Monitor and optimize an analytics solution

A Fabric administrator notices that interactive report queries in a workspace have started returning delayed responses in the afternoon, and some background semantic model refreshes are being rejected. The workspace is assigned to an F64 capacity. The admin opens the Microsoft Fabric Capacity Metrics app to investigate. Which observation would most directly confirm that the capacity is throttling operations due to accumulated overutilization?

Users of a Power BI report backed by a Fabric warehouse complain that visuals take an unusually long time to render during business hours. You open the Microsoft Fabric Capacity Metrics app and see that during peak periods the capacity shows sustained overloading, with the timepoint detail indicating a high volume of carryforward from earlier background operations. Interactive operations are experiencing an added delay before execution. Which throttling stage is the capacity currently in, and what does it mean for these interactive queries?

A Dataflow Gen2 that loads customer data into a lakehouse has started failing intermittently. The data engineer needs to determine the specific query and error message that caused the most recent failed refresh so it can be fixed. Which action provides the fastest, most detailed root-cause information for the failed run?

An Eventhouse in your Real-Time Intelligence solution stores 18 months of telemetry data for compliance, but analysts only run interactive dashboards against the most recent 30 days. Queries against recent data have become noticeably slower over time, and capacity monitoring shows high memory pressure on the Eventhouse. You need to improve interactive query performance for recent data while retaining all 18 months for occasional audits, keeping costs reasonable. What should you configure?

You manage an Eventhouse (KQL database) that ingests IoT telemetry through a data connection. Overnight, downstream dashboards show no new rows for one table, while other tables continue to update normally. You need to quickly determine whether ingestion for that specific table is failing and see the underlying error messages. Which action should you take first?

An Eventhouse in Microsoft Fabric receives a continuous stream of small event batches from an Eventstream. Analysts complain that newly ingested data does not appear in query results for several minutes. On investigation, you find the KQL database table uses the default ingestion batching settings. You need to reduce the end-to-end latency between event arrival and query availability without significantly increasing ingestion cost per record. Which action should you take?

An Eventhouse ingests millions of IoT telemetry rows per hour into a KQL table. A dashboard runs a query that computes the count of events per device over the last 24 hours, and this same aggregation query is executed hundreds of times per minute by many concurrent users. Users report the dashboard is slow and consuming excessive query resources. You need to reduce query latency and resource consumption for this repeated aggregation with minimal ongoing maintenance. What should you do?

An Eventstream ingests telemetry from an Azure Event Hubs source and routes it to a KQL database destination. Over the past hour, the source is receiving events normally, but no new rows appear in the destination table. The upstream Event Hubs metrics show healthy throughput. Where should you look FIRST to identify the root cause of the delivery failure?

A data engineer creates a OneLake shortcut in a Fabric lakehouse that points to an external Amazon S3 bucket. Reports built on this data intermittently show outdated values even though the source files in S3 were updated hours ago. The engineer confirms the source data is correct and the shortcut path is valid. What is the most likely cause of the stale results?

A data engineer creates a OneLake shortcut in the Sales lakehouse that points to a Files folder in a Finance lakehouse located in a different workspace. When a notebook run by a colleague on the Sales team attempts to read data through the shortcut, it fails with a 403 (Unauthorized) error, even though the shortcut was created successfully. The colleague has the Viewer role on the Sales workspace. What is the most likely cause of the error?

A Fabric lakehouse contains a large Delta fact table, sales_transactions, that analysts frequently query with filters on the customer_id column. Query latency has increased steadily over several weeks as many small Parquet files accumulated from continuous appends. You need to reduce query scan time for the customer_id filter while consolidating the small files. Which maintenance action should you run on the table?

A data engineer notices that queries against a Delta table in a Fabric lakehouse have become progressively slower over several weeks. The table is loaded every 15 minutes by a streaming pipeline that appends small batches. Investigating the table's storage folder, the engineer finds tens of thousands of tiny Parquet files. Which action will most directly resolve the query performance degradation?

A data engineer manages a large Delta table in a Fabric lakehouse that stores three years of sales transactions. Analysts run Power BI Direct Lake reports that almost always filter by a single calendar month. Queries have become slow, and file inspection shows thousands of small Parquet files across the table with no partitioning. The engineer wants to improve read performance for the month-filtered queries with minimal ongoing maintenance. What should the engineer do?

A data engineering team runs a nightly pipeline that ingests data, then triggers a downstream notebook and a semantic model refresh. This morning users report stale dashboard data. You need to quickly determine, across all three item types in one place, which specific run failed and view its status, duration, and start time without opening each item individually. Which Fabric capability should you use?

You manage a Fabric workspace containing dozens of pipelines, notebooks, and semantic models. Users report that a specific overnight data flow occasionally fails, but you are not sure which item. You want a single place to review the recent run status of all schedulable items across the workspace, filter by item type and status, and drill into the failure details for a given run. Which Fabric feature should you use?

A scheduled Fabric notebook that loads data into a lakehouse Delta table has started failing. When you open the notebook run in Monitoring hub, you see the job failed but the summary only shows a generic 'Notebook execution failed' message. You need to pinpoint the specific line of code and exception that caused the failure with the least effort. What should you do?

A data engineer runs a Spark notebook in Fabric that joins a large fact DataFrame with several transformations. The job completes but takes 45 minutes, far longer than expected. In the Spark UI for the run, one stage shows a very high 'Shuffle Spill (Disk)' value and several tasks running much longer than others. What is the MOST likely root cause to investigate first?

A data engineer creates a OneLake shortcut in a Fabric lakehouse pointing to an Amazon S3 bucket. Yesterday the shortcut returned data correctly, but today all queries against the shortcut folder fail with an authorization/access error, while other native lakehouse tables work fine. No changes were made to the lakehouse itself. What is the MOST likely cause the engineer should investigate first?

A data engineering team runs a nightly Fabric pipeline that loads a lakehouse. Management wants to be proactively notified whenever the number of rows loaded into the sales table drops below an expected daily minimum, which may indicate an upstream data problem even when the pipeline itself succeeds. The team needs a low-code approach that continuously watches the metric and sends an email when the condition is met. Which approach should they implement?

A data engineer manages a nightly ingestion pipeline in Microsoft Fabric that loads sales data into a lakehouse. The business requires an automated notification to be sent to a Microsoft Teams channel whenever the pipeline finishes successfully, without writing custom code or maintaining external logic. Which approach best meets this requirement?