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Question 106
You need to ingest multiple semi-structured data sources into a Fabric Lakehouse, handle schema evolution, and maintain historical versions for auditing and compliance. Which solution should you implement?
A) Manual JSON ingestion
B) Copy Data activity in a Data Pipeline with Delta tables and schema evolution
C) Notebook ingestion without versioning
D) Raw JSON storage
Answer: B) Copy Data activity in a Data Pipeline with Delta tables and schema evolution
Explanation
Manual JSON ingestion requires custom scripts for each source and schema change, which is operationally intensive and error-prone. Historical version tracking must be implemented manually, which increases complexity and operational risk.
Copy Data activity in a Data Pipeline with Delta tables and schema evolution provides a robust automated solution. Delta tables maintain a transaction log that records inserts, updates, and deletes, enabling rollback, time travel queries, and auditing. Schema evolution automatically accommodates new or modified fields, ensuring downstream analytics remain uninterrupted. Pipelines orchestrate ingestion, handle retries, and provide monitoring dashboards for operational visibility. This approach scales efficiently across multiple semi-structured sources while ensuring governance and compliance.
Notebook ingestion without versioning requires manual handling of schema changes and historical tracking, increasing operational risk and engineering effort.
Raw JSON storage captures raw data but lacks structure, ACID compliance, and historical tracking. Downstream pipelines must implement schema evolution and version tracking manually, increasing operational overhead.
Considering these factors, Copy Data activity in a Data Pipeline with Delta tables and schema evolution is the optimal solution for semi-structured ingestion with schema evolution and historical preservation.
Question 107
You need to compute aggregated metrics from high-volume IoT telemetry data every 10 minutes and store results for dashboards with historical tracking. Which approach is most suitable?
A) Dataflow Gen2 batch processing
B) Eventstream ingestion with windowed aggregation
C) Notebook batch processing
D) SQL scheduled import
Answer: B) Eventstream ingestion with windowed aggregation
Explanation
Dataflow Gen2 batch processing operates on scheduled batch workloads and cannot support high-frequency streaming data. Using batch refresh for 10-minute windows introduces latency, making dashboards less reliable for operational decision-making.
Eventstream ingestion with windowed aggregation is designed for high-frequency streaming workloads. Data is aggregated into 10-minute windows, computed, and stored in Delta tables. Delta tables provide ACID compliance, historical tracking, and time travel queries for auditing. Pipelines manage retries, fault tolerance, and monitoring dashboards, ensuring reliable and timely delivery of metrics. Late-arriving events are automatically incorporated into aggregates, maintaining accurate metrics.
Notebook batch processing provides flexibility but requires custom coding for windowed aggregation, retries, and schema handling. Processing high-frequency streaming data increases operational complexity and risk.
SQL scheduled import is batch-oriented and cannot efficiently provide near-real-time 10-minute aggregation. Latency reduces responsiveness and operational utility of dashboards.
Given requirements for streaming aggregation, low latency, and historical tracking, Eventstream ingestion with windowed aggregation is the optimal solution.
Question 108
You need to orchestrate multiple dependent Fabric pipelines with automated error handling, retries, and notifications. Which solution is optimal?
A) Manual pipeline execution
B) Pipeline triggers with dependencies and retry policies
C) Notebook-only orchestration
D) Ad hoc Dataflows Gen2 execution
Answer: B) Pipeline triggers with dependencies and retry policies
Explanation
Manual pipeline execution relies on human intervention and does not enforce dependencies. Failures in upstream pipelines can propagate to downstream workflows, reducing operational reliability. Notifications are also manual, increasing response time.
Pipeline triggers with dependencies and retry policies allow pipelines to execute sequentially or in parallel based on defined dependencies. Retry policies automatically handle transient failures, and automated notifications alert stakeholders to failures. Monitoring dashboards provide operational visibility, enabling proactive issue resolution. This solution ensures reliable orchestration, reduces operational risk, and supports governance and compliance for complex workflows.
Notebook-only orchestration triggers code execution but does not inherently manage dependencies, retries, or notifications. Scaling multiple notebooks manually increases complexity and operational risk.
Ad hoc Dataflows Gen2 execution supports isolated transformations but cannot orchestrate multiple dependent pipelines, enforce retries, or provide notifications. It is insufficient for enterprise-grade orchestration.
Considering these factors, pipeline triggers with dependencies and retry policies are the most robust and reliable solution.
Question 109
You need to merge incremental updates from multiple sources into a Delta table while preserving historical versions and supporting rollback. Which approach should you implement?
A) Overwrite Delta table
B) Delta table merge operations in a Data Pipeline
C) Notebook append only
D) SQL scheduled append
Answer: B) Delta table merge operations in a Data Pipeline
Explanation
Overwriting a Delta table removes existing records, destroying historical versions and preventing rollback, which is unsuitable for auditing or compliance.
Delta table merge operations in a Data Pipeline allow transactional inserts, updates, and deletes while preserving historical versions in the Delta transaction log. Time travel queries enable rollback and historical analysis. Pipelines handle orchestration, retries, and monitoring, ensuring operational reliability. Schema evolution accommodates source changes without breaking downstream pipelines. This approach provides a robust, enterprise-grade solution for incremental ingestion while maintaining historical tracking.
Notebook append only adds new records without handling updates or deletes. Maintaining historical accuracy or rollback requires custom coding, increasing operational complexity and risk.
SQL scheduled append inserts data in batches but cannot efficiently manage updates or deletions. Historical versioning is not preserved, and schema changes must be manually handled, reducing operational reliability.
Considering incremental updates, historical preservation, rollback, and governance, Delta table merge operations in a Data Pipeline are the optimal solution.
Question 110
You need to monitor multiple Fabric pipelines, detect failures, trigger retries, and maintain lineage for auditing and compliance. Which solution is most appropriate?
A) Dataflow Gen2 monitoring
B) Fabric Data Pipeline monitoring with integrated lineage
C) Manual SQL logging
D) KQL queries for retrospective analysis
Answer: B) Fabric Data Pipeline monitoring with integrated lineage
Explanation
Dataflow Gen2 monitoring provides basic refresh status and error messages but lacks end-to-end lineage, real-time alerts, and dashboards for monitoring multiple pipelines. It is insufficient for enterprise-scale monitoring and compliance.
Fabric Data Pipeline monitoring with integrated lineage offers comprehensive monitoring and governance capabilities. Dashboards visualize execution metrics, dependencies, and transformations. Real-time alerts notify stakeholders of failures, enabling rapid remediation. Integrated lineage ensures traceability for auditing, governance, and compliance. Automated retry mechanisms reduce downtime and maintain operational reliability. Both batch and streaming pipelines are supported, providing proactive monitoring and operational insights at scale.
Manual SQL logging captures execution details but does not provide real-time alerts, retries, or lineage tracking. Scaling for multiple pipelines using SQL logging increases operational overhead and risk.
KQL queries allow retrospective analysis but cannot provide proactive monitoring, real-time alerts, or lineage tracking. Delays in detecting issues reduce operational reliability and increase operational risk.
Considering these factors, Fabric Data Pipeline monitoring with integrated lineage is the most effective solution for monitoring multiple pipelines, detecting failures, triggering retries, and ensuring governance and compliance.
Question 111
You need to ingest multiple structured sources into a Fabric Lakehouse, automatically manage schema changes, and maintain historical records for auditing. Which solution should you implement?
A) Manual SQL ingestion
B) Copy Data activity in a Data Pipeline with Delta tables and schema evolution
C) Notebook ingestion without versioning
D) Raw CSV storage
Answer: B) Copy Data activity in a Data Pipeline with Delta tables and schema evolution
Explanation
Manual SQL ingestion requires custom scripts for each source and schema change. Maintaining historical records must be implemented manually, increasing complexity and operational risk. This approach does not scale efficiently for multiple sources or frequent schema changes.
Copy Data activity in a Data Pipeline with Delta tables and schema evolution provides a robust automated solution. Delta tables maintain a transaction log that tracks inserts, updates, and deletes, enabling rollback, time travel queries, and auditing. Schema evolution allows the system to handle new or modified columns without breaking downstream analytics. Pipelines orchestrate ingestion, manage retries, and provide monitoring dashboards for operational visibility. This solution scales efficiently and supports governance and compliance.
Notebook ingestion without versioning requires custom code for schema changes and historical tracking, increasing operational risk and engineering effort.
Raw CSV storage captures raw data but lacks structure, ACID compliance, and historical tracking. Downstream pipelines must manually implement schema evolution and versioning, increasing overhead and risk.
Given these factors, Copy Data activity in a Data Pipeline with Delta tables and schema evolution is the optimal solution for structured ingestion with schema evolution and historical preservation.
Question 112
You need to calculate real-time metrics from high-frequency IoT data every 5 minutes and store results for operational dashboards with historical tracking. Which approach should you implement?
A) Dataflow Gen2 batch processing
B) Eventstream ingestion with windowed aggregation
C) Notebook batch processing
D) SQL scheduled import
Answer: B) Eventstream ingestion with windowed aggregation
Explanation
Dataflow Gen2 batch processing is designed for scheduled batch workloads and cannot efficiently handle high-frequency streaming data. Using batch refresh for 5-minute intervals introduces latency, reducing operational dashboard reliability.
Eventstream ingestion with windowed aggregation is designed for streaming workloads. Data is grouped into 5-minute windows, aggregated, and stored in Delta tables. Delta tables provide ACID compliance, historical tracking, and time travel queries. Pipelines manage retries, fault tolerance, and monitoring dashboards, ensuring reliable delivery of metrics. Late-arriving events are incorporated into aggregates automatically, maintaining accurate metrics.
Notebook batch processing provides flexibility but requires coding for windowed aggregation, retries, and schema handling. High-volume streams increase operational complexity and risk.
SQL scheduled import executes queries at fixed intervals but cannot efficiently handle near-real-time 5-minute aggregation. Latency reduces dashboard responsiveness and operational utility.
Given the requirements for streaming aggregation, low latency, and historical tracking, Eventstream ingestion with windowed aggregation is the optimal solution.
Question 113
You need to orchestrate multiple dependent pipelines with automated error handling, retries, and notifications for enterprise compliance. Which solution should you implement?
A) Manual pipeline execution
B) Pipeline triggers with dependencies and retry policies
C) Notebook-only orchestration
D) Ad hoc Dataflows Gen2 execution
Answer: B) Pipeline triggers with dependencies and retry policies
Explanation
Manual pipeline execution relies on human intervention and does not enforce dependencies. Failures in upstream pipelines can propagate downstream, reducing operational reliability. Notifications are also manual, increasing response time.
Pipeline triggers with dependencies and retry policies allow pipelines to execute sequentially or in parallel based on dependency rules. Retry policies handle transient failures automatically, and notifications alert stakeholders of failures. Monitoring dashboards provide operational visibility and enable proactive issue resolution. This approach ensures reliable orchestration, reduces operational risk, and supports governance and compliance.
Notebook-only orchestration triggers code execution but does not inherently manage dependencies, retries, or notifications. Scaling multiple notebooks manually increases complexity and risk.
Ad hoc Dataflows Gen2 execution supports isolated transformations but cannot orchestrate multiple dependent pipelines, enforce retries, or provide notifications. It is insufficient for enterprise-grade operations.
Considering these factors, pipeline triggers with dependencies and retry policies are the most robust and reliable solution.
Question 114
You need to merge incremental updates from multiple sources into a Delta table while maintaining historical versions and supporting rollback. Which approach should you implement?
A) Overwrite Delta table
B) Delta table merge operations in a Data Pipeline
C) Notebook append only
D) SQL scheduled append
Answer: B) Delta table merge operations in a Data Pipeline
Explanation
Overwriting a Delta table replaces existing data, destroying historical versions and preventing rollback, which is unsuitable for auditing or compliance.
Delta table merge operations in a Data Pipeline allow transactional inserts, updates, and deletes while preserving historical versions in the Delta transaction log. Time travel queries enable rollback and historical analysis. Pipelines handle orchestration, retries, and monitoring, ensuring operational reliability. Schema evolution allows source changes without breaking downstream pipelines. This solution provides an enterprise-grade approach for incremental ingestion while maintaining historical tracking.
Notebook append only adds new records without handling updates or deletes. Maintaining historical accuracy or rollback requires custom code, increasing operational complexity and risk.
SQL scheduled append inserts data in batches but cannot efficiently manage updates or deletions. Historical versioning is not preserved, and schema changes must be handled manually, reducing reliability.
Considering incremental updates, historical preservation, rollback, and governance, Delta table merge operations in a Data Pipeline are the optimal solution.
Question 115
You need to monitor multiple Fabric pipelines, detect failures, trigger retries, and maintain lineage for auditing and compliance purposes. Which solution should you implement?
A) Dataflow Gen2 monitoring
B) Fabric Data Pipeline monitoring with integrated lineage
C) Manual SQL logging
D) KQL queries for retrospective analysis
Answer: B) Fabric Data Pipeline monitoring with integrated lineage
Explanation
Dataflow Gen2 monitoring provides basic refresh status and error messages but lacks end-to-end lineage, real-time alerts, and dashboards for monitoring multiple pipelines. It is insufficient for enterprise-scale monitoring and compliance purposes.
Fabric Data Pipeline monitoring with integrated lineage provides comprehensive monitoring and governance capabilities. Dashboards display execution metrics, dependencies, and transformations. Real-time alerts notify stakeholders of failures, enabling rapid remediation. Integrated lineage ensures traceability for auditing, governance, and compliance. Automated retry mechanisms reduce downtime and maintain operational reliability. Both batch and streaming pipelines are supported, providing proactive monitoring and operational insights at scale.
Manual SQL logging captures execution details but does not provide real-time alerts, retries, or lineage tracking. Scaling for multiple pipelines using SQL logging increases operational overhead and risk.
KQL queries allow retrospective analysis but cannot provide proactive monitoring, real-time alerts, or lineage tracking. Delays in issue detection reduce operational reliability and increase operational risk.
Considering these factors, Fabric Data Pipeline monitoring with integrated lineage is the most effective solution for monitoring multiple pipelines, detecting failures, triggering retries, and ensuring governance and compliance.
Question 116
You need to ingest multiple relational databases into a Fabric Lakehouse, handle schema drift automatically, and maintain historical versions for auditing purposes. Which solution should you implement?
A) Manual SQL ingestion
B) Copy Data activity in a Data Pipeline with Delta tables and schema evolution
C) Notebook ingestion without versioning
D) Raw CSV storage
Answer: B) Copy Data activity in a Data Pipeline with Delta tables and schema evolution
Explanation
Manual SQL ingestion requires custom scripts for each source and schema change. Historical versioning must be implemented manually, which increases operational complexity and risk, especially at enterprise scale. This approach is not efficient for multiple sources or frequent schema changes.
Copy Data activity in a Data Pipeline with Delta tables and schema evolution provides a robust, automated solution. Delta tables maintain a transaction log that tracks inserts, updates, and deletes, enabling rollback, time travel queries, and auditing. Schema evolution automatically accommodates new or modified columns without breaking downstream analytics. Pipelines orchestrate ingestion, handle retries for transient failures, and provide monitoring dashboards for operational visibility. This solution scales efficiently and supports governance and compliance.
Notebook ingestion without versioning requires custom handling of schema changes and historical tracking, which increases engineering effort and operational risk.
Raw CSV storage captures raw data but lacks structure, ACID compliance, and historical tracking. Downstream pipelines must manually implement schema evolution and versioning, increasing operational overhead.
Considering these factors, Copy Data activity in a Data Pipeline with Delta tables and schema evolution is the optimal solution for relational ingestion with schema evolution and historical preservation.
Question 117
You need to calculate aggregated metrics from high-frequency telemetry data every 15 minutes and store results for dashboards with historical tracking. Which approach should you implement?
A) Dataflow Gen2 batch processing
B) Eventstream ingestion with windowed aggregation
C) Notebook batch processing
D) SQL scheduled import
Answer: B) Eventstream ingestion with windowed aggregation
Explanation
Dataflow Gen2 batch processing operates on scheduled batch workloads and cannot efficiently process high-frequency streaming data. Using batch refresh for 15-minute intervals introduces latency, reducing the reliability of operational dashboards.
Eventstream ingestion with windowed aggregation is designed for streaming scenarios. Data is grouped into 15-minute windows, aggregated, and written to Delta tables. Delta tables provide ACID compliance, historical tracking, and time travel queries. Pipelines manage retries, fault tolerance, and monitoring dashboards, ensuring reliable delivery of metrics. Late-arriving events are incorporated into aggregates automatically, maintaining accurate results.
Notebook batch processing provides flexibility but requires coding for windowed aggregation, retries, and schema handling. Processing high-frequency streams in notebooks increases operational complexity and risk.
SQL scheduled import executes queries at fixed intervals but cannot efficiently handle near-real-time aggregation every 15 minutes. Latency reduces dashboard responsiveness and operational utility.
Given the requirements for streaming aggregation, low latency, and historical tracking, Eventstream ingestion with windowed aggregation is the optimal solution.
Question 118
You need to orchestrate multiple dependent pipelines with automated error handling, retries, and notifications. Which solution is most appropriate?
A) Manual pipeline execution
B) Pipeline triggers with dependencies and retry policies
C) Notebook-only orchestration
D) Ad hoc Dataflows Gen2 execution
Answer: B) Pipeline triggers with dependencies and retry policies
Explanation
Manual pipeline execution relies on human intervention and does not enforce dependencies. Failures in upstream pipelines can propagate to downstream workflows, reducing operational reliability. Notifications are manual, increasing response time.
Pipeline triggers with dependencies and retry policies allow pipelines to execute sequentially or in parallel based on dependency rules. Retry policies automatically handle transient failures, and automated notifications alert stakeholders to failures. Monitoring dashboards provide operational visibility, enabling proactive issue resolution. This solution ensures reliable orchestration, reduces operational risk, and supports governance and compliance for complex workflows.
Notebook-only orchestration triggers code execution but does not inherently manage dependencies, retries, or notifications. Scaling multiple notebooks manually increases complexity and operational risk.
Ad hoc Dataflows Gen2 execution supports isolated transformations but cannot orchestrate multiple dependent pipelines, enforce retries, or provide notifications. It is insufficient for enterprise-grade orchestration.
Considering these factors, pipeline triggers with dependencies and retry policies are the most robust and reliable solution.
Question 119
You need to merge incremental updates from multiple sources into a Delta table while maintaining historical versions and supporting rollback. Which approach should you implement?
A) Overwrite Delta table
B) Delta table merge operations in a Data Pipeline
C) Notebook append only
D) SQL scheduled append
Answer: B) Delta table merge operations in a Data Pipeline
Explanation
Overwriting a Delta table replaces existing data, destroying historical versions and preventing rollback, which is unsuitable for auditing or compliance.
Delta table merge operations in a Data Pipeline allow transactional inserts, updates, and deletes while preserving historical versions in the Delta transaction log. Time travel queries enable rollback and historical analysis. Pipelines manage orchestration, retries, and monitoring, ensuring operational reliability. Schema evolution accommodates source changes without breaking downstream pipelines. This provides a robust enterprise-grade approach for incremental ingestion while maintaining historical tracking.
Notebook append only adds new records without handling updates or deletes. Maintaining historical accuracy or rollback requires custom code, increasing operational complexity and risk.
SQL scheduled append inserts records in batches but cannot efficiently handle updates or deletions. Historical versioning is not preserved, and schema changes must be handled manually, reducing reliability.
Considering incremental updates, historical preservation, rollback, and governance, Delta table merge operations in a Data Pipeline are the optimal solution.
Question 120
You need to monitor multiple Fabric pipelines, detect failures, trigger retries, and maintain lineage for auditing and compliance purposes. Which solution should you implement?
A) Dataflow Gen2 monitoring
B) Fabric Data Pipeline monitoring with integrated lineage
C) Manual SQL logging
D) KQL queries for retrospective analysis
Answer: B) Fabric Data Pipeline monitoring with integrated lineage
Explanation
Dataflow Gen2 monitoring provides basic refresh status and error messages but lacks end-to-end lineage, real-time alerts, and dashboards for monitoring multiple pipelines. It is insufficient for enterprise-scale monitoring and compliance purposes.
Fabric Data Pipeline monitoring with integrated lineage provides comprehensive monitoring and governance capabilities. Dashboards display execution metrics, dependencies, and transformations. Real-time alerts notify stakeholders of failures, enabling rapid remediation. Integrated lineage ensures traceability for auditing, governance, and compliance. Automated retry mechanisms reduce downtime and maintain operational reliability. Both batch and streaming pipelines are supported, providing proactive monitoring and operational insights at scale.
Manual SQL logging captures execution details but does not provide real-time alerts, retries, or lineage tracking. Scaling multiple pipelines using SQL logging increases operational overhead and risk.
KQL queries allow retrospective analysis but cannot provide proactive monitoring, real-time alerts, or lineage tracking. Delays in issue detection reduce operational reliability and increase operational risk.
Considering these factors, Fabric Data Pipeline monitoring with integrated lineage is the most effective solution for monitoring multiple pipelines, detecting failures, triggering retries, and ensuring governance and compliance.