Microsoft DP-700 Implementing Data Engineering Solutions Using Microsoft Fabric Exam Dumps and Practice Test Questions Set 2 Q16-30

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Question 16

You need to ensure that sensitive columns in Lakehouse Tables are protected from unauthorized access while allowing analytics on other columns. Which Fabric feature should you use?

A) Column-level security
B) Incremental refresh
C) Power BI Dataset
D) Dataflows

Answer: A) Column-level security

Explanation:

In modern data platforms, managing both efficient data processing and robust data security is crucial for organizations handling large and sensitive datasets. Incremental refresh is a widely used feature in Microsoft Fabric that optimizes data ingestion by updating only the portions of data that have changed since the last refresh. This significantly reduces the processing time, minimizes computational resource usage, and enables organizations to handle very large datasets more efficiently. However, while incremental refresh addresses performance and scalability, it does not inherently provide mechanisms for protecting sensitive information or enforcing access control within datasets.

Power BI Datasets are commonly used to provide analytics and reporting capabilities. They allow users to visualize and interact with data efficiently, offering features such as aggregations, caching, and fast query performance. Despite these advantages, Power BI Datasets rely on preprocessed data and do not automatically enforce column-level security across the data lake. This limitation can leave sensitive data exposed if appropriate access controls are not implemented at the source or within the reporting layer.

Similarly, Dataflows in Microsoft Fabric are designed to automate extract, transform, and load (ETL) operations. They enable organizations to prepare, clean, and shape data before it is consumed by analytics tools. Dataflows provide excellent capabilities for transforming raw data into structured formats suitable for analysis. However, Dataflows alone cannot enforce fine-grained access control on individual columns. Users with access to the Dataflow output may inadvertently gain access to sensitive information unless additional security measures are applied.

Column-level security addresses this critical gap by enabling administrators to restrict access to specific columns within a dataset while allowing users to interact with non-sensitive data. This approach ensures that sensitive information, such as personally identifiable information (PII) or financial details, is protected even in multi-user or collaborative environments. Column-level security integrates seamlessly with Lakehouse Tables, enabling organizations to enforce security policies directly at the storage layer. This ensures that access restrictions are consistent across different analytics and reporting tools while maintaining compliance with privacy regulations and internal governance requirements.

By implementing column-level security, administrators can define dynamic access rules based on user roles, job functions, or organizational hierarchies. For example, finance teams may have access to revenue figures, while marketing teams can see campaign metrics but not confidential financial data. These controls are applied automatically during query execution, ensuring that users only see data they are authorized to view without impacting their ability to perform meaningful analyses.

while incremental refresh, Power BI Datasets, and Dataflows provide performance, scalability, and transformation capabilities, they do not inherently enforce fine-grained data protection. Column-level security complements these features by providing precise control over sensitive data, protecting it from unauthorized access, and ensuring compliance with regulatory and governance standards. By integrating column-level security with Lakehouse Tables and analytics workflows, organizations can achieve a secure, collaborative, and highly efficient environment for data processing and reporting, balancing both performance and data privacy effectively.

This approach allows organizations to handle large-scale data efficiently while maintaining strict control over sensitive information, supporting secure collaboration and reliable governance across the enterprise.

Question 17

You need to transform raw JSON IoT data into a structured format for analytics in Fabric. Which approach is most suitable?

A) Spark notebook transformations into Lakehouse Tables
B) Power BI Dataset
C) Manual Excel processing
D) Dataflows only

Answer: A) Spark notebook transformations into Lakehouse Tables

Explanation:

Processing and transforming high-volume IoT data presents unique challenges that demand scalable, flexible, and reliable solutions. Traditional approaches, such as using Power BI datasets, are primarily designed for analytics and visualization rather than large-scale data transformations. While Power BI excels at querying and presenting insights, it lacks the computational power and flexibility needed to process massive streams of semi-structured or unstructured data in real time. Similarly, manual processing with tools like Excel is not a viable option for IoT workloads, as it is prone to errors, cannot handle large volumes of data efficiently, and requires significant human intervention, making it both time-consuming and unreliable for high-velocity datasets.

Dataflows provide a low-code solution for data transformation and integration. They are effective for preparing and cleaning moderate volumes of structured data, but they are not optimized for handling complex, high-velocity streaming data, such as IoT JSON payloads. Dataflows may struggle with semi-structured formats, nested objects, and irregular schemas, which are common in IoT environments. Furthermore, their performance and scalability are limited when compared to distributed computing solutions, making them less suitable for real-time or near-real-time processing of large-scale data streams.

Spark notebooks, on the other hand, offer a highly flexible and scalable platform for transforming and processing IoT data at scale. By leveraging Apache Spark’s distributed computing capabilities, Spark notebooks can handle massive datasets in parallel across multiple nodes, ensuring both speed and reliability. They can ingest semi-structured JSON data, parse complex objects, and convert them into structured formats that are suitable for downstream analytics, reporting, and machine learning. This transformation step is critical for converting raw IoT telemetry into actionable insights, while maintaining data integrity and consistency.

Once the data is transformed in Spark notebooks, it can be written to Lakehouse Tables, which combine the benefits of data lakes and data warehouses. These tables support ACID compliance, ensuring transactional consistency, and provide indexing for fast query performance. They also allow for optimized storage and retrieval of large datasets, making them ideal for analytics workloads that require repeated querying and aggregation. Lakehouse Tables serve as a reliable foundation for building analytics pipelines, dashboards, and machine learning models.

Additionally, Spark notebooks support advanced features such as checkpointing, fault tolerance, and streaming integration, which are essential for managing high-velocity IoT data. Distributed processing ensures that even as data volume grows, performance remains predictable and scalable. By automating data transformations and integrating directly with storage and analytics layers, Spark notebooks reduce operational complexity while enabling efficient, accurate, and timely insights.

for handling high-volume IoT data, Spark notebooks provide a robust, scalable, and reliable solution. Unlike Power BI datasets, Excel, or dataflows, Spark notebooks can efficiently process semi-structured JSON streams, transform them into structured formats, and store the results in Lakehouse Tables with ACID compliance, indexing, and optimized query performance. This architecture enables organizations to manage large-scale IoT workloads effectively, supporting high-speed ingestion, transformation, and analytics with minimal operational overhead.

Question 18

A company wants to monitor the performance and execution of Fabric data pipelines for troubleshooting and optimization. Which feature should they enable?

A) Pipeline monitoring and logging
B) Power BI Datasets
C) Lakehouse Tables
D) Dataflows

Answer: A) Pipeline monitoring and logging

Explanation:

Power BI Datasets and Lakehouse Tables are powerful components within Microsoft Fabric, optimized primarily for analytics, reporting, and large-scale data storage. They provide excellent performance for querying structured and semi-structured data and are ideal for supporting dashboards, interactive reports, and high-volume analytics. However, these tools are not inherently designed to monitor the execution of data processing pipelines. While they store and process the data that pipelines generate, they do not offer native capabilities for tracking pipeline activity, evaluating workflow performance, or capturing execution metrics.

Dataflows, on the other hand, provide a low-code approach to automating ETL operations, allowing data to be extracted, transformed, and loaded into target destinations efficiently. They enable organizations to standardize and automate data preparation across various sources, ensuring that datasets remain clean, consistent, and ready for analytics. Despite their automation capabilities, dataflows are not designed for comprehensive pipeline monitoring. They lack detailed logging of each processing step, execution histories, failure diagnostics, and performance analysis, all of which are critical for managing complex, enterprise-grade ETL workflows.

For organizations seeking full observability into their data operations, Synapse Pipelines within Microsoft Fabric provide robust pipeline monitoring and logging capabilities. Synapse Pipelines allow administrators and data engineers to track each step of a data workflow, from the initiation of a pipeline to the completion of individual activities. Every execution is logged, providing a complete history that can be reviewed to identify patterns, performance bottlenecks, and points of failure. This historical visibility is essential for understanding the behavior of ETL workflows over time and for ensuring that any recurring issues are addressed proactively.

Moreover, Synapse Pipeline monitoring supports real-time alerts and notifications, enabling teams to respond quickly to failed or delayed processes. Performance metrics such as activity duration, throughput, and error rates are captured and made available for analysis, allowing data engineers to optimize workflows for efficiency and reliability. These capabilities ensure that pipelines run smoothly, reduce downtime, and maintain the integrity and timeliness of data delivered to downstream analytics platforms like Power BI Datasets and Lakehouse Tables.

By integrating pipeline monitoring and logging with robust ETL orchestration, organizations can achieve a proactive approach to data operations. Failures can be detected and addressed before they impact business-critical analytics, and workflows can be fine-tuned to maximize resource utilization and minimize processing times. This end-to-end visibility fosters operational efficiency and builds confidence that the data powering analytics and reporting is accurate, timely, and reliable.

while Power BI Datasets and Lakehouse Tables serve as the backbone for analytics and storage in Microsoft Fabric, and dataflows provide automated ETL capabilities, neither offers comprehensive pipeline monitoring. Synapse Pipelines fill this critical gap by providing detailed tracking, execution history, error identification, and performance metrics, enabling proactive troubleshooting, optimization, and reliable, efficient data processing across the enterprise. This combination ensures that analytics workflows operate smoothly and deliver high-quality insights to decision-makers.

Question 19

You want to optimize Power BI reports based on Lakehouse data for high concurrency among many users. Which strategy is most effective?

A) Use aggregated tables and Power BI Datasets
B) Directly query Lakehouse Tables
C) Use Dataflows for reporting
D) Export data to Excel

Answer: A) Use aggregated tables and Power BI Datasets

Explanation:

Relying solely on direct queries against Lakehouse Tables for reporting in high-concurrency scenarios can create significant performance bottlenecks. Lakehouse Tables are designed to store vast amounts of structured and semi-structured data efficiently, supporting complex analytics and large-scale transformations. However, when many users attempt to access this data simultaneously, query execution times can increase dramatically, resulting in slow dashboards and delayed insights. High-volume, interactive reporting workloads can overwhelm the storage layer, which is optimized for bulk analytics rather than rapid, concurrent queries, leading to resource contention and degraded performance.

Dataflows provide a low-code approach to extract, transform, and load (ETL) data from various sources into a structured form. While they simplify the transformation of raw data into consumable formats, they are not inherently designed to serve high-concurrency reporting needs. Dataflows are excellent for preparing data, performing cleaning and standardization, or joining multiple sources, but they do not address the challenges of supporting large numbers of simultaneous report viewers or ensuring sub-second query responses.

Exporting data into Excel or similar tools is another approach, but it introduces operational limitations. Manual exports are prone to errors, difficult to automate at scale, and cannot provide real-time insights. Excel sheets become outdated quickly and are not feasible for enterprises requiring rapid decision-making across many users. They also place the burden of data management on individual analysts, further increasing operational overhead.

A more effective strategy involves the use of aggregated tables. These tables precompute metrics, summaries, and other derived data ahead of time, significantly reducing query complexity and execution time. Aggregations minimize the amount of raw data that needs to be processed on-demand, enabling faster responses and reducing strain on underlying Lakehouse infrastructure. By storing commonly requested metrics and summaries, aggregated tables help maintain performance under heavy load while supporting more consistent and reliable reporting experiences.

Power BI Datasets complement this approach by caching query results and preloaded data in a highly optimized in-memory structure. Datasets allow dashboards and reports to serve a large number of concurrent users with minimal latency. Users can interactively slice, filter, and drill down into the data without triggering repeated queries against the Lakehouse Tables, ensuring consistent performance even under peak demand. This caching mechanism drastically reduces the workload on the backend and ensures that analytical reports remain responsive.

Combining aggregated tables with Power BI Datasets creates a highly scalable architecture for enterprise analytics. The aggregated tables ensure that computationally intensive transformations are handled ahead of time, while Power BI Datasets deliver results to users quickly and efficiently. This hybrid approach not only maintains performance under high concurrency but also provides a more reliable, maintainable, and user-friendly reporting environment. By strategically separating the responsibilities of raw data storage, precomputed summaries, and interactive visualization, organizations can achieve fast, scalable, and resilient analytics that meets the demands of modern business intelligence requirements.

This architecture ensures that enterprise reporting remains efficient, responsive, and scalable, providing actionable insights to decision-makers without overloading the underlying Lakehouse infrastructure or compromising user experience.

Question 20

You are designing an ETL process to load large datasets from multiple sources into a Lakehouse. You want to track data lineage for compliance. Which Fabric feature should you use?

A) Data lineage tracking in Synapse Pipelines
B) Power BI Datasets
C) Manual documentation
D) DirectQuery

Answer: A) Data lineage tracking in Synapse Pipelines

Explanation:

In modern enterprise environments, understanding and managing the flow of data from source systems to final analytics is a critical component of effective data governance and operational efficiency. Power BI Datasets, while highly effective for analytics and visualization, do not inherently capture the complete lineage of data as it moves through the various stages of extraction, transformation, and loading. This limitation can pose challenges for organizations that need to maintain strict compliance, ensure data quality, and provide audit-ready insights into how information is processed and transformed.

Relying on manual documentation to track data lineage is an option, but it is fraught with potential errors and is difficult to maintain, particularly in complex systems with multiple data sources, transformations, and destinations. Over time, manual tracking can become outdated, incomplete, or inconsistent, leaving gaps in understanding how data flows through the organization’s infrastructure. This lack of clarity can lead to mistakes in reporting, inefficient troubleshooting, and difficulties demonstrating compliance with internal policies or external regulations.

DirectQuery, which allows Power BI to query data live from the source, can provide up-to-date information, but it does not solve the lineage challenge. While it ensures that the dataset reflects the latest data, it does not record or visualize the steps that data has undergone during the ETL process, leaving a blind spot in governance and audit capabilities. Organizations that require a comprehensive understanding of the transformations applied to data before it reaches reporting layers need more robust solutions.

Synapse Pipelines offer a powerful alternative for tracking data lineage in a systematic and automated manner. By integrating lineage tracking into the ETL workflow, Synapse Pipelines provide a visual representation of data flow from the original source systems, through all transformations and processing steps, to the destination datasets. This visibility enables data engineers and analysts to understand not only where the data originated but also how it has been modified, aggregated, or enriched along the way. Such detailed tracking is invaluable for troubleshooting, ensuring data quality, and maintaining transparency for audit and compliance purposes.

Moreover, automated data lineage tracking enhances governance by making it easier to enforce policies regarding data handling and access. Organizations can quickly identify sensitive data, trace its path through the ETL processes, and ensure that security and compliance measures are applied consistently. It also supports collaboration across teams, as stakeholders can clearly see the dependencies between datasets, transformations, and reporting outputs without relying on manually maintained documentation.

while Power BI Datasets excel at analytics and visualization, they lack native capabilities for full data lineage tracking. Manual methods are error-prone and insufficient for enterprise-scale operations, and DirectQuery does not address transformation visibility. Synapse Pipelines, with integrated lineage tracking, provide a visual and comprehensive view of the ETL process, capturing the full journey of data from source to destination. This capability ensures compliance, enables effective troubleshooting, strengthens governance, and is essential for organizations aiming to maintain reliable, transparent, and auditable data workflows in large-scale, enterprise environments.

This approach not only reduces operational risk but also empowers organizations to make informed, data-driven decisions while maintaining regulatory compliance and operational integrity.

Question 21

You need to ensure that multiple teams can collaborate on Spark notebooks without overwriting each other’s changes. Which Fabric feature supports this?

A) Version control and Git integration
B) Incremental refresh
C) Power BI Dataset
D) DirectQuery

Answer: A) Version control and Git integration

Explanation:

In contemporary data engineering and analytics workflows, collaboration is a crucial component for ensuring efficiency, accuracy, and innovation. While incremental refresh is a powerful mechanism for optimizing data ingestion by processing only new or updated records, it primarily focuses on improving performance and does not provide features that facilitate collaborative work within notebooks. Similarly, Power BI Datasets and DirectQuery are highly effective tools for analytics and live querying but are not designed to handle the complexities of collaborative development or version management in notebook-based environments.

To address these limitations, version control and Git integration have become essential for teams working with Spark notebooks. These tools enable multiple users to simultaneously edit notebooks, ensuring that contributions from different team members can be managed effectively. Through branching, developers can experiment with new logic or transformation pipelines without affecting the production environment. This separation allows for testing and iterative development, reducing the risk of errors and enhancing overall workflow reliability. By creating branches for feature development or troubleshooting, teams can safely isolate changes until they are validated and ready to merge into the main codebase.

Git integration also provides comprehensive change tracking, capturing every modification made to notebooks, including additions, deletions, and edits. This ensures reproducibility by allowing teams to identify precisely when and how a change was introduced. If issues arise, the ability to roll back to previous versions ensures that production workflows remain stable, avoiding disruptions in data processing or analytics. Moreover, maintaining a detailed audit trail enhances accountability, as team members can be linked to specific changes, fostering responsibility and transparency in collaborative projects.

Merging changes from multiple contributors is another critical aspect of collaborative notebook development. Git provides structured mechanisms for handling conflicts that may arise when different team members modify the same section of a notebook. These merge tools allow developers to reconcile differences efficiently, preserving valuable work while preventing data loss or workflow inconsistencies. Additionally, by integrating notebooks with centralized repositories, organizations can enforce coding standards, implement review processes, and monitor progress, which supports best practices in enterprise data engineering.

In large-scale data engineering projects, multiple teams often work on different components simultaneously, including data ingestion pipelines, transformation logic, and machine learning workflows. Leveraging Git and version control ensures that these teams can collaborate seamlessly, maintain synchronization, and minimize the risk of conflicting changes. This collaborative environment not only improves productivity but also encourages knowledge sharing, reduces redundancy, and strengthens governance over data processing workflows.

By combining incremental refresh for efficient data ingestion with robust version control and Git integration for collaborative notebook management, organizations can achieve a balanced approach that addresses both performance and teamwork. Teams benefit from fast, reliable data processing while maintaining the ability to innovate, iterate safely, and uphold high standards of reproducibility and accountability in their data engineering operations. This approach enables organizations to build resilient, collaborative, and efficient data workflows capable of supporting complex analytics and machine learning initiatives.

Question 22

You want to combine structured data from SQL databases with unstructured JSON logs for analysis in Fabric. Which approach is optimal?

A) Load data into Lakehouse Tables using Synapse Pipelines and transform using Spark notebooks
B) Load all data directly into Power BI
C) Use Dataflows only
D) Export JSON logs to Excel

Answer: A) Load data into Lakehouse Tables using Synapse Pipelines and transform using Spark notebooks

Explanation:

Loading large datasets directly into Power BI or exporting them to Excel often becomes inefficient, especially when the volume of information grows beyond what these tools can comfortably handle. Power BI’s direct ingestion methods are designed for interactive analytics rather than heavy data engineering tasks, and Excel is limited by its row capacity and performance constraints. Because of these limitations, organizations dealing with substantial or complex data frequently need an alternative strategy that can manage scale, maintain performance, and support advanced transformations.

Dataflows, while useful for certain transformation tasks, are not always ideal when working with deeply nested or highly unstructured JSON files. Their transformation capabilities tend to be more suited to semi-structured or tabular datasets, and handling intricate JSON structures can become cumbersome or even impractical. As a result, data engineers often need a more sophisticated environment to process these formats efficiently and reliably.

Synapse Pipelines offer a more powerful and flexible option for data ingestion. With these pipelines, it becomes possible to bring data in from numerous sources, whether they are databases, cloud storage systems, APIs, or other enterprise applications. The orchestration capabilities allow teams to design robust, automated workflows that reliably acquire data on a schedule or through event triggers. This creates an end-to-end pipeline that can move and organize large quantities of raw information without manual intervention.

To further refine and shape the ingested data, Spark notebooks play a crucial role. They provide a scalable and code-driven environment where complex JSON structures can be parsed, expanded, and transformed into clean, structured formats. Spark’s distributed processing engine ensures that even massive datasets can be processed efficiently, reducing the time needed for heavy transformations. Developers can apply custom logic, perform schema evolution, filter data, and integrate multiple datasets in ways that are not feasible within more limited transformation tools.

Once the data has been standardized and properly structured, Lakehouse Tables become an ideal destination for storage. These tables are built for scalability and optimized performance, allowing large volumes of data to be stored in an organized manner that remains accessible for downstream analysis. They support transactional consistency, which helps maintain data quality and reliability, even when numerous processes or users interact with the data simultaneously. Additionally, Lakehouse Tables enable fast querying, making them appropriate for reporting and analytical workloads.

This combination of Synapse Pipelines for ingestion, Spark notebooks for transformation, and Lakehouse Tables for storage results in a highly efficient data architecture. It ensures that large datasets can be managed without performance bottlenecks, while also guaranteeing that the data remains accurate and trustworthy throughout the process. Furthermore, the approach supports seamless integration with other analytics and machine learning tools available within Fabric, allowing organizations to extract insights, build advanced models, and share meaningful information across teams.

By adopting this strategy, organizations can handle complex data at scale, maintain high-quality transformations, and build a foundation that supports advanced analytics and future growth.

Question 23

You need to allow analysts to query semi-structured log data without moving it from the lake. Which service is most appropriate?

A) Lakehouse Tables
B) Power BI Dataset
C) Dataflows
D) Synapse Pipelines

Answer: A) Lakehouse Tables

Explanation:

Power BI Datasets and Dataflows require preprocessed data, and Synapse Pipelines orchestrates ETL but does not allow direct querying. Lakehouse Tables can store semi-structured data in formats like JSON or Parquet, enabling analysts to query it directly without data movement. Features like ACID transactions, indexing, and partitioning optimize performance and ensure consistency. This allows efficient exploration of raw logs while maintaining data governance and minimizing storage duplication.

Question 24

You want to reduce storage and improve query performance for large historical datasets in Lakehouse. Which technique should you implement?

A) Partitioning and compaction
B) Incremental refresh
C) DirectQuery
D) Dataflows

Answer: A) Partitioning and compaction

Explanation:

Incremental refresh optimizes ingestion but does not affect storage or query performance for historical datasets. DirectQuery enables live access but does not reduce storage. Dataflows handle ETL but do not optimize large datasets in storage. Partitioning divides large datasets into logical segments, allowing queries to scan only relevant partitions. Compaction reduces small files into larger optimized files, improving query performance and reducing storage overhead. These techniques are critical for managing and querying historical datasets efficiently in Fabric Lakehouse.

Question 25

You want to track the impact of upstream changes in multiple ETL pipelines on downstream reports. Which Fabric feature supports this?

A) Data lineage visualization in Synapse Pipelines
B) Power BI Dataset
C) DirectQuery
D) Lakehouse Tables

Answer: A) Data lineage visualization in Synapse Pipelines

Explanation:

Power BI Datasets and DirectQuery focus on reporting and live queries but do not capture full upstream dependencies. Lakehouse Tables store data but do not track pipeline flows. Data lineage visualization in Synapse Pipelines provides a comprehensive view of data transformations and dependencies, allowing engineers and analysts to understand how changes in upstream sources affect downstream reports. This ensures traceability, compliance, and effective troubleshooting.

Question 25

You want to track the impact of upstream changes in multiple ETL pipelines on downstream reports. Which Fabric feature supports this?

A) Data lineage visualization in Synapse Pipelines
B) Power BI Dataset
C) DirectQuery
D) Lakehouse Tables

Answer: A) Data lineage visualization in Synapse Pipelines

Explanation:

Question 26

You need to create a reusable transformation process that business users can maintain without writing code. Which Microsoft Fabric feature is most appropriate?

A) Dataflows
B) Notebooks
C) Lakehouse Tables
D) Direct Lake mode

Answer: A) Dataflows

Explanation:

Notebooks provide powerful transformation capabilities, but they require programming knowledge, making them unsuitable for business users who need low-code or no-code solutions. Lakehouse Tables serve as storage and compute layers but do not provide transformation logic. Direct Lake mode enables high-performance report queries directly on the Lakehouse but does not address data transformation needs. Dataflows offer a low-code environment for creating repeatable ETL processes using a graphical interface. They allow business users to build transformations with ease, create reusable data preparation logic, and refresh the output on a schedule. By supporting connectors, transformation steps, and output to Lakehouse or Datamarts, Dataflows are designed to democratize data preparation and empower business teams. They integrate with the wider Fabric ecosystem, ensuring governance and reusability. Ultimately, Dataflows are the best choice when the objective is to enable repeated, maintainable transformations without requiring coding knowledge.

Question 27

A data engineering team needs to build a high-performance analytical model that directly uses Delta tables in a Lakehouse without importing data. What should they use?

A) Direct Lake mode
B) Import mode
C) Dataflows
D) Copy activity

Answer: A) Direct Lake mode

Explanation:

Import mode creates a cached copy of data inside a Power BI dataset, increasing redundancy and refresh overhead. Dataflows prepare and store transformed data but do not enable direct analytical querying at scale. Copy activity is designed for moving data between systems and does not create analytical models. Direct Lake mode enables Power BI datasets to connect directly to the underlying Delta tables in the Lakehouse without importing data. It allows real-time analytics with low latency, high performance, and minimal duplication. This approach uses the storage engine of the Lakehouse directly, reducing refresh time and maintaining a single source of truth. It is designed for Fabric’s unified architecture and is preferred for large enterprise datasets requiring instant availability. Direct Lake mode is therefore the most effective choice for real-time analytics on Delta Lakehouse storage.

Question 28

You want to ingest streaming data from an external event source into Microsoft Fabric for near real-time processing. Which service should you use?

A) Eventstream
B) Dataflows
C) Power BI Dataset
D) DirectQuery

Answer: A) Eventstream

Explanation:

Dataflows cannot handle streaming ingestion because they operate in batch mode. Power BI Dataset does not ingest data; it only consumes data for reporting. DirectQuery supports live querying but does not pull streaming data into Fabric. Eventstream is designed specifically for real-time ingestion and processing of streaming data. It supports capture from sources such as Event Hubs, Kafka, and custom event publishers. Eventstream allows routing data to destinations like Lakehouse Tables, KQL Databases, and real-time dashboards. It provides transformations, filters, and routing logic for processing data as it arrives. This makes it the ideal solution for near real-time analytics and time-sensitive business processes such as IoT telemetry, operational monitoring, and event-driven pipelines.

Question 29

A team needs to run distributed data transformations on large datasets, supporting both Python and SQL. Which Fabric compute engine is appropriate?

A) Spark
B) Power BI semantic model
C) Dataflow Gen2
D) SQL endpoint

Answer: A) Spark

Explanation:

Power BI semantic models support analytics but cannot execute large-scale transformations. Dataflow Gen2 performs ETL operations but lacks full distributed compute capabilities and advanced languages. SQL endpoints provide relational queries but are not optimized for heavy distributed transformations. Spark is a distributed compute engine that supports Python, SQL, Scala, and R, making it ideal for large-scale transformations. Spark notebooks and Spark jobs in Fabric allow parallel processing, making them suitable for handling large datasets and complex pipelines. They directly integrate with the Lakehouse, providing high performance and flexible transformation capabilities.

Question 30

You must implement a governance mechanism ensuring that only approved data products are used in Analytics projects. Which Fabric capability should you enable?

A) Data catalog with endorsement
B) DirectQuery
C) Eventstream
D) Data gateway

Answer: A) Data catalog with endorsement

Explanation:

DirectQuery is a connectivity mode, not a governance feature. Eventstream handles streaming data ingestion, unrelated to approval processes. Data gateway connects on-premises data sources but does not control data product approval. The data catalog with endorsement allows administrators and stewards to tag datasets, Lakehouse items, and dataflows as certified or promoted. This ensures that analysts use only trusted and validated data products. Endorsement improves governance, reduces duplication, and enhances trust across the organization, making it the correct choice.

Microsoft DP-700 Implementing Data Engineering Solutions Using Microsoft Fabric Exam Dumps and Practice Test Questions Set 2 Q16-30

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