Microsoft DP-700 Implementing Data Engineering Solutions Using Microsoft Fabric Exam Dumps and Practice Test Questions Set 5 Q61-75

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Question 61

You need to perform incremental data ingestion from multiple sources into a Lakehouse, 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)

Efficiently managing and updating large datasets in enterprise data platforms requires strategies that go beyond simple overwrites or basic append operations. A common approach is full table overwrite, where the entire dataset is replaced each time new data arrives. While straightforward, this method is highly inefficient for large datasets. Rewriting the entire table consumes excessive compute resources and storage, increasing operational costs and extending processing times. Furthermore, full table overwrites eliminate any historical records, making it impossible to track changes over time or perform time-travel queries. For organizations that require versioned data for auditing, compliance, or reproducibility, this approach is inadequate.

Another commonly attempted method is copying the entire dataset into CSV tables. Although CSV files are widely used and easily accessible, duplicating the entire dataset each time new data arrives is inefficient and introduces latency. CSV lacks transactional guarantees such as ACID compliance, which means that concurrent writes or partial failures can lead to inconsistent or corrupted datasets. The overhead of repeated full-table duplication, combined with the absence of built-in data integrity controls, makes CSV an impractical solution for incremental updates or production-scale Lakehouse operations. Moreover, CSV-based workflows fail to maintain historical versions, which are essential for tracking changes, debugging, or auditing.

KQL database append operations provide a slightly more efficient alternative for adding new data to existing tables. Append operations allow new records to be added without rewriting the entire dataset, reducing computational overhead compared with full table overwrites. However, KQL append has important limitations. It cannot modify or merge existing records, meaning that updates, corrections, or deduplication must be handled separately. In addition, KQL append does not maintain historical versions of the data. This makes it unsuitable for pipelines that require both incremental updates and comprehensive tracking of changes over time. For enterprises aiming to maintain accurate, versioned datasets while applying regular incremental updates, simple append operations are insufficient.

Delta Lake offers a robust solution for incremental ingestion through its MERGE operations, which are specifically designed to address the inefficiencies and limitations of overwrites, CSV copies, and append-only approaches. MERGE allows the system to compare incoming records with existing data and apply inserts, updates, and deletes in a single atomic transaction. This ensures that data remains consistent and accurate, even in concurrent or high-volume environments. MERGE operations also preserve historical versions, enabling time-travel queries, auditing, and reproducibility. Additionally, Delta Lake supports ACID transactions and schema evolution, allowing pipelines to adapt to changing data structures without breaking downstream processes.

By combining incremental updates, transaction integrity, schema management, and historical tracking, Delta Lake MERGE operations provide an efficient, reliable, and scalable approach for enterprise Lakehouse workflows. They minimize compute and storage overhead, reduce latency, maintain data accuracy, and support complex analytics over large datasets. For organizations seeking a production-ready, high-performance method for incremental ingestion, Delta Lake MERGE operations are the optimal solution, delivering both operational efficiency and long-term data reliability.

Question 62

A company wants to enable analysts to explore curated datasets interactively, enforce row-level security, and reuse entities in multiple reports. Which Fabric feature is most suitable?

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

Correct Answer: B)

In enterprise analytics environments, the way data is accessed and presented has a significant impact on governance, performance, and user productivity. Allowing analysts to query raw Lakehouse tables directly might seem like a straightforward approach to data access, but it introduces several critical risks. Raw tables contain uncurated records that may include incomplete, inconsistent, or sensitive information. Providing unrestricted access to such data can violate organizational governance policies, expose confidential information, and lead to compliance issues. Furthermore, raw tables are not optimized for multiple concurrent queries. When many users access these large datasets simultaneously, performance can degrade substantially, causing slow query responses and potentially impacting other downstream processes.

Another common practice is exporting data to CSV for analysis. While CSV files are easy to generate and widely supported by various tools, they offer only static, non-interactive snapshots of the data. These snapshots do not reflect updates in the underlying datasets, making it difficult to maintain accuracy over time. Managing multiple CSV exports also becomes cumbersome as datasets grow, leading to redundancy, versioning issues, and inefficiencies in reporting workflows. In addition, CSV files lack integration with data pipelines and analytical tools, so updates must be manually synchronized, which introduces further latency and operational overhead.

KQL database dashboards provide an alternative approach for exploring log and streaming data. These dashboards excel at monitoring events, logs, and real-time telemetry, offering insights into system performance and operational metrics. However, KQL dashboards are not designed for enterprise-scale reporting or analytical reuse. They focus on raw event data rather than curated datasets, and they do not offer semantic modeling capabilities or reusable business-friendly entities. Analysts using KQL dashboards cannot easily leverage standardized measures, hierarchies, or relationships for broader reporting purposes. As a result, these dashboards are best suited for operational monitoring rather than structured, enterprise-level analytics.

Warehouse semantic models address these limitations by providing a secure, governed, and reusable abstraction layer over curated data. By centralizing business logic, relationships, and calculations, semantic models create a consistent and reliable interface for analysts. They support row-level security, ensuring that users only see data they are authorized to access. Analysts can interact with curated datasets directly in Power BI, leveraging reusable measures, relationships, and optimized queries without ever touching raw data. This approach not only protects sensitive information but also improves query performance by predefining optimized storage structures and aggregations. Semantic models also reduce duplication and promote consistency across reports and dashboards, allowing multiple teams to work with the same trusted datasets while maintaining governance standards.

By combining security, performance, and reusability, Warehouse semantic models offer a scalable solution for enterprise reporting and analytics. Analysts can explore, filter, and visualize curated datasets interactively, all while governance policies and business rules are automatically enforced. Compared to raw table access, CSV exports, or KQL dashboards, semantic models provide the ideal balance of usability, performance, and control, ensuring that organizations can deliver reliable, consistent, and high-performance analytics at scale. This architecture aligns with enterprise data governance principles while empowering analysts to generate insights efficiently and securely.

Question 63

You need to reduce query latency 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 to CSV files
D) KQL database views

Correct Answer: B)

Efficient data management in Lakehouse architectures is critical for maintaining high performance and ensuring that analytical workloads run smoothly. One common challenge in Lakehouses is the accumulation of small files, which can degrade query performance, increase metadata overhead, and consume unnecessary storage. Incremental refresh is a feature that can improve the performance of Dataflows by only processing new or updated data, reducing the amount of work required during each refresh cycle. While this approach can speed up transformations and refreshes within Dataflows, it does not address the underlying problem of small files accumulating in the Lakehouse storage. Over time, this can lead to a proliferation of files that negatively impacts query planning and execution.

Another approach some organizations take is exporting data to CSV files for downstream use or analytics. While CSV exports are easy to generate and widely supported, they exacerbate the small-file problem. Each CSV file is an independent object, and repeated exports create a large number of small files. This results in increased metadata overhead, as the system must track and manage all these individual files. Additionally, CSV files do not offer transactional guarantees or optimized storage for analytical queries, making them inefficient for enterprise-scale workflows. Queries over CSV exports tend to be slower, and the files themselves do not contribute to improving the performance of Lakehouse operations.

KQL database views are often used for log and streaming data analytics. They provide a flexible interface for querying and aggregating data in real time, but they have limitations in terms of storage and performance optimization. KQL views do not modify the underlying storage layout of the datasets they query. This means that even if the underlying files are fragmented or inefficiently stored, KQL cannot consolidate or optimize them. As a result, while KQL is excellent for near-real-time monitoring and operational analytics, it cannot resolve storage inefficiencies or improve query performance for large, continuously ingested datasets.

The most effective solution to these challenges is Delta Lake’s auto-optimize feature. Auto-optimize automatically merges small files into larger, optimized files, reducing the total number of files in the Lakehouse and significantly decreasing metadata overhead. By consolidating files, queries require less planning and execution time, which improves overall latency and ensures that Delta Lake maintains high performance even as data grows. When combined with partitioning and Z-ordering, auto-optimize further enhances query efficiency by organizing data to minimize scan operations and maximize resource utilization. Partitioning separates data into logical subsets, while Z-ordering clusters related data together, both of which contribute to faster query execution and reduced I/O.

For Lakehouse environments that experience continuous ingestion of new data, auto-optimize provides a scalable, automated, and reliable mechanism to maintain high performance over time. Unlike incremental refresh, CSV exports, or KQL views, auto-optimize directly addresses the root cause of small-file accumulation, ensuring that data remains organized, accessible, and efficient to query. By leveraging these combined optimization strategies, organizations can achieve better resource utilization, faster query performance, and a more maintainable Lakehouse architecture. This makes auto-optimize, when paired with partitioning and Z-ordering, the most effective approach for sustaining enterprise-scale performance on continuously ingested datasets.

Question 64

A data engineering team wants to implement a medallion architecture in Fabric. Raw data is stored in JSON format, cleaned data needs schema enforcement, and curated data must support analytics. Which storage format is best?

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

Correct Answer: C)

When building enterprise-scale medallion architectures, the choice of storage format is critical for ensuring performance, reliability, and maintainability across raw, cleansed, and curated data layers. CSV, one of the most commonly used formats, is simple and widely supported, but it has significant limitations in production environments. Being a row-based text format, CSV lacks support for ACID transactions, meaning that concurrent writes or partial failures can result in inconsistent or corrupted data. It also does not enforce schemas, so any structural changes in the data require manual validation, and there is no built-in mechanism for tracking historical versions of datasets. These limitations make CSV impractical for layered architectures where data consistency, reproducibility, and incremental updates are essential.

Parquet is a more advanced format that provides columnar storage, which is particularly beneficial for analytics. Columnar storage allows queries to scan only the relevant columns, improving read performance and reducing storage requirements. Parquet is widely adopted in data warehouses and analytical workloads because of its efficient compression and query optimization. However, Parquet files do not natively support ACID transactions or schema evolution. Without these features, maintaining consistent incremental transformations becomes complex, especially in pipelines where data is continually updated or appended. Handling upserts, deletions, and merges requires additional frameworks or custom logic, which can introduce complexity and risk.

JSON files offer another option, particularly for raw or semi-structured data. JSON is flexible and can accommodate varying data structures without predefined schemas, making it ideal for initial data ingestion where formats may be inconsistent or evolving. Despite this flexibility, JSON has several drawbacks for enterprise analytics. It is verbose, which increases storage and processing requirements, and it is inefficient for large-scale query execution. JSON also lacks transactional guarantees and does not provide schema enforcement or historical versioning, which limits its usability for reliable, reproducible transformations or curated data layers.

Delta Lake addresses the shortcomings of CSV, Parquet, and JSON by combining columnar storage with robust transactional and governance features. Delta Lake supports ACID transactions, ensuring that data remains consistent even during concurrent operations or pipeline failures. Schema enforcement prevents invalid or inconsistent records from entering datasets, maintaining data quality across raw, cleaned, and curated layers. Time travel functionality allows teams to query previous versions of datasets for auditing, debugging, or reproducing results, while incremental updates through the MERGE operation enable efficient handling of upserts and deletes. Delta Lake also integrates seamlessly with Lakehouse pipelines, Spark notebooks, and other processing frameworks, allowing fully automated, reliable workflows.

By combining high-performance columnar storage with ACID compliance, schema enforcement, historical tracking, and integration with enterprise pipelines, Delta Lake provides a comprehensive solution for medallion architectures. It enables organizations to maintain raw, cleansed, and curated data layers with consistency, reliability, and efficiency. Queries are optimized for performance, pipelines can handle incremental updates without rewriting entire tables, and historical versions of datasets are always accessible. This combination of capabilities ensures that Delta Lake meets the performance, governance, and scalability requirements of enterprise-scale data platforms, making it the preferred choice for modern medallion architecture implementations.

Question 65

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 you use?

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

Correct Answer: B)

In enterprise data environments, understanding the flow and lineage of data is essential for governance, compliance, and operational efficiency. While several tools provide partial visibility into data operations, most are limited in scope and fail to offer a comprehensive view of enterprise-wide data movement. Dataflow monitoring, for example, is designed to track execution logs for individual Dataflows. It provides insights into refresh schedules, transformation steps, and potential errors for a single workflow. Although this information is useful for operational monitoring, it does not extend beyond the scope of the individual Dataflow. Organizations that rely solely on Dataflow monitoring lack visibility into how datasets interact across multiple services, which prevents them from fully understanding the impact of changes on downstream analytics or reports.

Warehouse audit logs offer another layer of insight, primarily focusing on the usage and queries executed within the Warehouse component. Administrators can monitor which datasets are being queried, identify usage patterns, and troubleshoot performance issues within the Warehouse. However, this visibility is limited to a single component of the enterprise data ecosystem. Warehouse audit logs do not capture lineage across other critical services such as Lakehouses, KQL databases, or Power BI. Consequently, while they support operational oversight within the Warehouse, they do not provide the end-to-end understanding necessary for enterprise governance or compliance.

Power BI lineage tracks the relationships between reports, datasets, and dashboards within the Power BI environment. It enables analysts and administrators to see how reports are connected to underlying datasets, helping to identify dependencies and assess the impact of changes on analytics outputs. While this functionality is valuable within Power BI, it does not extend to transformations, pipelines, or datasets outside of the Power BI ecosystem. Changes in Lakehouse tables, KQL databases, or upstream pipelines are not captured, leaving gaps in the overall lineage picture. Organizations using only Power BI lineage may struggle to maintain consistent governance and ensure that all transformations and dependencies are properly documented.

Microsoft Purview addresses these limitations by providing enterprise-wide data governance and comprehensive lineage tracking across all Fabric services. Purview catalogs datasets across Lakehouse, Warehouse, KQL databases, and Power BI semantic models, providing a centralized view of all organizational data assets. It tracks transformations, records dependencies, and enforces governance policies, ensuring that changes in one system are visible across the entire data ecosystem. Purview also supports auditing and compliance requirements by maintaining historical records of data movement and usage. Analysts and administrators can see exactly how data flows from ingestion through transformation to reporting, enabling impact analysis, troubleshooting, and informed decision-making.

By integrating with multiple Fabric services and consolidating lineage, cataloging, and policy enforcement, Microsoft Purview provides the visibility, control, and governance necessary for large-scale enterprise data management. Unlike Dataflow monitoring, Warehouse audit logs, or Power BI lineage, Purview offers a unified, end-to-end solution that ensures transparency, accountability, and compliance across the organization. It allows teams to track data movement, understand dependencies, and maintain consistent governance policies across all datasets and transformations, making it the definitive choice for enterprise-scale lineage and oversight.

Question 66

You need to implement a data pipeline in Microsoft Fabric that handles both batch and streaming data sources while ensuring fault tolerance and retry logic. Which solution should you choose?

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

Correct Answer: B)

In modern data platforms, selecting the right orchestration tool is critical for ensuring reliable, scalable, and fault-tolerant data workflows. While several services provide data transformation and processing capabilities, they differ significantly in their ability to manage complex pipelines that involve multiple sources, dependencies, and execution conditions. Dataflow Gen2, for instance, is optimized for low-code transformations and incremental refreshes. It enables analysts and engineers to clean, shape, and transform data with minimal coding effort, making it ideal for routine or moderately complex tasks. However, Dataflow Gen2 is not designed to orchestrate workflows that span multiple data sources or processing steps. It lacks advanced orchestration features, including automated retries, conditional execution, and error-handling mechanisms, which are essential for enterprise-scale, production-grade pipelines.

Spark notebooks are another widely used tool, particularly for scenarios requiring distributed computation. They excel at parallel processing of large datasets, support Python, PySpark, and Scala, and allow complex transformations and machine learning workflows to be executed efficiently across Spark clusters. Despite their computational power, Spark notebooks function as independent processing units and do not natively provide orchestration capabilities. They cannot manage dependencies between multiple ingestion tasks, coordinate batch and streaming workflows, or automatically handle failures and retries. Consequently, while Spark notebooks are excellent for heavy data transformations, they are insufficient for orchestrating end-to-end pipelines that require coordination across multiple tasks or services.

KQL database ingestion rules offer a specialized solution for streaming or event-driven data ingestion into KQL databases. These rules are highly effective for capturing real-time data from logs, telemetry, or events and applying lightweight transformations as data enters the KQL database. However, KQL ingestion is constrained to a single data sink and is primarily designed for real-time or near-real-time analytics. It cannot manage batch workloads, coordinate tasks across multiple datasets, or enforce dependencies between processes. For organizations that require complex pipelines involving multiple services or stages of processing, KQL ingestion alone does not provide sufficient orchestration or reliability features.

Synapse Pipelines, in contrast, is built to handle full-scale orchestration of both batch and streaming data workflows. It provides a unified framework to sequence and coordinate a wide range of data activities, including Dataflows, Spark notebooks, and other processing tasks within Fabric. Synapse Pipelines enables conditional execution, dependency management, retries, error handling, and scheduling, ensuring that pipelines run reliably and recover automatically from transient failures. Monitoring capabilities allow teams to track execution status, identify bottlenecks, and troubleshoot issues across the entire workflow. Integration with Lakehouse, Warehouse, and KQL databases ensures seamless connectivity between storage, transformation, and analytics layers, enabling consistent and repeatable processing of enterprise datasets.

By providing comprehensive orchestration, Synapse Pipelines bridges the gaps left by Dataflow Gen2, Spark notebooks, and KQL ingestion rules. It allows organizations to design end-to-end, fault-tolerant pipelines that can handle diverse data sources, complex dependencies, and high-volume processing requirements. For scenarios that demand reliable, scalable, and fully coordinated workflows, Synapse Pipelines delivers the operational control, visibility, and integration needed to manage enterprise data pipelines efficiently and effectively.

Question 67

A company wants to provide analysts with a curated dataset for Power BI that enforces row-level security, reusable measures, and semantic modeling. Which feature should they implement?

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

Correct Answer: B)

In enterprise data environments, providing analysts with access to raw data directly from Lakehouse tables may seem like the simplest solution, but it introduces significant risks and limitations. Raw tables contain uncurated data, which often includes incomplete, inconsistent, or sensitive information. Allowing unrestricted access to this data can violate governance policies and compromise compliance standards. Additionally, querying large raw tables directly can create performance bottlenecks. When multiple analysts run concurrent queries against raw datasets, the system may experience slow response times, resource contention, and overall degradation in performance, affecting downstream processes and analytics workloads.

A common alternative is exporting data to CSV files for analysis. While CSV files are widely supported and easy to generate, they present their own set of challenges. CSV exports provide static snapshots of the data that do not update dynamically as the source changes. They lack interactivity, making exploratory analysis cumbersome. CSVs also do not support row-level security or access control, which increases the risk of exposing sensitive information. Moreover, they are not designed for enterprise-scale reporting; maintaining multiple CSV exports across departments leads to redundancy, data inconsistency, and operational inefficiencies. Analysts using CSV files must manually track updates and reconcile datasets, which increases the likelihood of errors and slows down reporting cycles.

KQL database dashboards offer another approach, particularly for log and streaming data. They are optimized for real-time monitoring, telemetry, and operational insights, providing analysts with fast access to event-driven datasets. However, KQL dashboards do not provide a semantic modeling layer, meaning they lack reusable business entities, measures, and relationships. While effective for operational analytics, they are not suitable for building standardized, reusable reporting assets across the organization. Analysts cannot rely on KQL dashboards for consistent enterprise-wide metrics or structured analytical reporting, limiting their usefulness in large-scale business intelligence initiatives.

Warehouse semantic models provide a robust solution for enterprise analytics by creating a secure, governed, and reusable abstraction layer over curated datasets. These models centralize business logic, relationships, and measures, allowing analysts to explore data interactively without direct access to raw tables. Row-level security ensures that users only see data they are authorized to access, maintaining compliance and governance. By predefining relationships, measures, and optimized queries for Power BI, semantic models improve query performance and reduce redundancy in reporting. Analysts can build multiple reports and dashboards on the same trusted datasets, ensuring consistency in calculations and metrics across the organization.

Beyond security and performance, semantic models also enhance maintainability and scalability. Changes in underlying data or transformations can be reflected in the model, automatically propagating updates to all dependent reports without requiring manual adjustments. This reduces errors, improves efficiency, and ensures that governance policies are consistently applied across all analytics workflows. By providing a centralized, reusable layer that balances security, interactivity, and performance, Warehouse semantic models represent the most effective approach for enterprise reporting and analytics. They allow organizations to provide analysts with rich, interactive data exploration while maintaining compliance, governance, and high-performance querying, making them essential for modern business intelligence practices.

Question 68

You need to reduce query latency on a continuously updated Lakehouse table that contains 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)

In Lakehouse architectures, managing the efficiency and performance of data queries is critical, especially as data volumes grow and ingestion becomes continuous. One common approach to improving performance in Dataflows is incremental refresh. Incremental refresh allows the system to process only new or updated data rather than reprocessing the entire dataset. While this method reduces refresh times and lowers computational overhead for Dataflows, it does not address a major challenge in Lakehouse storage: the accumulation of small files. Over time, continuously ingested data often generates many small files, which can severely degrade query performance and increase the workload on the metadata layer.

Some organizations attempt to address these challenges by exporting datasets to CSV files. CSV exports, however, exacerbate the small-file problem. Each export creates new independent files, adding to metadata overhead and making the system less efficient. Queries must handle an increasing number of small files, which slows down processing and increases resource consumption. Additionally, CSV files are static snapshots and do not benefit from features like transactional guarantees or optimized storage layouts. This makes them a poor choice for continuously updated datasets, where performance, consistency, and scalability are critical.

KQL database views are another method used for querying data, particularly streaming or log-based datasets. They allow analysts to build dashboards and extract insights in near real-time. However, KQL views do not alter the underlying storage layout or consolidate fragmented files. While they are effective for real-time monitoring and operational analytics, they do not optimize query performance for large-scale, continuously ingested datasets. As a result, relying solely on KQL views leaves the underlying inefficiencies in the storage layer unaddressed, limiting overall performance and scalability.

The most effective approach for maintaining high-performance querying in Lakehouses is Delta Lake’s auto-optimize feature. Auto-optimize automatically merges small files into larger, optimized files, reducing metadata overhead and minimizing the number of objects the system needs to manage. By consolidating these files, queries can execute faster because fewer files are scanned, reducing I/O and improving latency. Auto-optimize ensures that Delta Lake tables maintain high performance even as data ingestion continues, without requiring manual intervention or complex maintenance tasks.

When combined with additional optimization strategies such as partitioning and Z-ordering, auto-optimize becomes even more powerful. Partitioning organizes data into logical segments based on key columns, limiting the amount of data scanned for queries. Z-ordering further improves efficiency by clustering related data together, which reduces the number of files that must be read for common query patterns. Together, these techniques ensure that queries run efficiently, resource utilization is optimized, and performance remains consistent across large and rapidly growing datasets.

By addressing the small-file problem, reducing metadata overhead, and optimizing file layout automatically, auto-optimize provides a scalable and reliable solution for Lakehouse architectures. Unlike incremental refresh, CSV exports, or KQL views alone, it directly improves query performance while supporting continuous ingestion, making it the optimal choice for enterprises that require high-speed, efficient analytics on large-scale datasets. This combination of automated consolidation, partitioning, and Z-ordering ensures that Lakehouse environments remain performant and manageable at scale.

Question 69

A data engineering team needs to implement a medallion architecture where raw JSON data is ingested, cleaned with schema enforcement, and transformed into curated analytics-ready tables. Which storage format is best?

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

Correct Answer: C)

When designing enterprise-scale medallion architectures, selecting the appropriate storage format is critical for performance, reliability, and maintainability. CSV files, while widely used for data exchange and simple storage, present significant limitations in production environments. Being row-based and flat, CSV files do not support ACID transactions, which means that concurrent writes or partial failures can corrupt data. They also lack schema enforcement, requiring manual validation whenever the structure changes, and do not provide any mechanism for versioning or historical tracking. This makes them unsuitable for layered architectures, where datasets evolve from raw to cleaned and curated forms and must maintain consistency over time.

Parquet files are a popular alternative because of their columnar format, which allows for efficient analytical querying and data compression. Columnar storage optimizes read performance by enabling selective access to only the required columns, significantly improving query speed for analytical workloads. However, Parquet lacks native support for ACID transactions, upserts, and merges, which are essential for incremental updates. Without these transactional guarantees, maintaining consistency across multiple transformations becomes cumbersome, and handling continuously updated or evolving datasets requires complex workarounds. While excellent for static analytical queries, Parquet alone cannot fully support the dynamic requirements of a medallion architecture.

JSON files offer a different set of benefits, primarily around flexibility. They can store raw or semi-structured data, accommodating records with variable schemas without the need for strict column definitions. This makes JSON suitable for initial ingestion or exploratory pipelines where data structure is not fixed. Despite this flexibility, JSON is inefficient for large-scale analytics because it is verbose, lacks columnar storage benefits, and consumes more storage and compute resources during queries. Moreover, JSON provides no transactional guarantees or schema evolution mechanisms, limiting its use in reliable, enterprise-grade data pipelines that require consistent and reproducible results.

Delta Lake addresses the limitations of CSV, Parquet, and JSON by combining columnar storage with enterprise-grade transactional and governance features. Delta Lake provides ACID transactions, ensuring data consistency even when multiple processes write to the same dataset simultaneously. Its schema enforcement capabilities prevent invalid or inconsistent records from entering curated layers. Delta Lake also supports time travel, allowing teams to access historical snapshots of data for auditing, troubleshooting, or reproducing analyses. Incremental updates are handled efficiently through the MERGE operation, enabling upserts, deletes, and updates without full table rewrites.

In addition, Delta Lake integrates seamlessly with Fabric pipelines, Spark notebooks, and Dataflows, enabling a complete, automated workflow from ingestion to transformation to curation. Its architecture supports raw, cleaned, and curated layers within the medallion framework, ensuring reliable historical tracking, consistent transformations, and high-performance querying at scale. By combining the performance advantages of columnar storage with robust transaction management, schema enforcement, and integration with enterprise pipelines, Delta Lake provides a comprehensive solution for building resilient, scalable, and governed medallion architectures. This makes it the optimal choice for organizations seeking both reliability and efficiency in large-scale data processing and analytics workflows.

Question 70

You need to track lineage, transformations, and dependencies of datasets 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, understanding how data moves, transforms, and is used across the organization is critical for governance, compliance, and operational efficiency. While several tools provide some level of monitoring or lineage tracking, most are limited in scope and do not provide a comprehensive view of enterprise-wide data flow. For example, Dataflow monitoring offers detailed logs of individual Dataflows, including refresh schedules, execution status, and transformation steps. These logs are useful for troubleshooting or understanding the behavior of a single Dataflow, but they do not capture dependencies across multiple services or provide a holistic view of how datasets interact throughout the organization. Without this broader perspective, it becomes challenging to understand downstream impacts or assess enterprise-level data lineage.

Warehouse audit logs provide visibility into query activity within the Warehouse component. Administrators can see which datasets are being accessed, track user activity, and analyze query patterns within that environment. While helpful for operational oversight within the Warehouse, these logs are restricted to a single service. They cannot capture interactions with Lakehouse tables, KQL databases, or downstream reporting tools. As a result, they do not offer end-to-end visibility across the organization’s data ecosystem, leaving gaps in governance, auditing, and compliance.

Power BI lineage provides insight into the relationships between reports, datasets, and dashboards within the Power BI environment. It allows analysts and administrators to identify which datasets feed into which reports and understand dependencies within Power BI. However, this lineage is limited to the Power BI service and does not extend to upstream sources such as Lakehouse tables, KQL databases, or transformations executed in pipelines. Consequently, organizations that rely solely on Power BI lineage may struggle to maintain consistent governance across all stages of data processing and reporting, and they may lack the visibility required for impact analysis or auditing across services.

Microsoft Purview addresses these limitations by providing a unified platform for enterprise-wide data governance, cataloging, and lineage tracking. Purview catalogs datasets across Lakehouse, Warehouse, KQL databases, and Power BI semantic models, creating a centralized repository of metadata. It tracks data transformations, records dependencies, and enforces governance policies, ensuring that changes in one system are visible and understood across the organization. Purview also enables auditing and compliance by maintaining historical records of data movement and usage, providing transparency into who accessed data, when, and how it was used. By integrating across multiple services, it provides full end-to-end lineage, allowing teams to understand how data flows from ingestion through transformation to reporting, and to assess the impact of changes on downstream processes.

By consolidating monitoring, lineage tracking, policy enforcement, and auditing in a single platform, Microsoft Purview enables organizations to maintain consistent governance and compliance at scale. Unlike Dataflow logs, Warehouse audit reports, or Power BI lineage alone, Purview provides comprehensive visibility into enterprise data usage. It allows organizations to manage datasets reliably, track dependencies, enforce security policies, and ensure that analytical and operational workflows adhere to governance standards. For enterprises seeking a centralized solution for data governance and lineage, Purview provides the visibility, control, and scalability needed to manage complex, multi-service data ecosystems effectively.

Question 71

You need to process streaming telemetry data and store it in a format suitable for analytics in Microsoft Fabric. The solution must allow schema evolution and support incremental updates.

A) CSV files in OneLake
B) JSON files in Lakehouse
C) Delta Lake tables in Lakehouse
D) Parquet files in Warehouse

Correct Answer: C)

CSV files are simple text-based files but lack ACID transactions, schema evolution, and efficient incremental update support, making them unsuitable for continuous ingestion of telemetry data. JSON files are flexible for semi-structured data but are inefficient for analytics queries, do not handle ACID transactions, and lack robust incremental update mechanisms. Parquet files offer columnar storage and good query performance but by themselves do not provide ACID compliance, incremental merge capabilities, or versioning. Delta Lake tables provide ACID transactions, schema evolution, time travel, and incremental update support via MERGE operations. This allows streaming telemetry data to be ingested in near real time, updated or merged efficiently, and stored in a format optimized for analytics. Delta Lake integrates seamlessly with Lakehouse pipelines and enables reliable, high-performance queries over continuously updated datasets.

Question 72

A company wants to provide analysts with curated datasets while ensuring row-level security and enabling reusable measures in Power BI. Which solution is most appropriate?

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

Correct Answer: B)

Direct access to Lakehouse tables exposes raw data and can compromise governance and performance. CSV exports provide static, non-interactive datasets without reusability or security. KQL database dashboards focus on streaming or log analytics and do not provide semantic modeling or reusable measures. A Warehouse semantic model creates a secure abstraction layer over curated datasets, enforcing row-level security, reusable measures, and relationships. Analysts can explore curated datasets interactively without accessing raw data, ensuring consistent, governed, and high-performance reporting across multiple Power BI reports. Semantic models also support enterprise-wide consistency and governance, making them ideal for analytic use cases.

Question 73

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

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

Correct Answer: B)

Incremental refresh improves Dataflow performance but does not resolve small-file accumulation in the Lakehouse. Exporting to CSV adds additional small files and increases metadata overhead, worsening query performance. KQL database views abstract queries but do not modify the underlying storage or optimize file layouts. Auto-optimize merges small files into larger optimized files, reduces metadata overhead, improves query latency, and maintains Delta Lake performance. When combined with partitioning and Z-ordering, auto-optimize ensures efficient query execution and better resource utilization on continuously ingested datasets, making it the best approach for maintaining high-performance Lakehouse queries.

Question 74

A data engineering team wants to implement a medallion architecture with raw, cleaned, and curated layers. The raw layer contains semi-structured JSON data, the cleaned layer must enforce schema, and the curated layer supports analytics. Which storage format is ideal?

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

Correct Answer: C)

CSV files are row-based, lack ACID compliance, schema enforcement, and versioning, making them unsuitable for medallion architecture. Parquet provides columnar storage and query efficiency but does not offer transactional guarantees or schema evolution natively. JSON is flexible for raw semi-structured 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, enabling historical tracking, reliability, and high-performance analytics in enterprise-scale medallion architectures.

Question 75

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

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

Correct Answer: B)

Dataflow monitoring provides execution logs for individual Dataflows but does not offer enterprise-wide lineage or transformation tracking. Warehouse audit logs are limited to queries within a single Warehouse and do not capture cross-service dependencies. Power BI lineage tracks datasets and reports but cannot track transformations or dependencies in Lakehouse or KQL databases. Microsoft Purview provides 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.