Microsoft DP-700 Implementing Data Engineering Solutions Using Microsoft Fabric Exam Dumps and Practice Test Questions Set 5 Q81-100

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Question 81

Which Microsoft Fabric service allows you to build, schedule, and monitor ELT pipelines with built-in connectors for various data sources?

Answer:

A) Azure Data Factory
B) Power BI
C) Delta Lake
D) Synapse Analytics

Explanation:

The correct answer is A) Azure Data Factory. ADF is the central orchestration tool in Microsoft Fabric, enabling the design, scheduling, and monitoring of ELT pipelines. It supports hundreds of built-in connectors, allowing seamless integration with relational databases, SaaS applications, cloud storage, and more.

ADF pipelines allow for modular design, with reusable components and parameterization, enabling the same pipeline to work with multiple datasets or sources. Control flow activities, such as loops and conditional splits, allow complex business logic to be implemented efficiently.

ADF integrates with Delta Lake for data storage, Databricks for transformations, and Synapse Analytics or Power BI for downstream analytics. Monitoring, logging, and alerts ensure operational reliability, while integration with Purview and Azure Key Vault ensures governance and security compliance.

For DP-700, understanding ADF’s orchestration, scheduling, monitoring, and integration capabilities is crucial for implementing scalable, reliable, and governed ELT workflows across Microsoft Fabric.

Question 82

Which Microsoft Fabric feature provides schema enforcement, ACID transactions, and time-travel queries for data lakes?

Answer:

A) Delta Lake
B) Azure Data Factory
C) Power BI
D) Synapse Analytics

Explanation:

The correct answer is A) Delta Lake. Delta Lake converts raw data lakes into enterprise-ready lakehouse architectures. ACID compliance ensures data consistency during concurrent writes, while schema enforcement prevents invalid data ingestion.

Time-travel queries allow users to access historical versions of data for auditing, debugging, and reproducing results. Integration with Databricks enables distributed transformations, while ADF orchestrates pipelines and Synapse/Power BI enables analytics.

For DP-700, understanding Delta Lake’s role in maintaining reliable, consistent, and auditable datasets is essential for designing robust data pipelines. Engineers must leverage incremental processing and time-travel features for optimized, fault-tolerant ETL workflows.

Question 83

Which Microsoft Fabric service allows interactive visualization, dashboarding, and self-service analytics on curated datasets?

Answer:

A) Power BI
B) Azure Databricks
C) Delta Lake
D) Azure Data Factory

Explanation:

The correct answer is A) Power BI. Power BI enables business users to interactively explore datasets, create visualizations, and generate insights without heavy reliance on engineering teams.

It connects to curated datasets in Delta Lake, Synapse Analytics, or Databricks outputs, supporting DirectQuery and live connections. Power BI allows filtering, slicers, drill-throughs, and bookmarks for interactive exploration.

For DP-700, understanding how to design pipelines that provide clean, curated datasets for Power BI consumption is crucial. Engineers must ensure data quality, governance, and performance for downstream analytics and reporting workflows.

Question 84

Which Microsoft Fabric feature ensures secure, role-based access control and policy enforcement for enterprise data storage?

Answer:

A) ADLS Gen2 Access Control
B) Delta Lake
C) Azure Data Factory
D) Power BI

Explanation:

The correct answer is A) ADLS Gen2 Access Control. ADLS Gen2 provides enterprise-grade security via Role-Based Access Control (RBAC) and Access Control Lists (ACLs), ensuring only authorized users and processes can access sensitive datasets.

Integration with ADF, Databricks, and Purview ensures secure pipelines, governed transformations, and compliance with organizational policies. For DP-700, understanding how to implement and manage access control is critical for protecting enterprise data and supporting regulatory compliance.

Question 85

Which Microsoft Fabric service provides centralized data governance, classification, and lineage tracking for all datasets?

Answer:

A) Microsoft Purview
B) Delta Lake
C) Azure Data Factory
D) Power BI

Explanation:

The correct answer is A) Microsoft Purview. Purview provides automated discovery, classification, cataloging, and lineage tracking for datasets across Microsoft Fabric.

Purview ensures that sensitive data is labeled correctly, access policies are enforced, and all transformations and movements are traceable. Integration with Delta Lake, ADF, Databricks, Synapse, and Power BI ensures governance is applied consistently.

For DP-700, mastering Purview is essential to implement enterprise-wide governance, ensure compliance, and maintain trusted and discoverable datasets for analytics and reporting.

Question 86

Which Microsoft Fabric service enables incremental data processing to optimize ETL pipeline performance and reduce compute costs?

Answer:

A) Delta Lake
B) Power BI
C) Azure Data Factory
D) Synapse Analytics

Explanation:

The correct answer is A) Delta Lake. Delta Lake provides the capability for incremental data processing, which is a fundamental concept in building efficient, reliable, and scalable ETL pipelines in Microsoft Fabric. Incremental processing allows pipelines to process only new or changed data rather than reprocessing the entire dataset, which significantly reduces compute costs, improves processing speed, and ensures operational efficiency—critical aspects for enterprise-scale data engineering solutions.

Incremental processing works by leveraging Delta Lake’s transaction log, which records all changes made to datasets, including inserts, updates, and deletes. This log allows the data engineering workflow to identify which data has changed since the last pipeline run. For example, in a sales dataset containing millions of records, only the newly added transactions or updates to existing transactions need to be processed during each ETL execution, rather than reprocessing all historical sales data.

This capability is particularly important in large enterprises where datasets can span terabytes or even petabytes. Processing the entire dataset repeatedly would be cost-prohibitive and time-consuming. Delta Lake’s incremental processing ensures that data pipelines remain efficient, timely, and cost-effective while maintaining data consistency and integrity.

In addition to reducing costs, incremental processing improves the agility of analytics workflows. Business users can access updated datasets more frequently, enabling near-real-time insights for operational reporting, decision-making, or machine learning model scoring. For example, a pipeline processing daily transaction logs can update customer analytics metrics incrementally, providing up-to-date insights into sales trends, customer behavior, or operational KPIs without the delay of full dataset processing.

Delta Lake ensures that incremental processing is reliable by leveraging ACID transactions. Even in the case of concurrent updates or streaming data, transactions are handled atomically, consistently, and durably. This prevents conflicts, duplicate records, or data corruption, which are common challenges in traditional data lake architectures. ACID compliance also ensures that incremental processing can safely be combined with batch processing or complex transformations without risking inconsistencies.

Schema enforcement is another critical feature in the context of incremental processing. As datasets evolve over time—such as adding new columns or changing data types—Delta Lake ensures that only compatible changes are applied. This prevents pipeline failures or downstream errors during incremental updates, providing a stable and predictable environment for data engineers.

Time-travel queries complement incremental processing by allowing engineers to access historical versions of the dataset. This is particularly useful for auditing, troubleshooting, or reproducing analytics results. For instance, if a discrepancy is observed in a sales report, engineers can query the dataset as it existed before a recent pipeline run to investigate the root cause. This capability supports transparency, compliance, and governance in enterprise data workflows.

Delta Lake’s incremental processing integrates seamlessly with other Microsoft Fabric services. Azure Data Factory can orchestrate incremental pipelines by triggering Databricks notebooks that read only changed data from Delta Lake tables, transform it, and load it into curated layers. Synapse Analytics can then query these updated datasets for analytics, while Power BI can visualize them for end-users. Purview ensures that all datasets remain classified, compliant, and traceable throughout the pipeline.

For DP-700 candidates, understanding incremental processing is critical. Exam scenarios may include designing pipelines that minimize resource usage while ensuring that data remains accurate, timely, and auditable. Candidates must be able to leverage Delta Lake features, such as transaction logs, schema enforcement, and time-travel, to implement optimized ETL workflows. Additionally, knowledge of how incremental processing interacts with batch and streaming workloads, data transformations, and downstream analytics is essential for designing enterprise-grade solutions in Microsoft Fabric.

In summary, Delta Lake’s incremental processing capability optimizes ETL pipeline performance, reduces compute costs, ensures data reliability, and enables timely analytics. By integrating ACID-compliant transactions, schema enforcement, and time-travel queries, Delta Lake provides a robust foundation for enterprise-scale data engineering in Microsoft Fabric. Mastery of these concepts is essential for DP-700 candidates to implement efficient, reliable, and governed data pipelines.

Question 87

Which Microsoft Fabric service provides interactive dashboards for monitoring ETL pipelines, data quality, and operational metrics?

Answer:

A) Power BI
B) Azure Data Factory
C) Delta Lake
D) Synapse Analytics

Explanation:

The correct answer is A) Power BI. Power BI serves as the visualization and analytics layer in Microsoft Fabric, enabling interactive dashboards that monitor ETL pipelines, data quality, operational performance, and business KPIs. In enterprise environments, having a centralized view of pipeline execution and data health is critical for proactive management and decision-making.

Power BI integrates seamlessly with data engineering services such as Azure Data Factory, Databricks, Delta Lake, and Synapse Analytics. Pipelines in ADF can generate logs, metrics, and data quality indicators, which Power BI can visualize in near-real-time. For example, engineers can track the success rate of ETL activities, detect failed data loads, monitor execution duration, or measure throughput in terms of records processed per hour.

Data quality monitoring is particularly important. Power BI dashboards can display metrics such as missing data, schema mismatches, invalid records, and duplicate entries. By visualizing these metrics, engineers can quickly identify issues, understand their impact on downstream analytics, and initiate corrective actions. This reduces errors in reporting, analytics, and machine learning workflows.

Operational metrics, such as compute usage, storage consumption, and processing time, can also be visualized in Power BI. These insights allow engineering teams to optimize pipelines, manage costs, and plan for scaling compute resources. For instance, dashboards can highlight peak processing times or resource bottlenecks, informing decisions on cluster sizing in Databricks or adjusting pipeline scheduling in ADF.

Power BI’s interactivity enhances exploration and collaboration. Users can drill down into specific pipeline runs, filter metrics by source system or data domain, and compare historical performance trends. This level of granularity enables detailed root-cause analysis, helping engineers understand why a particular pipeline failed or experienced delays.

For DP-700 candidates, understanding how to integrate Power BI with ETL workflows is essential. Candidates should know how to design dashboards that display pipeline performance, data quality metrics, and operational KPIs. They should also understand how to connect Power BI to curated datasets, Delta Lake tables, Synapse Analytics outputs, and logs from ADF or Databricks for comprehensive monitoring.

Power BI also supports alerting and automated responses. Engineers can configure alerts based on threshold breaches, such as when error rates exceed a predefined limit or when data quality metrics fall below acceptable standards. These alerts can trigger notifications or automated remediation pipelines, reducing downtime and ensuring continuous operational reliability.

From a governance perspective, Power BI integrates with Microsoft Purview to enforce data classification, sensitivity labels, and role-level security. Users see only the data they are authorized to access, and lineage information can be displayed to show the origin and transformation path of the metrics being visualized. This ensures compliance and transparency across the organization.

In addition to monitoring, Power BI dashboards support strategic planning. By visualizing trends in pipeline efficiency, processing volumes, and data quality over time, engineering and management teams can make informed decisions about infrastructure investments, workflow optimization, and resource allocation.

In conclusion, Power BI provides interactive dashboards for monitoring ETL pipelines, data quality, and operational metrics. Its integration with Azure Data Factory, Databricks, Delta Lake, and Synapse Analytics enables a comprehensive view of enterprise data workflows. For DP-700, mastering Power BI dashboards and their connection to curated datasets is essential for ensuring operational efficiency, proactive management, and actionable insights in Microsoft Fabric.

Question 88

Which Microsoft Fabric feature allows real-time ingestion and processing of streaming data from sources like IoT devices or Event Hubs?

Answer:

A) Azure Databricks
B) Power BI
C) Delta Lake
D) Synapse Analytics

Explanation:

The correct answer is A) Azure Databricks. Azure Databricks provides a powerful platform for real-time ingestion, transformation, and processing of streaming data at scale. This capability is crucial for scenarios such as IoT telemetry, financial transactions, web analytics, and operational monitoring, where timely insights are required to drive decisions or trigger automated actions.

Databricks leverages Apache Spark’s Structured Streaming engine to process data streams continuously or in micro-batches. This ensures high throughput, low latency, and fault-tolerant processing. The service can connect to multiple streaming sources, including Azure Event Hubs, Kafka, IoT Hubs, or custom streaming endpoints, allowing enterprises to capture real-time events from diverse applications.

One of the most critical aspects of streaming pipelines is ensuring reliability and consistency. Databricks accomplishes this through its integration with Delta Lake, which provides ACID-compliant storage. This guarantees that streaming data is written transactionally, preventing data loss or duplication even in the case of failures or retries. Additionally, Delta Lake supports schema enforcement and schema evolution, ensuring that incoming streaming data aligns with predefined structures while allowing flexibility for changes over time.

Engineers can implement windowed aggregations, joins with static or slowly changing reference data, and enrichment of streaming datasets within Databricks notebooks. For example, IoT sensor data can be aggregated to calculate operational metrics, combined with historical data for trend analysis, or joined with master reference datasets to provide context such as device location or configuration. These transformations can be performed in near-real-time, enabling immediate insights.

Integration with Azure Data Factory is essential for orchestrating streaming pipelines. ADF can trigger Databricks notebooks, manage dependencies, and monitor pipeline health. Synapse Analytics or Power BI can consume processed streaming data for analytics, visualization, and operational dashboards. This end-to-end integration ensures that real-time insights are delivered efficiently, reliably, and securely across the organization.

Monitoring and alerting are also critical in streaming scenarios. Databricks provides metrics on processing rates, error counts, and checkpointing. These metrics can be visualized in Power BI dashboards or integrated with Azure Monitor to trigger alerts if anomalies are detected. For example, an unusually high number of failed messages from IoT sensors could indicate device malfunctions or network issues, prompting immediate investigation.

From a DP-700 perspective, candidates are expected to understand how to design, implement, and monitor real-time data pipelines using Databricks and Delta Lake. This includes knowledge of micro-batching vs. continuous processing, fault tolerance mechanisms, schema management, integration with ADF for orchestration, and downstream analytics consumption in Synapse or Power BI. Additionally, candidates must understand how to ensure reliability, maintain data lineage, and comply with governance standards using Purview.

Streaming data workflows require careful consideration of performance, resource management, and cost optimization. Databricks allows partitioning, caching, and optimization strategies to handle high-volume data efficiently. Engineers can also leverage cluster autoscaling to adjust resources dynamically, ensuring cost-effectiveness while maintaining low-latency processing.

Security and compliance are equally important. Data in motion must be protected using encryption at rest and in transit, secure authentication, and access control. Integration with Azure Key Vault ensures that secrets and credentials are managed securely. Role-based access control and Purview integration ensure that only authorized users can access streaming data, supporting enterprise compliance requirements.

In conclusion, Azure Databricks enables real-time ingestion and processing of streaming data from IoT devices, Event Hubs, or other sources. Its integration with Delta Lake ensures transactional reliability, schema enforcement, and incremental processing, while orchestration with ADF and visualization in Power BI provides end-to-end enterprise-grade solutions. For DP-700, mastering streaming pipeline design, fault tolerance, incremental processing, schema management, monitoring, and governance is essential for implementing scalable and reliable real-time data workflows in Microsoft Fabric.

Question 89

Which Microsoft Fabric feature provides centralized data governance, classification, and lineage tracking across all datasets in the organization?

Answer:

A) Microsoft Purview
B) Delta Lake
C) Azure Data Factory
D) Power BI

Explanation:

The correct answer is A) Microsoft Purview. Microsoft Purview is the centralized data governance platform within Microsoft Fabric. It allows organizations to automatically discover, classify, catalog, and trace the lineage of datasets across multiple storage and processing systems. Effective governance is critical for ensuring compliance, security, and trust in data used for analytics, reporting, and machine learning.

Purview scans and catalogs datasets from diverse sources, including ADLS Gen2, Delta Lake tables, Databricks, Synapse Analytics, and on-premises or SaaS systems. During the discovery process, metadata such as schema, data types, and relationships are captured, creating a comprehensive inventory of available datasets. This enables stakeholders to understand what data exists, where it comes from, and how it is being used.

Classification and sensitivity labeling are core capabilities. Data engineers can label datasets with categories like Personally Identifiable Information (PII), financial data, health records, or internal business information. These labels enforce access controls and support regulatory compliance with GDPR, HIPAA, SOC 2, or other standards. For example, a dataset containing customer phone numbers and email addresses can be classified as sensitive PII, ensuring that only authorized users can access it.

Lineage tracking provides end-to-end visibility into how data moves and transforms across the organization. It shows the source of the data, the transformations applied in Databricks or Synapse, and the final consumption in Power BI dashboards or reports. Lineage is critical for debugging, auditing, and ensuring transparency. For example, if a report shows unexpected results, engineers can trace back through Purview to identify which pipeline, transformation, or source caused the anomaly.

Purview also integrates with Delta Lake to provide insights into dataset versions, enabling time-travel awareness and versioned lineage tracking. Engineers can identify which version of a dataset was used in a particular analysis, supporting reproducibility and accountability. This is crucial for regulatory audits, machine learning model reproducibility, and operational troubleshooting.

Monitoring and governance are enhanced with automated policies. Purview can enforce rules for data retention, access permissions, or transformation approvals. For example, sensitive data cannot be moved to an unclassified storage account without triggering a policy alert. Integration with Azure Active Directory ensures secure authentication and role-based access, while Purview’s APIs allow automated enforcement across pipelines.

For DP-700, candidates are expected to understand how Purview interacts with the Microsoft Fabric ecosystem. This includes integrating Purview with ADF pipelines for governance-aware orchestration, Delta Lake for versioned data management, Databricks for secure transformations, and Power BI for governed reporting. Candidates must be able to apply classification, lineage, and policy management to ensure enterprise-wide compliance and reliable data operations.

Purview also supports collaboration and data discovery. Analysts, engineers, and business users can search for authoritative datasets, view their lineage, understand data quality, and access governance metadata before using them. This reduces redundancy, prevents misuse of sensitive data, and fosters trust in enterprise analytics.

In conclusion, Microsoft Purview provides centralized governance, classification, and lineage tracking across all enterprise datasets. Its integration with Delta Lake, Databricks, ADF, Synapse, and Power BI ensures comprehensive visibility, compliance, and accountability. Mastery of Purview is essential for DP-700 candidates to implement secure, compliant, and trusted data pipelines in Microsoft Fabric.

Question 90

Which Microsoft Fabric service enables visual, low-code transformations of datasets for analytics workflows?

Answer:

A) Power Query
B) Azure Databricks
C) Delta Lake
D) Synapse Analytics

Explanation:

The correct answer is A) Power Query. Power Query is a low-code, visual data preparation tool in Microsoft Fabric that allows users to clean, transform, and shape datasets before analytics or reporting. It is integrated into Power BI, Dataflows, and other services, enabling repeatable and maintainable data transformations.

With Power Query, users can merge, filter, pivot, unpivot, aggregate, and enrich datasets without writing code. This is particularly valuable in self-service analytics scenarios, where business analysts need to quickly prepare data for reports or dashboards. For example, transactional data can be aggregated by region or product category, or missing values can be imputed, ensuring that the data is analytics-ready.

Power Query supports connection to multiple sources, including Delta Lake tables, Synapse Analytics, SQL databases, and external files. Transformations are recorded in a series of steps that are fully repeatable, allowing pipelines to refresh data automatically as new data arrives. Integration with ADF and Delta Lake ensures that these transformations can be applied consistently across enterprise workflows.

From a DP-700 perspective, candidates must understand Power Query’s role in preparing curated datasets, ensuring data quality, and enabling efficient analytics pipelines. It reduces the need for custom code, improves maintainability, and ensures consistency in transformed datasets used for reporting or machine learning.

In conclusion, Power Query provides visual, low-code transformations of datasets, enabling engineers and analysts to prepare high-quality, analytics-ready data efficiently. Its integration with Microsoft Fabric services such as Delta Lake, Power BI, and ADF ensures reliable, repeatable, and governed data workflows, making it an essential tool for DP-700 candidates to master.

Question 91

Which Microsoft Fabric service provides a unified analytics engine for querying both structured and unstructured data across multiple storage systems?

Answer:

A) Synapse Analytics
B) Power BI
C) Azure Databricks
D) Delta Lake

Explanation:

The correct answer is A) Synapse Analytics. Synapse Analytics is a scalable analytics platform within Microsoft Fabric designed to query both structured and unstructured datasets from multiple sources, including relational databases, data lakes, Delta Lake tables, and semi-structured formats like JSON, Parquet, or Avro.

Synapse Analytics combines on-demand serverless querying with provisioned, dedicated SQL pools for high-performance analytics. Serverless SQL allows analysts and engineers to run ad-hoc queries against large datasets without managing compute resources, while dedicated SQL pools provide consistent performance for recurring analytics workloads.

Integration with Delta Lake ensures that curated, ACID-compliant datasets can be queried efficiently. Delta Lake provides schema enforcement, time-travel, and incremental processing, which Synapse can leverage to perform accurate analytics on historical or up-to-date data. For example, a financial dataset stored in Delta Lake can be queried in Synapse to generate reports or perform trend analysis while maintaining data integrity.

Synapse Analytics supports both batch and streaming queries. Streaming data ingested via Databricks or Event Hubs can be aggregated and analyzed in near-real-time, enabling operational insights. Complex analytical functions such as joins, windowed aggregations, and machine learning scoring can also be applied within Synapse.

Power BI integrates seamlessly with Synapse, allowing business users to visualize and explore the results of queries. Governance and lineage are maintained through Purview, ensuring that all data consumed in Synapse queries is compliant, classified, and traceable.

For DP-700 candidates, understanding Synapse Analytics is essential. Candidates must know how to query structured and unstructured datasets, integrate with Delta Lake, optimize performance, and support downstream analytics in Power BI. Designing end-to-end analytics workflows requires knowledge of incremental updates, security, compliance, and resource management to ensure scalable, reliable, and governable solutions in Microsoft Fabric.

Question 92

Which Microsoft Fabric feature enables ACID-compliant storage and transaction logging for large-scale data lake tables?

Answer:

A) Delta Lake
B) Power BI
C) Azure Data Factory
D) Synapse Analytics

Explanation:

The correct answer is A) Delta Lake. Delta Lake is critical in Microsoft Fabric for providing reliable, ACID-compliant storage for large-scale data lake tables. Transaction logging ensures that operations such as inserts, updates, deletes, and merges are executed atomically and consistently, preventing data corruption in concurrent or distributed pipelines.

Delta Lake’s transaction log allows incremental processing, which is essential for efficient ETL pipelines. By tracking only new or changed data, pipelines avoid unnecessary computation and reduce costs. Time-travel queries enable access to historical dataset versions for auditing, debugging, or reproducing analytical results.

Integration with Databricks allows distributed transformations on Delta Lake tables. ADF orchestrates these transformations, while Synapse Analytics and Power BI provide analytics and visualization layers. Governance is enforced through Purview, ensuring compliance with enterprise policies and regulatory standards.

For DP-700, understanding Delta Lake’s ACID compliance, transaction logging, incremental processing, and time-travel capabilities is critical for designing robust, scalable, and reliable data engineering workflows in Microsoft Fabric.

Question 93

Which Microsoft Fabric service provides low-code data preparation and transformation for curated datasets used in analytics?

Answer:

A) Power Query
B) Azure Databricks
C) Delta Lake
D) Synapse Analytics

Explanation:

The correct answer is A) Power Query. Power Query is a visual, low-code tool for cleaning, transforming, and shaping datasets for analytics workflows. Users can filter, merge, pivot, unpivot, aggregate, and enrich datasets without writing code, making it suitable for both analysts and data engineers.

Power Query integrates with Power BI, Dataflows, and ADF, enabling repeatable and maintainable data transformations. It ensures that datasets are analytics-ready and consistent, reducing errors in downstream reports or machine learning models.

For DP-700, understanding Power Query’s role in preparing curated datasets, enabling efficient data transformations, and supporting self-service analytics is essential. It helps create high-quality, reliable datasets that feed into Power BI dashboards or machine learning pipelines, reducing the need for extensive coding while maintaining governance and consistency.

Question 94

Which Microsoft Fabric feature supports distributed, multi-language transformations at scale for large datasets?

Answer:

A) Azure Databricks
B) Power BI
C) Delta Lake
D) Synapse Analytics

Explanation:

The correct answer is A) Azure Databricks. Databricks allows engineers to perform distributed, scalable transformations on large datasets using multiple programming languages, including Python, R, SQL, and Scala.

Databricks integrates with Delta Lake to maintain ACID compliance, schema enforcement, and incremental processing. It supports batch and streaming data pipelines, enabling complex transformations such as aggregations, joins, enrichment, and anomaly detection at scale.

For DP-700, candidates must understand how to implement distributed data transformations efficiently, optimize performance, integrate with Delta Lake, orchestrate with ADF, and deliver curated datasets for downstream analytics in Synapse or Power BI. Databricks is essential for handling enterprise-scale workloads and real-time processing in Microsoft Fabric.

Question 95

Which Microsoft Fabric service enables secure, role-based access and policy enforcement for enterprise datasets?

Answer:

A) ADLS Gen2 Access Control
B) Power BI
C) Delta Lake
D) Azure Databricks

Explanation:

The correct answer is A) ADLS Gen2 Access Control. ADLS Gen2 provides enterprise-grade security by enforcing role-based access control (RBAC) and access control lists (ACLs) for datasets stored in the data lake.

Integration with ADF, Databricks, Delta Lake, and Purview ensures that pipelines and transformations adhere to security policies and compliance requirements. Sensitive datasets are protected from unauthorized access, supporting regulatory compliance and enterprise governance standards.

For DP-700 candidates, mastering ADLS Gen2 security, access policies, and integration with data engineering pipelines is essential to implement secure, reliable, and compliant data workflows in Microsoft Fabric.

Question 96

Which Microsoft Fabric service enables orchestration of complex ETL workflows with scheduling, monitoring, and dependency management?

Answer:

A) Azure Data Factory
B) Power BI
C) Delta Lake
D) Synapse Analytics

Explanation:

The correct answer is A) Azure Data Factory (ADF). ADF is the orchestration backbone for Microsoft Fabric, enabling data engineers to design, schedule, and monitor complex ETL workflows that integrate multiple data sources, transformations, and destinations.

ADF pipelines are composed of activities (tasks such as data copy, transformations, or control flow operations), datasets (representing data structure and metadata), and linked services (connections to storage, compute, or external systems). This modular design allows pipelines to be reusable, parameterized, and scalable.

One key feature is dependency management. Engineers can implement sequential or parallel execution, loops, conditional logic, and error handling. For example, a pipeline could first ingest raw sales data from SQL Server, then transform it in Databricks, load it into Delta Lake, and finally trigger updates in Synapse Analytics for reporting. ADF ensures each step executes in the correct order, handling failures and retries automatically.

Scheduling is another critical aspect. Pipelines can be triggered on-demand, via schedule, or based on external events. This flexibility allows data engineers to implement batch or near-real-time workflows. Incremental processing can be configured, especially when integrated with Delta Lake, to reduce compute costs and improve efficiency by processing only new or changed data.

Monitoring and alerting are built into ADF, providing real-time visibility into pipeline execution. Engineers can track success rates, execution duration, data volume processed, and specific activity-level details. Integration with Azure Monitor and Log Analytics allows for custom alerts, anomaly detection, and automated remediation, reducing operational risk.

ADF also integrates with Purview for governance and lineage tracking. Every dataset and transformation can be traced, ensuring compliance with regulatory and organizational policies. Secure management of credentials through Azure Key Vault ensures pipelines are safe and follow best practices.

For DP-700 candidates, mastery of ADF is critical. Candidates must understand pipeline design, parameterization, orchestration of batch and streaming workloads, integration with Delta Lake and Databricks, monitoring, alerting, and governance. Designing scalable, reliable, and compliant ETL workflows is central to implementing Microsoft Fabric data engineering solutions.

In summary, Azure Data Factory orchestrates complex ETL workflows by managing dependencies, scheduling executions, monitoring performance, and integrating securely with storage and compute systems. Its capabilities for modularity, reusability, incremental processing, and governance make it indispensable for enterprise-scale data engineering solutions.

Question 97

Which Microsoft Fabric feature enables ACID-compliant transactions, schema enforcement, and versioned storage for lakehouse tables?

Answer:

A) Delta Lake
B) Power BI
C) Azure Data Factory
D) Synapse Analytics

Explanation:

The correct answer is A) Delta Lake. Delta Lake provides ACID-compliant storage for large-scale lakehouse tables, supporting reliable and consistent operations in multi-user and distributed data environments. ACID transactions ensure that inserts, updates, merges, and deletes are atomic and consistent, preventing conflicts or corruption when multiple pipelines operate concurrently.

Schema enforcement ensures that only valid data conforming to predefined structures is ingested, while schema evolution allows controlled modifications over time. For example, adding a new column to a sales dataset does not disrupt downstream analytics.

Delta Lake also provides time-travel queries, enabling engineers to access historical versions of datasets. This is critical for auditing, debugging, or reproducing analytical results. For example, if a business report is questioned, engineers can query the dataset as it existed when the report was generated to verify accuracy.

Integration with Databricks allows distributed processing of large datasets, supporting both batch and streaming transformations. ADF orchestrates these transformations, while Synapse Analytics and Power BI provide downstream analytics and visualization. Purview integration ensures governance, lineage, and compliance are maintained across all datasets.

For DP-700 candidates, understanding Delta Lake’s ACID compliance, schema enforcement, incremental processing, and time-travel capabilities is essential. Candidates must know how to leverage these features to design reliable, scalable, and governable pipelines in Microsoft Fabric.

In conclusion, Delta Lake enables ACID-compliant transactions, schema enforcement, incremental updates, and time-travel for lakehouse tables. These capabilities ensure data reliability, auditability, and performance, making Delta Lake a cornerstone of Microsoft Fabric data engineering solutions.

Question 98

Which Microsoft Fabric service provides interactive, visual dashboards and self-service analytics for business users?

Answer:

A) Power BI
B) Azure Databricks
C) Delta Lake
D) Synapse Analytics

Explanation:

The correct answer is A) Power BI. Power BI is the self-service analytics and visualization tool in Microsoft Fabric that enables business users and analysts to explore curated datasets interactively, create reports, and derive insights without deep technical knowledge.

Power BI integrates seamlessly with Delta Lake, Synapse Analytics, Databricks, and ADF outputs, supporting live connections and DirectQuery for near-real-time reporting. Users can apply filters, slicers, drill-throughs, bookmarks, and hierarchies to navigate complex datasets.

Data modeling in Power BI ensures accurate analytics. Calculated columns, measures, and relationships can be defined to support reporting requirements. Incremental refresh, query folding, and caching improve performance for large datasets.

Governance and compliance are enforced through integration with Purview. Sensitivity labels, role-level security, and lineage visibility ensure that users access only authorized datasets while maintaining transparency.

For DP-700 candidates, understanding how to design pipelines to feed high-quality, curated datasets into Power BI is critical. Knowledge of Power BI’s capabilities, integration points, data modeling, and governance ensures that analytics workflows are efficient, reliable, and compliant.

In conclusion, Power BI provides interactive dashboards, self-service analytics, and governance integration, allowing organizations to turn curated data into actionable insights. Mastery of Power BI is essential for DP-700 candidates to deliver enterprise-grade analytics solutions in Microsoft Fabric.

Question 99

Which Microsoft Fabric feature supports secure, enterprise-grade role-based access control and governance for large-scale datasets?

Answer:

A) ADLS Gen2 Access Control
B) Power BI
C) Delta Lake
D) Azure Databricks

Explanation:

The correct answer is A) ADLS Gen2 Access Control. ADLS Gen2 provides enterprise-grade security for datasets stored in the data lake, ensuring that only authorized users and processes can access sensitive data.

ADLS Gen2 supports both Role-Based Access Control (RBAC) and Access Control Lists (ACLs), providing fine-grained security. Integration with ADF, Databricks, Delta Lake, and Purview ensures that pipelines, transformations, and analytics workflows adhere to security and compliance policies.

For DP-700 candidates, understanding ADLS Gen2 security, permission management, and integration with Microsoft Fabric workflows is critical for implementing secure, compliant, and reliable data engineering pipelines.

In conclusion, ADLS Gen2 Access Control enforces secure, role-based permissions, ensuring enterprise datasets are protected while enabling collaboration and governance across Microsoft Fabric.

Question 100

Which Microsoft Fabric service allows orchestration, monitoring, and automated execution of data pipelines for batch and streaming workloads?

Answer:

A) Azure Data Factory
B) Power BI
C) Delta Lake
D) Synapse Analytics

Explanation:

The correct answer is A) Azure Data Factory. Azure Data Factory orchestrates complex data pipelines, supporting both batch and streaming workloads. It manages scheduling, dependencies, monitoring, and error handling, ensuring reliable pipeline execution.

ADF integrates with Delta Lake for incremental processing and time-travel, Databricks for distributed transformations, Synapse Analytics for analytical querying, and Power BI for visualization. Purview ensures governance and lineage are maintained across all datasets.

ADF’s parameterization allows pipelines to process multiple datasets using the same workflow, while control flow features support conditional logic, loops, and error handling. Monitoring dashboards and alerts ensure operational reliability and cost optimization.

For DP-700 candidates, mastery of ADF is critical. Designing scalable, reliable, and governable ETL pipelines that integrate with Microsoft Fabric services ensures enterprise-grade data engineering solutions.

In conclusion, Azure Data Factory is the orchestration and automation service for batch and streaming pipelines, enabling monitoring, governance, and efficient execution of enterprise data workflows in Microsoft Fabric.