing medallion architectures that involve raw, cleansed, and curated layers. CSV files, while widely used and simple to generate, are inherently limited in their applicability for production-scale data workflows. Being row-based, they do not provide ACID compliance, which means that concurrent updates or partial failures can lead to inconsistent or corrupted datasets. CSV files also lack schema enforcement, so structural changes in data require manual validation, and there is no built-in mechanism for historical versioning. These shortcomings make CSV an inefficient and risky choice for enterprise environments that require governance, reproducibility, and incremental data updates.
Parquet, a columnar storage format, addresses some of the limitations of CSV by enabling highly efficient analytics. Columnar storage allows queries to read only the relevant columns, reducing I/O and improving performance for analytical workloads. Parquet also offers compression advantages, which lowers storage requirements and accelerates query execution. Despite these benefits, Parquet does not natively support ACID transactions or incremental merges. In continuously ingested datasets, maintaining up-to-date views of the data requires additional tools or processes to handle inserts, updates, and deletions. Without transactional guarantees and built-in support for incremental transformations, Parquet alone is insufficient for robust medallion architectures.
JSON files provide flexibility for handling raw or semi-structured data, making them useful for initial ingestion when datasets may have inconsistent formats. JSON’s schema-less structure allows the storage of diverse data types without prior definition. However, JSON has significant limitations for enterprise analytics. It is verbose and inefficient for large-scale querying, lacks transactional guarantees, and does not support schema evolution or versioning. These deficiencies limit its suitability for intermediate or curated layers in medallion architectures, where data consistency, governance, and incremental processing are essential.
Delta Lake addresses these challenges by combining the performance benefits of columnar storage with robust transactional and governance features. Delta Lake supports ACID transactions, ensuring data consistency even in high-concurrency environments. It enforces schema management, preventing invalid or inconsistent records from entering the datasets. Time travel functionality allows users to query historical versions of tables for auditing, debugging, or reproducing results. Delta Lake also supports incremental updates through MERGE operations, enabling efficient handling of inserts, updates, and deletes without rewriting entire tables. This ensures that raw, cleaned, and curated layers can be maintained reliably and efficiently.
Additionally, Delta Lake integrates seamlessly with Lakehouse pipelines, Spark notebooks, and other data processing frameworks, providing an end-to-end solution for enterprise-scale workflows. By combining high-performance querying, ACID compliance, schema enforcement, historical tracking, and incremental processing, Delta Lake delivers the reliability, scalability, and governance required for medallion architectures. It ensures that analytical workloads are performant, datasets remain consistent, and historical data is always accessible, making it the preferred solution for modern enterprise data platforms.
Question 92
A data engineering team wants to perform incremental ingestion of data from multiple sources while ensuring that only new or changed records are processed. Which approach is most efficient?
A) Full table overwrite
B) Delta Lake MERGE operations
C) Copy all data into a CSV table
D) KQL database append
Correct Answer: B) Delta Lake MERGE operations
Explanation:
Full table overwrite is inefficient for large datasets, consumes excessive compute and storage, and does not preserve historical versions. Copying all data into a CSV table duplicates entire datasets, lacks ACID compliance, and increases operational overhead. KQL database append adds only new records and cannot update existing ones or handle deletions. Delta Lake MERGE operations allow comparing incoming records with existing data to perform inserts, updates, and deletes in a single atomic transaction while preserving history. MERGE ensures data integrity, supports schema evolution, handles large-scale datasets efficiently, and enables incremental ingestion workflows. This makes it the most efficient method for incremental data processing.
Question 93
You need 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) Warehouse semantic model
Explanation:
Direct Lakehouse access exposes raw data, risking governance violations and inconsistent analysis. CSV exports provide static snapshots without interactivity, reusability, or row-level security. KQL dashboards are optimized for streaming or log analytics and do not support reusable measures or semantic modeling. Warehouse semantic models provide a secure, governed, and reusable abstraction layer over curated datasets. They enforce row-level security, relationships, and reusable measures. Analysts can explore curated datasets interactively without accessing raw data, ensuring performance, governance, and consistency across multiple Power BI reports. Semantic models support enterprise-wide standardization and high-performance analytics.
Question 94
A Lakehouse table receives frequent micro-batches, creating millions of small files and degrading query performance. Which approach 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) Auto-optimize and file compaction
Explanation:
Efficient management of Lakehouse tables is essential for maintaining high performance, especially in environments with continuous data ingestion. One common strategy to improve the performance of Dataflows is incremental refresh. Incremental refresh allows Dataflows to process only new or updated data rather than reprocessing the entire dataset during each refresh cycle. This significantly reduces the workload and improves refresh performance, making Dataflows faster and more resource-efficient. However, while incremental refresh addresses execution speed within Dataflows, it does not resolve a critical challenge in Lakehouse storage: the accumulation of small files. As data is ingested continuously, numerous small files are generated over time, leading to performance degradation, increased metadata overhead, and slower query execution.
Some organizations attempt to handle this challenge by exporting data into CSV files for downstream analysis or archival purposes. Although CSV files are simple and widely supported, exporting data in this manner often exacerbates the small-file problem. Each CSV file is a separate object, and repeated exports create additional small files in the Lakehouse. This proliferation of files increases the load on the metadata layer, slows down queries, and consumes additional storage. Moreover, CSV files are static snapshots, meaning they do not reflect ongoing changes in the underlying data. Consequently, relying on CSV exports for analytics or reporting introduces inefficiencies and operational complexity without addressing the root cause of performance issues.
KQL database views provide another option for interacting with datasets, particularly for log or streaming data. Views enable analysts to abstract queries over the underlying tables, allowing for flexible reporting and aggregation. However, KQL views do not modify the underlying file layout or consolidate fragmented files. While they are valuable for real-time monitoring or operational analytics, they do not optimize storage or reduce metadata overhead. As a result, the small-file issue persists, and query performance over large, continuously ingested datasets remains suboptimal.
The most effective solution for addressing these performance challenges in Lakehouses is Delta Lake’s auto-optimize feature. Auto-optimize automatically consolidates small files into larger, optimized files, reducing the total number of files and lowering metadata overhead. This improves query latency because fewer files need to be scanned during query execution, enhancing overall system performance. Auto-optimize ensures that Delta Lake tables maintain high performance even under continuous data ingestion, eliminating the need for manual intervention or complex file management procedures.
When combined with additional strategies such as partitioning and Z-ordering, auto-optimize provides further performance gains. Partitioning organizes data into logical segments, allowing queries to scan only relevant subsets of data, while Z-ordering clusters related data together, reducing the number of files that must be read for common query patterns. Together, these optimizations ensure efficient query execution, better resource utilization, and consistent performance over time. By automatically addressing the root cause of small-file accumulation, Delta Lake’s auto-optimize, along with partitioning and Z-ordering, provides a scalable, high-performance solution for enterprise Lakehouse environments, ensuring that continuous ingestion does not compromise analytics speed or efficiency.
Question 95
You need to track data lineage, transformations, and dependencies across Lakehouse, Warehouse, and KQL databases for compliance. Which service should you implement?
A) Dataflow monitoring
B) Microsoft Purview
C) Warehouse audit logs
D) Power BI lineage
Correct Answer: B) Microsoft Purview
Explanation:
In today’s enterprise data ecosystems, maintaining visibility into how data flows, transforms, and is used across the organization is essential for governance, compliance, and operational efficiency. While several tools provide partial insights into data operations, many are limited in scope and do not offer a comprehensive view of enterprise-wide lineage. Dataflow monitoring, for instance, provides detailed execution logs for individual Dataflows, including refresh schedules, transformation steps, and error tracking. This information is helpful for operational monitoring and troubleshooting specific workflows but is restricted to single Dataflows. It does not capture dependencies across multiple services or track the movement and transformation of datasets throughout the broader enterprise ecosystem, limiting its effectiveness for enterprise governance.
Warehouse audit logs provide another layer of insight by tracking query activity within the Warehouse component. Administrators can use these logs to identify which datasets are being accessed, analyze usage patterns, and troubleshoot performance issues. However, Warehouse audit logs are confined to a single component and do not monitor interactions with other key data services such as Lakehouse tables, KQL databases, or downstream reporting tools. This lack of cross-service visibility creates gaps in enterprise-wide lineage, leaving organizations without a complete understanding of how data is consumed, transformed, and shared across multiple systems.
Power BI lineage is designed to track relationships between reports, datasets, and dashboards within the Power BI environment. It enables users to understand which datasets feed into specific reports and how metrics are calculated within dashboards. While this is valuable for analytics lineage within Power BI, it does not extend to upstream data sources or transformations in other Fabric services. Dependencies on Lakehouse tables, KQL databases, or external pipelines are not captured, which limits the organization’s ability to fully manage data governance and assess the downstream impact of changes to datasets or transformations.
Microsoft Purview addresses these limitations by providing a unified, enterprise-wide data governance solution. Purview catalogs datasets across all Fabric services, including Lakehouse, Warehouse, KQL databases, and semantic models. It tracks lineage end-to-end, recording how data moves, transforms, and is consumed across the organization. Transformations, dependencies, and data relationships are automatically captured, enabling teams to understand the full lifecycle of datasets. Purview also enforces governance policies, manages classifications, and supports auditing and compliance, ensuring that organizational standards are consistently applied to all data assets. Its integration across multiple services provides a single source of truth for data movement, usage, and lineage, allowing organizations to maintain transparency, accountability, and regulatory compliance.
By consolidating monitoring, lineage tracking, policy enforcement, and auditing into a centralized platform, Microsoft Purview offers a comprehensive approach to enterprise data governance. Unlike Dataflow monitoring, Warehouse audit logs, or Power BI lineage alone, Purview provides complete visibility into data flows, transformations, and dependencies across the entire data ecosystem. Organizations gain the ability to perform impact analysis, troubleshoot downstream issues, and maintain compliance at scale, all while ensuring that governance policies are applied consistently. This end-to-end visibility makes Microsoft Purview the definitive solution for managing lineage, transformations, and compliance in modern enterprise data environments.
Question 96
You need to implement a data pipeline in Microsoft Fabric that handles both batch and streaming sources while providing fault tolerance, retry logic, and orchestration of dependent tasks. Which solution is most appropriate?
A) Dataflow Gen2
B) Synapse Pipelines
C) Spark notebooks
D) KQL database ingestion rules
Correct Answer: B) Synapse Pipelines
Explanation:
Dataflow Gen2 is optimized for low-code transformations and incremental refresh but cannot orchestrate complex pipelines with multiple dependent tasks, retry mechanisms, or fault-tolerant execution. Spark notebooks are suitable for distributed computation and transformations but lack orchestration capabilities for end-to-end pipelines that involve multiple batch and streaming sources. KQL database ingestion rules are designed for streaming ingestion into KQL databases but cannot manage batch workloads or orchestrate dependent tasks. Synapse Pipelines provide a robust orchestration framework that handles batch and streaming data, supports retry logic, manages dependencies, and provides monitoring and fault tolerance. It can orchestrate Dataflows, Spark notebooks, and ingestion tasks across Lakehouse, Warehouse, and KQL databases, ensuring enterprise-scale, reliable, and fault-tolerant data pipelines.
Question 97
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 access to Lakehouse tables
B) Warehouse semantic model
C) CSV exports to Excel
D) KQL database dashboards
Correct Answer: B) Warehouse semantic model
Explanation:
In large-scale enterprise analytics environments, granting direct access to raw Lakehouse tables may seem like the most straightforward approach for analysts, but it carries significant drawbacks. Raw tables contain uncurated data that can include incomplete, inconsistent, or sensitive records. Allowing unrestricted access to these tables creates risks for governance violations and may compromise data security. Analysts working directly with raw data may inadvertently bypass organizational policies, resulting in inconsistent reporting and potential exposure of sensitive information. Furthermore, querying raw tables can negatively impact performance, particularly when multiple users simultaneously access large datasets, leading to slower response times and higher resource consumption.
A common workaround is exporting datasets to CSV files for analysis. While CSV exports are easy to create and widely supported, they offer limited functionality for enterprise-scale analytics. CSV files provide static snapshots that do not update automatically when underlying data changes, requiring repeated manual refreshes. They lack interactivity, preventing analysts from exploring data dynamically or drilling down into insights. Additionally, CSV files do not support row-level security, leaving sensitive information exposed to unauthorized users. Reusable business logic, such as standardized measures and relationships, cannot be defined in CSV, which increases redundancy and the risk of inconsistencies across reports. Overall, CSV-based workflows are difficult to scale and do not meet the governance or performance requirements of enterprise analytics.
KQL dashboards offer another method for accessing data, particularly for streaming or log analytics. These dashboards are optimized for real-time monitoring and operational insights, making them useful for scenarios where continuous data ingestion and rapid query response are required. However, KQL dashboards do not provide a semantic modeling layer. They do not support reusable measures, pre-defined relationships between tables, or business-friendly abstractions of the data. As a result, analysts cannot consistently rely on KQL dashboards for enterprise-scale reporting, and building standardized, reusable analytics across multiple teams becomes challenging.
The most robust solution for enterprise analytics is a Warehouse semantic model. Semantic models provide a secure, governed abstraction layer over curated datasets, enabling analysts to interact with data without accessing raw tables directly. These models enforce row-level security, ensuring that users can only view data they are authorized to see. They also support reusable measures, predefined calculations, and relationships between tables, which promote consistency across reports. Analysts can explore curated datasets interactively, creating multiple Power BI reports from the same trusted data sources without duplicating effort.
In addition to security and interactivity, semantic models maintain enterprise-wide standardization by creating a single source of truth for analytics. Changes to underlying data, measures, or relationships automatically propagate to all dependent reports, ensuring accuracy and consistency across the organization. Optimized storage and query design within the semantic model also improve performance, allowing analysts to work with large datasets efficiently. By providing a governed, reusable, and high-performance framework, Warehouse semantic models address the limitations of direct Lakehouse access, CSV exports, and KQL dashboards, making them the ideal approach for enterprise-scale reporting and analytics.
Question 98
You need to optimize query performance on a Lakehouse table that receives frequent micro-batches resulting in millions of small files. Which approach is most effective?
A) Incremental refresh in Dataflow
B) Auto-optimize and file compaction
C) Export data to CSV
D) KQL database views
Correct Answer: B) Auto-optimize and file compaction
Explanation:
Efficient data management and query performance are critical considerations for Lakehouse architectures, particularly when dealing with continuously ingested datasets. One common approach to improving performance in Dataflows is incremental refresh. By processing only newly added or updated data instead of reprocessing the entire dataset, incremental refresh reduces execution times and conserves computational resources. This approach is particularly valuable for mid-sized datasets or frequent refresh cycles, as it allows Dataflows to run more efficiently and reduces the overall load on the system. However, while incremental refresh improves Dataflow performance, it does not address one of the most persistent challenges in Lakehouse environments: the accumulation of small files.
Over time, continuous ingestion of data generates numerous small files. This proliferation of files can significantly degrade query performance because each query must process multiple file objects, increasing I/O overhead and straining the metadata layer. One approach organizations sometimes attempt is exporting data to CSV files. While CSVs are easy to generate and widely supported, each export produces additional files, further contributing to small-file accumulation. Additionally, CSVs lack optimization for large-scale querying, and because they are static snapshots, they fail to support incremental updates effectively. This approach introduces additional metadata overhead and slows down query execution, making it an inefficient solution for high-performance analytics.
KQL views offer another option by providing query abstraction over datasets, enabling analysts to interact with data without modifying the underlying tables. KQL views are useful for building dashboards or pre-aggregating log and streaming data for real-time analysis. However, they do not optimize the storage layout of the underlying files. Queries still have to scan numerous small files individually, leaving the performance degradation caused by fragmented storage unresolved. While KQL views improve query manageability and flexibility, they are insufficient for addressing the underlying performance challenges associated with small-file accumulation in Lakehouses.
The most effective solution for mitigating small-file issues and maintaining high-performance querying is Delta Lake’s auto-optimize feature. Auto-optimize automatically merges small files into larger, optimized files, reducing the number of files that the system must manage and thereby lowering metadata overhead. This consolidation improves query latency, as fewer files need to be scanned, and it maintains consistent Delta Lake performance even under continuous data ingestion.
When combined with additional optimization strategies such as partitioning and Z-ordering, auto-optimize provides substantial performance benefits. Partitioning organizes data into logical segments, allowing queries to scan only the relevant partitions and avoid reading unnecessary data. Z-ordering clusters related records together, improving the efficiency of range queries and minimizing file scans for common query patterns. Together, these features ensure that continuously ingested data remains efficiently organized and highly performant, while also optimizing resource utilization and reducing the operational burden on administrators.
By addressing the root cause of small-file accumulation, Delta Lake’s auto-optimize, in combination with partitioning and Z-ordering, delivers a scalable, reliable, and high-performance solution for enterprise Lakehouse environments. Unlike incremental refresh, CSV exports, or KQL views alone, this approach ensures that queries execute efficiently on large datasets, supports continuous ingestion, and maintains consistent performance for analytics workloads, making it the preferred solution for modern data platforms.
Question 99
A data engineering team wants to implement a medallion architecture. Raw data is ingested as JSON, cleaned data must enforce schema, and curated data should support analytics. Which storage format is ideal?
A) CSV
B) Parquet
C) Delta Lake
D) JSON
Correct Answer: C) Delta Lake
Explanation:
CSV files are row-based and lack ACID compliance, schema enforcement, and historical versioning, making them unsuitable for medallion architectures. Parquet provides columnar storage and improves query performance but does not natively support ACID transactions or incremental merges. JSON is suitable for raw semi-structured data but is inefficient for analytics, lacks transactional guarantees, and cannot enforce schema reliably. Delta Lake combines columnar storage with ACID transactions, schema enforcement, time travel, and incremental updates via MERGE operations. It efficiently supports raw, cleaned, and curated layers, ensuring reliability, historical tracking, and high-performance querying. Delta Lake integrates seamlessly with Lakehouse pipelines, enabling enterprise-scale medallion architecture with consistency and governance.
Question 100
You need to track data lineage, transformations, and dependencies across Lakehouse, Warehouse, and KQL databases in Microsoft Fabric for compliance. Which service should you implement?
A) Dataflow monitoring
B) Microsoft Purview
C) Warehouse audit logs
D) Power BI lineage
Correct Answer: B) Microsoft Purview
Explanation:
In today’s complex enterprise data environments, understanding how data flows, transforms, and is consumed across multiple platforms is critical for ensuring compliance, governance, and operational efficiency. While various tools provide monitoring and lineage capabilities, most operate within limited scopes and do not offer a comprehensive, enterprise-wide view of data operations.
Dataflow monitoring is one such tool that provides detailed execution logs for individual Dataflows. These logs capture valuable information such as refresh schedules, execution times, success or failure statuses, and performance metrics. Analysts and data engineers can leverage this information to troubleshoot errors, optimize refresh cycles, and maintain operational visibility for specific Dataflows. However, the scope of Dataflow monitoring is confined to individual flows and does not extend to tracking how data moves across multiple datasets, services, or transformations. It lacks the ability to provide end-to-end lineage or insight into how changes in one dataset may impact downstream analytics, making it insufficient for enterprise-wide governance.
Warehouse audit logs offer another layer of operational visibility by recording queries, user activity, and table-level interactions within a single Warehouse instance. These logs are useful for monitoring usage patterns, detecting unauthorized access, and supporting compliance within that specific component. While Warehouse audit logs provide valuable information about query activity, they are limited to the scope of a single component and cannot capture dependencies, transformations, or data movement across other services or platforms. This constraint makes it difficult for organizations to understand the full lifecycle of data across the enterprise.
Power BI lineage provides visibility into datasets and reports within the Power BI ecosystem. Analysts and report authors can see which datasets feed into particular dashboards and visualize dependencies between data and reports. This capability is useful for impact analysis and maintaining consistency in reporting. However, Power BI lineage is primarily focused on the reporting layer and does not extend to upstream sources such as Lakehouse tables or KQL databases. It cannot capture the transformations applied during data ingestion or processing, leaving critical gaps in enterprise-wide visibility.
Microsoft Purview addresses these limitations by offering a unified, enterprise-wide governance solution. Purview catalogs datasets across Lakehouse, Warehouse, KQL databases, and semantic models, providing a centralized repository of metadata. It tracks lineage end-to-end, capturing how data flows, transforms, and is consumed across all platforms. Purview also records transformations and dependencies, enforces governance policies, and supports auditing and compliance requirements. By providing full visibility into data movement and usage, it allows organizations to monitor how changes in one dataset may impact other systems, ensuring traceability and accountability across the enterprise.
Beyond lineage and governance, Purview integrates seamlessly with operational and analytical workflows to support consistent, reliable decision-making. Organizations can implement enterprise-wide policies, detect and remediate compliance violations, and maintain a single source of truth for all data assets. By centralizing data governance and lineage tracking, Purview enables stakeholders to manage complex data ecosystems efficiently, reduce operational risk, and ensure that all data processes align with organizational standards.
while Dataflow monitoring, Warehouse audit logs, and Power BI lineage provide localized insights into execution, query activity, and reporting dependencies, they are insufficient for enterprise-wide governance. Microsoft Purview delivers a holistic solution, combining metadata management, end-to-end lineage, transformation tracking, policy enforcement, and auditing across all Fabric services. This unified approach ensures complete visibility, compliance, and governance across the organization, providing the foundation for secure, traceable, and well-managed data operations.
Question 101
You need to implement a data pipeline in Microsoft Fabric that can process both batch and streaming sources while ensuring retry logic, fault tolerance, and orchestration. Which solution should you use?
A) Dataflow Gen2
B) Synapse Pipelines
C) Spark notebooks
D) KQL database ingestion rules
Correct Answer: B) Synapse Pipelines
Explanation:
In today’s enterprise data ecosystems, maintaining clear visibility into how data flows, transforms, and is consumed is essential for governance, compliance, and operational efficiency. Organizations rely on a variety of tools to monitor data activities, but most solutions are limited to specific components or services and fail to provide a comprehensive, end-to-end perspective.
Dataflow monitoring is commonly used to track the execution of individual Dataflows. It provides logs detailing refresh schedules, transformation steps, and error occurrences, which are valuable for troubleshooting and operational oversight at the level of a single Dataflow. However, while these logs offer insight into the behavior of individual workflows, they do not provide enterprise-wide lineage. They cannot track dependencies across multiple datasets, pipelines, or services, leaving organizations with only a fragmented understanding of how data moves through their ecosystem.
Similarly, Warehouse audit logs capture query activity within the Warehouse environment. These logs allow administrators to monitor which datasets are being accessed and by whom, helping to identify usage patterns or potential performance issues. Yet, this visibility is confined to the Warehouse component and does not extend to other services such as Lakehouse tables, KQL databases, or downstream reporting platforms. The lack of cross-service lineage creates blind spots, making it difficult for organizations to understand the full impact of data changes and to enforce consistent governance across the enterprise.
Power BI lineage provides a higher level of insight within the analytics layer, showing how datasets, reports, and dashboards are interconnected. It enables users to track how data flows into reports and how calculations are applied to produce visualizations. While useful for understanding analytics dependencies within Power BI, this lineage does not extend upstream to capture transformations or relationships in Lakehouse tables, Warehouses, or KQL databases. Without a unified view across all data services, organizations cannot achieve complete transparency or enterprise-wide governance.
Microsoft Purview addresses these limitations by providing a comprehensive data governance platform that spans the entire enterprise. Purview catalogs datasets across all Fabric services, including Lakehouse, Warehouse, KQL databases, and semantic models, ensuring that all data assets are discoverable and properly classified. It tracks lineage end-to-end, capturing transformations, dependencies, and the movement of data between services. Policies and classifications can be enforced consistently, supporting compliance, auditability, and security requirements across the organization. Purview also provides centralized visibility into data usage, helping teams understand the downstream impacts of changes, manage risk, and maintain accurate, governed datasets.
By integrating across multiple services and capturing the complete lifecycle of data—from ingestion and transformation to reporting and analytics—Microsoft Purview ensures that organizations have a single, reliable source of truth for governance. Unlike Dataflow monitoring, Warehouse audit logs, or Power BI lineage alone, Purview delivers full enterprise-wide lineage, policy enforcement, and auditing capabilities. This enables organizations to maintain transparency, compliance, and operational control at scale, ensuring that data is trustworthy, properly managed, and consistently governed across all platforms.
Question 102
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) Warehouse semantic model
Explanation:
Direct access to Lakehouse tables exposes raw data, which can compromise governance and consistency. CSV exports provide static datasets without interactivity, reusability, or row-level security, making them unsuitable for analytics purposes. KQL dashboards focus on streaming or log analytics but do not support reusable measures or semantic modeling. A Warehouse semantic model provides a secure, governed, and reusable abstraction layer over curated datasets. It enforces row-level security, supports reusable measures, and defines relationships between tables. Analysts can explore curated datasets interactively without accessing raw data, ensuring performance, governance, and consistency across multiple Power BI reports. Semantic models also provide enterprise-wide standardization, acting as a single source of truth and ensuring consistent metrics across all analytics workloads.
Question 103
You need to optimize query performance on a Lakehouse table that receives frequent micro-batches, resulting in millions of small files. Which approach is most effective?
A) Incremental refresh in Dataflow
B) Auto-optimize and file compaction
C) Export data to CSV
D) KQL database views
Correct Answer: B) Auto-optimize and file compaction
Explanation:
Managing performance in Lakehouse environments requires careful attention to how data is ingested, stored, and queried. One of the common strategies used to improve the efficiency of Dataflows is incremental refresh. Incremental refresh allows Dataflows to process only newly added or updated records instead of reprocessing the entire dataset during every refresh cycle. This optimization reduces execution time and conserves computational resources, making it particularly useful for frequently updated datasets. However, while incremental refresh enhances the performance of individual Dataflows, it does not solve a critical challenge that arises in Lakehouse architectures: the accumulation of small files over time.
Continuous data ingestion often generates a large number of small files. This proliferation of files can significantly degrade query performance because each query must interact with numerous individual file objects, increasing I/O overhead and straining the metadata management system. One approach some teams attempt is exporting datasets to CSV files. Although CSV files are straightforward to create and compatible with a variety of tools, exporting data in this format exacerbates the small-file problem. Each CSV export creates additional files, and because CSV files are not optimized for analytics, queries on these files tend to be slow and inefficient. Moreover, CSV files lack incremental update mechanisms, meaning every refresh may require full data duplication, which further increases metadata overhead and degrades performance.
KQL database views are often used to provide an abstraction layer over datasets, allowing analysts to query data without directly interacting with the underlying tables. These views are particularly useful for log and streaming analytics, enabling filtered or aggregated views of data for dashboards. However, KQL views do not optimize the physical layout of data files. Queries still scan all underlying files individually, and the presence of millions of small files continues to impact performance. While these views improve usability and query organization, they do not address the root cause of small-file accumulation or optimize resource utilization.
The most effective solution for this challenge is Delta Lake’s auto-optimize feature. Auto-optimize automatically consolidates small files into larger, optimized files, significantly reducing metadata overhead and improving query performance. By merging fragmented files, auto-optimize allows queries to scan fewer, larger files, lowering I/O demands and reducing latency. This feature maintains consistent Delta Lake performance even under high-frequency ingestion scenarios.
When combined with partitioning and Z-ordering, auto-optimize becomes even more powerful. Partitioning organizes data into logical segments, enabling queries to target only the relevant partitions and avoid scanning unnecessary data. Z-ordering clusters related records together, improving the efficiency of common query patterns, such as range or filter queries. Together, these techniques optimize storage layout, enhance query performance, and maximize resource efficiency.
By addressing the small-file problem at its source, Delta Lake’s auto-optimize, in combination with partitioning and Z-ordering, provides a scalable and reliable approach to maintaining high-performance analytics in Lakehouse environments. Unlike incremental refresh, CSV exports, or KQL views alone, this approach ensures that continuously ingested datasets remain efficiently organized, queryable at scale, and performant across all analytics workloads.
Question 104
A data engineering team wants to implement a medallion architecture where raw JSON data is ingested, cleaned with schema enforcement, and curated for analytics. Which storage format is ideal?
A) CSV
B) Parquet
C) Delta Lake
D) JSON
Correct Answer: C) Delta Lake
Explanation:
In modern data architectures, particularly medallion architectures, the choice of file format and storage technology plays a critical role in determining data reliability, performance, and manageability. CSV files, while widely used due to their simplicity and universal support, present significant limitations when used in enterprise-scale analytical environments. Being row-based, CSVs do not natively support ACID transactions, which are essential for ensuring data consistency and integrity across concurrent operations. Additionally, CSVs lack schema enforcement, meaning that structural inconsistencies can easily arise, and historical tracking is unavailable, making it difficult to maintain versioned datasets or conduct time-based analyses. These constraints render CSV files unsuitable for layered medallion architectures, where raw, cleaned, and curated data need to coexist reliably.
Parquet, on the other hand, introduces columnar storage, which improves query performance by allowing analytics engines to read only the necessary columns rather than entire rows. This structure is particularly advantageous for aggregation-heavy or analytical queries, where scanning fewer columns reduces I/O and accelerates performance. Despite these benefits, Parquet does not inherently provide ACID transaction support or native incremental merge capabilities. Without these features, managing continuous data ingestion, updates, and deletes becomes cumbersome, and maintaining consistent, reliable data across multiple layers of a medallion architecture is challenging.
JSON files are highly flexible and can accommodate semi-structured or raw data formats, making them convenient for initial ingestion of diverse datasets. Their schema-on-read flexibility allows for the quick capture of heterogeneous data without prior modeling. However, JSON’s verbosity results in larger storage footprints and slower query performance, particularly for analytical workloads. Moreover, JSON lacks transactional guarantees and reliable schema enforcement, which complicates governance, consistency, and historical tracking across datasets, further limiting its suitability for structured, production-grade analytical pipelines.
Delta Lake addresses these limitations by combining the advantages of columnar storage with robust data management capabilities. Delta Lake supports ACID transactions, ensuring that data operations such as inserts, updates, and deletes are atomic, consistent, isolated, and durable. Schema enforcement guarantees structural consistency across datasets, while time travel allows users to access historical versions of the data, enabling auditability, reproducibility, and rollback capabilities. Additionally, Delta Lake supports incremental updates through MERGE operations, allowing seamless integration of new or updated records without the need to overwrite entire datasets, significantly improving efficiency for large-scale pipelines.
Delta Lake also supports the layered structure of medallion architectures, efficiently managing raw, cleaned, and curated layers while maintaining high performance for analytical queries. Its integration with Lakehouse pipelines ensures that data is consistently ingested, transformed, and governed across all layers, enabling enterprise-scale deployments. By combining transactional integrity, schema enforcement, historical tracking, and incremental processing, Delta Lake provides a comprehensive foundation for medallion architectures, ensuring reliability, performance, and governance across the entire data lifecycle.
while CSV, Parquet, and JSON each have niche applications, Delta Lake offers the full suite of features required for enterprise-scale medallion architectures. Its design supports efficient data processing, reliable storage, historical versioning, and seamless integration with Lakehouse pipelines, making it the optimal choice for organizations seeking high performance, consistency, and governance in modern analytics environments.
Question 105
You need to track data lineage, transformations, and dependencies across Lakehouse, Warehouse, and KQL databases for compliance. Which service should you implement?
A) Dataflow monitoring
B) Microsoft Purview
C) Warehouse audit logs
D) Power BI lineage
Correct Answer: B) Microsoft Purview
Explanation:
In today’s data-driven enterprises, maintaining visibility, traceability, and governance across the entire analytics ecosystem is essential for ensuring compliance, operational efficiency, and reliable decision-making. While several tools provide monitoring and lineage capabilities, most operate within limited boundaries, which can leave critical gaps in enterprise-wide data governance. Understanding the strengths and limitations of these tools is essential for designing a holistic governance strategy.
Dataflow monitoring is a valuable tool for observing individual Dataflows. It provides detailed execution logs that capture refresh schedules, execution times, success or failure statuses, and performance metrics. This enables data engineers and analysts to troubleshoot errors, optimize refresh cycles, and maintain operational oversight for specific Dataflows. However, the scope of Dataflow monitoring is confined to the Dataflows themselves. It does not track dependencies across multiple datasets, transformations, or services, nor does it provide a holistic view of how data flows from ingestion to consumption across the enterprise. Consequently, while useful for monitoring individual processes, Dataflow monitoring is insufficient for ensuring end-to-end visibility and governance across complex data pipelines.
Warehouse audit logs add another layer of operational insight by tracking query activity and user interactions within a single Warehouse environment. These logs are effective for auditing query patterns, monitoring usage, and detecting unauthorized access within the Warehouse. However, like Dataflow monitoring, Warehouse audit logs are limited in scope. They cannot capture dependencies, transformations, or data lineage beyond the boundaries of a single Warehouse component. This limitation makes it challenging for organizations to understand how upstream or downstream systems, such as Lakehouse tables or KQL databases, interact with Warehouse datasets. Without enterprise-wide lineage, organizations lack full traceability and are unable to confidently assess the impact of changes across multiple systems.
Power BI lineage provides visibility into datasets and reports within the Power BI ecosystem. It allows analysts to see which datasets feed into specific reports and how reports are interconnected. This capability is helpful for impact analysis, ensuring that changes to a dataset do not inadvertently break reports or dashboards. However, Power BI lineage is focused on the reporting layer and does not capture transformations, dependencies, or data movement in upstream sources such as Lakehouse tables, KQL databases, or Warehouses. As a result, while Power BI lineage helps manage report-level dependencies, it cannot provide a comprehensive view of data lineage across the entire analytics ecosystem.
Microsoft Purview addresses these limitations by providing enterprise-wide data governance and lineage tracking. Purview catalogs datasets across all Fabric services, including Lakehouse, Warehouse, KQL databases, and semantic models. It captures end-to-end lineage, recording how data flows, transforms, and is consumed across the organization. Purview also tracks dependencies, enforces governance policies, supports auditing, and ensures compliance with internal and regulatory requirements. By centralizing visibility and control, Purview allows organizations to understand the full lifecycle of their data, identify risks, and maintain traceability across multiple services and workflows.
By integrating monitoring, lineage tracking, policy enforcement, and auditing, Purview ensures that organizations have a single source of truth for data governance. It enables teams to maintain compliance, manage operational risks, and make informed decisions based on trusted, well-governed data. while Dataflow monitoring, Warehouse audit logs, and Power BI lineage provide important local insights, Microsoft Purview delivers a unified, enterprise-scale solution for comprehensive governance, visibility, and traceability across the entire data ecosystem.