Microsoft DP-700 Exam Dumps & Practice Test Questions
Question 1:
You are working in a Microsoft Fabric workspace with a semantic model named Model1. You need to automate the execution of data refreshes for Model1 and track the progress of the refresh process in real-time to ensure smooth operation.
Which built-in feature of Fabric provides operational visibility and real-time monitoring of dataset activities like refreshes, queries, and failures?
A. Dynamic management views in Microsoft SQL Server Management Studio (SSMS)
B. Monitoring Hub
C. Dynamic management views in Azure Data Studio
D. A semantic link in a notebook
Answer: B
Explanation:
To effectively manage and oversee the operation of data refreshes and query execution in Microsoft Fabric—particularly in a semantic model such as Model1—it is critical to use a built-in feature that offers real-time operational visibility. The correct and most efficient solution provided within Microsoft Fabric is the Monitoring Hub.
The Monitoring Hub is a native, integrated feature within Microsoft Fabric that provides real-time, centralized insights into operational activities across various Fabric experiences, including semantic models. It allows users to view refresh history, track current refreshes, observe query activity, and quickly identify failures or performance bottlenecks. This tool is especially valuable for data engineers and administrators who need continuous oversight of system processes to ensure high availability, reliability, and performance of data pipelines and models.
Let’s explore why the other options are not correct:
A. Dynamic management views in Microsoft SQL Server Management Studio (SSMS): While dynamic management views (DMVs) in SSMS are powerful tools for analyzing server performance and internal state, they are applicable to SQL Server or Azure SQL Database environments. They are not integrated into Microsoft Fabric or its semantic models and thus cannot offer real-time monitoring of activities in a Fabric workspace.
C. Dynamic management views in Azure Data Studio: Like SSMS, Azure Data Studio allows querying DMVs in supported databases. However, it does not provide real-time Fabric-level monitoring or visualization for dataset refreshes and semantic model activity. Therefore, it falls short of the required operational visibility for Fabric-based tasks.
D. A semantic link in a notebook: Semantic links in notebooks are designed to connect data models to notebooks for advanced analytics and calculations. While this feature enhances data exploration and analysis, it does not offer any monitoring capabilities or real-time visibility into refresh operations or failure diagnostics.
The Monitoring Hub stands out as the only option that:
Is fully integrated within the Microsoft Fabric environment.
Supports real-time tracking of dataset and semantic model refreshes.
Allows failure detection and diagnosis.
Offers visual and actionable insights for operational management.
This hub acts as a centralized dashboard where users can assess performance, check on recent jobs, and troubleshoot failures. Importantly, because it’s native to Fabric, there is no need for external tools or configurations, making it an ideal solution for maintaining operational continuity.
In summary, for real-time visibility and monitoring of data refreshes in a Fabric workspace semantic model like Model1, the Monitoring Hub is the only built-in tool designed specifically for this purpose, offering the control and insight needed to manage operations effectively.
Question 2:
You are working with a Microsoft Fabric event stream that ingests data into a database table named Bike_Location, which stores real-time information about bike-sharing stations. The table includes columns such as BikepointID, Street, Neighbourhood, No_Bikes, No_Empty_Docks, and Timestamp.
You need to implement a solution that:
Filters data to show only entries where the Neighbourhood is "Sands End".
Returns only rows where the number of bikes (No_Bikes) is greater than or equal to 15.
Sorts the output by No_Bikes in ascending order.
Projects only relevant columns for downstream analysis.
Proposed Solution:
You use the following query:
bike_location
| filter Neighbourhood == "Sands End" and No_Bikes >= 15
| sort by No_Bikes
| project BikepointID, Street, Neighbourhood, No_Bikes, No_Empty_Docks, Timestamp
| project BikepointID, Street, Neighbourhood, No_Bikes, No_Empty_Docks, Timestamp
Does this solution meet the requirements?
A. Yes
B. No
Answer: A
Explanation:
The proposed solution effectively meets all the listed requirements using appropriate Kusto Query Language (KQL) syntax, which is commonly used in Microsoft Fabric's event streaming and data exploration scenarios. Let’s break down the requirements and assess whether the query aligns with them.
Requirement 1: Filter for "Sands End" in the Neighbourhood column
The query includes the line:
| filter Neighbourhood == "Sands End" and No_Bikes >= 15
This clause filters the data so that only rows with Neighbourhood equal to "Sands End" are included. This satisfies the first requirement exactly.
Requirement 2: Filter for No_Bikes >= 15
The same filter clause also includes:
No_Bikes >= 15
This condition ensures only rows where the number of bikes is 15 or more are included in the output. This satisfies the second requirement.
Requirement 3: Sort the output by No_Bikes in ascending order
The query includes the line:
| sort by No_Bikes
By default, the sort by operator sorts data in ascending order unless otherwise specified (e.g., using desc for descending). Therefore, this clause fulfills the third requirement of sorting by number of bikes in ascending order.
Requirement 4: Project only relevant columns
The query includes two consecutive project statements:
| project BikepointID, Street, Neighbourhood, No_Bikes, No_Empty_Docks, Timestamp
| project BikepointID, Street, Neighbourhood, No_Bikes, No_Empty_Docks, Timestamp
While the second project is redundant—it projects the same fields as the first—it does not alter the correctness of the query. All relevant columns are included in the projection:
BikepointID
Street
Neighbourhood
No_Bikes
No_Empty_Docks
Timestamp
Since no additional columns are introduced and no required columns are omitted, the projection aligns fully with the requirement of projecting only relevant fields.
Redundancy Clarification
Although the double project statement is unnecessary and may raise concerns about inefficiency or clarity, it does not affect the output of the query. Redundant projections are allowed syntactically and result in the same output as a single projection. Therefore, from a functional and correctness standpoint, the query still meets all stated requirements.
All four core requirements have been satisfied:
Filtering by neighborhood and bike count
Sorting by number of bikes
Projecting necessary fields for downstream use
Even with the redundant projection, the query is logically and syntactically sound, and therefore the answer is Yes.
Thus, the proposed solution fully meets the requirements.
Question 3:
You are working with a Microsoft Fabric event stream ingesting data into a database. The data is stored in a table named Bike_Location, containing columns like BikepointID, Street, Neighbourhood, No_Bikes, No_Empty_Docks, and Timestamp.
You need to apply transformation and filtering logic to achieve the following:
Filter data to show only records where the Neighbourhood is "Sands End".
Include only records with No_Bikes greater than or equal to 15.
Sort the data by No_Bikes in ascending order.
Display only the following columns: BikepointID, Street, Neighbourhood, No_Bikes, No_Empty_Docks, and Timestamp.
The following query is proposed:
bike_location
| filter Neighbourhood == "Sands End" and No_Bikes >= 15
| order by No_Bikes
| project BikepointID, Street, Neighbourhood, No_Bikes, No_Empty_Docks, Timestamp
Does this query correctly implement the required transformation and filtering logic?**
A. Yes
B. No
Answer: B
Explanation:
At first glance, the proposed query seems to satisfy most of the specified requirements regarding filtering, sorting, and projection. However, the issue lies in the use of the order by operator within the query, which is not valid syntax in Kusto Query Language (KQL)—the language used in Microsoft Fabric event streams and other Azure Data Explorer-based experiences.
Let’s analyze the query step-by-step against each requirement.
Requirement 1: Filter data for Neighbourhood == "Sands End" and No_Bikes >= 15
The query includes:
| filter Neighbourhood == "Sands End" and No_Bikes >= 15
This statement correctly filters the dataset to include only those records where the Neighbourhood is "Sands End" and the number of bikes is 15 or more. This part of the logic is accurate and aligns with the requirement.
Requirement 2: Sort by No_Bikes in ascending order
Here is where the issue arises:
| order by No_Bikes
In SQL, the ORDER BY clause is valid and expected. However, in Kusto Query Language (KQL)—which is used in Microsoft Fabric for event streams—the correct operator for sorting is:
| sort by No_Bikes
The order by keyword is not recognized in KQL and will result in a syntax error. This invalidates the query even though the intention (sorting by No_Bikes) is correct. Therefore, while the logic of sorting is right, the syntax is wrong, making the query non-functional in this context.
Requirement 3: Display specific columns
The query includes:
| project BikepointID, Street, Neighbourhood, No_Bikes, No_Empty_Docks, Timestamp
This clause correctly projects only the specified columns. It meets the requirement of showing relevant fields for downstream analysis and omitting any unnecessary data.
Summary of Evaluation:
Because of the incorrect use of the order by clause, the query will not run successfully in a Microsoft Fabric event stream, making it an invalid implementation of the desired logic. While the intention is technically sound, practical execution matters in programming and data transformation. The correct syntax must be used for the query to function as intended.
Corrected Query:
To fix the issue, the query should use sort by instead of order by:
bike_location
| filter Neighbourhood == "Sands End" and No_Bikes >= 15
| sort by No_Bikes
| project BikepointID, Street, Neighbourhood, No_Bikes, No_Empty_Docks, Timestamp
This corrected version would meet all the transformation and filtering criteria without any syntax errors.
The proposed query fails due to an invalid sorting operator. As a result, the correct answer is B.
Question 4:
You are tasked with transforming and filtering data from a Microsoft Fabric Eventstream, where data is stored in a Bike_Location table with columns like BikepointID, Street, Neighbourhood, No_Bikes, No_Empty_Docks, and Timestamp.
You need to extract data that:
Belongs to the "Sands End" Neighbourhood.
Has No_Bikes greater than or equal to 15.
Is sorted by No_Bikes in ascending order.
The following SQL query is proposed:
SELECT BikepointID, Street, Neighbourhood, No_Bikes, No_Empty_Docks, Timestamp
FROM bike_location
WHERE Neighbourhood = 'Sands End'
AND No_Bikes >= 15
ORDER BY No_Bikes
Does this solution meet the requirements?
A. Yes
B. No
Answer: A
Explanation:
To evaluate the correctness of the proposed SQL query, we need to examine whether it fulfills each of the transformation and filtering requirements based on both logic and syntax, in the context of Microsoft Fabric.
It is important to understand that Microsoft Fabric Eventstream supports querying through different engines depending on how the data is consumed. While data transformations in Eventstream pipelines typically rely on Kusto Query Language (KQL) for real-time stream processing, when data is landed into a Lake House table or a Warehouse, T-SQL (Transact-SQL) becomes valid for querying that persisted data.
In this scenario, the query is written in T-SQL, and it’s assumed the data in the bike_location table has been ingested and stored in a structure where SQL syntax is valid—such as a Fabric Lakehouse SQL endpoint or Warehouse. Based on that understanding, we can validate the query step-by-step:
1. Filtering for the "Sands End" neighbourhood
WHERE Neighbourhood = 'Sands End'
This clause correctly filters records to only those where the Neighbourhood is "Sands End". This satisfies the first requirement.
2. Filtering for No_Bikes >= 15
AND No_Bikes >= 15
This additional condition correctly narrows the dataset to rows where No_Bikes is 15 or more, satisfying the second requirement.
3. Sorting by No_Bikes in ascending order
ORDER BY No_Bikes
In T-SQL, the ORDER BY clause by default arranges results in ascending order unless explicitly stated otherwise with DESC. Therefore, this syntax meets the third requirement of sorting by No_Bikes in ascending order.
4. Selecting only specific columns
SELECT BikepointID, Street, Neighbourhood, No_Bikes, No_Empty_Docks, Timestamp
This clause specifies exactly the columns that are relevant for downstream analysis. It does not include any unnecessary fields, and it includes all the ones mentioned in the question:
BikepointID
Street
Neighbourhood
No_Bikes
No_Empty_Docks
Timestamp
This satisfies the projection requirement.
Important Consideration
The only potential ambiguity is whether the data is being queried from a real-time stream using KQL or from a stored table using T-SQL. If this were a pure streaming context, this SQL syntax would not be valid. However, the question states the data is stored in a table, which strongly implies T-SQL is appropriate.
Since the query:
Correctly filters based on Neighbourhood and No_Bikes
Sorts the results properly
Projects the specified columns using valid SQL syntax
It meets all the stated transformation and filtering requirements under the assumption that it is executed in a SQL-capable environment within Microsoft Fabric (such as a Lakehouse or Warehouse).
The proposed SQL query satisfies all the logic and syntax requirements. Therefore, the correct answer is A.
Question 5:
Litware, Inc., a global publisher, wants to access external book review data from Amazon S3 buckets and integrate it into their Microsoft Fabric lakehouse. The team wants to avoid duplicating data and ensure data governance compliance. Which solution will allow you to access the data in the lakehouse without copying it?
A. Create a Dataflow Gen2 dataflow
B. Create a shortcut
C. Enable external data sharing
D. Create a data pipeline
Answer: B
Explanation:
In Microsoft Fabric, when you want to access external data sources like Amazon S3 without duplicating the data—especially in a lakehouse scenario—the most appropriate and efficient solution is to create a shortcut.
A shortcut in Microsoft Fabric allows users to reference external data stored in other locations (e.g., Amazon S3, Azure Data Lake Storage Gen2, or OneLake) directly from a lakehouse or other Fabric-enabled environment without moving or duplicating the underlying data. The shortcut appears in the Fabric environment as if it is part of the lakehouse, but the actual data remains in the external source.
This approach is optimal for:
Avoiding data duplication and associated storage costs.
Maintaining data governance and compliance by keeping data in its source location.
Ensuring that access is controlled and auditable, using the security settings and policies applied to the external data source.
Supporting performance efficiency, as shortcuts can leverage metadata management and structured access without full ingestion.
Let’s examine why the other options are not correct in this scenario:
A. Dataflow Gen2 dataflow:
Dataflows Gen2 are used for transforming and ingesting data into Fabric. While they can connect to external sources and transform data before loading it into a Fabric item like a lakehouse or warehouse, they result in data duplication because the transformed data is stored anew. This contradicts the requirement to avoid copying data.C. Enable external data sharing:
This typically refers to sharing data across organizational boundaries using Azure Data Share or external table sharing mechanisms. However, this is not a built-in Fabric lakehouse solution for accessing S3 data and does not address the integration aspect directly within the Fabric lakehouse. It also doesn't inherently avoid duplication or guarantee integration-level governance.D. Create a data pipeline:
A data pipeline in Fabric is used to orchestrate and move data from one place to another. Pipelines are excellent for ETL (Extract, Transform, Load) scenarios but involve copying data from source (e.g., Amazon S3) into Fabric storage. This directly conflicts with the requirement to avoid duplicating the data.
Why Shortcuts Are the Correct Solution
They support virtualization—linking to external data without physical import.
They integrate seamlessly into the OneLake architecture, Microsoft Fabric’s unified data lake.
They enforce governance and security policies by respecting the access rules set at the source.
They allow Fabric users to analyze and query external data using familiar tools like notebooks, SQL endpoints, and Power BI, without needing to move or reshape the underlying files.
Given that Litware, Inc. wants to access Amazon S3 data without copying it and maintain strong governance over the data, the correct and most efficient solution is to create a shortcut in Microsoft Fabric. This method ensures data can be accessed and analyzed from within the lakehouse while eliminating duplication and adhering to governance best practices.
The correct answer is B.
Question 6:
Litware, Inc., has a large volume of sales data, which is ingested every six hours but sometimes experiences delays or slowdowns during high sales periods. The dataflow process is also inefficient because it processes both historical and new data, which causes delays.
To improve performance, the engineering team needs to reduce the amount of data being processed. What should they do to achieve this?
A. Split the dataflow into two separate dataflows
B. Configure a scheduled refresh for the dataflow
C. Configure incremental refresh for the dataflow, setting the storage duration to 1 month
D. Configure incremental refresh for the dataflow, setting the refresh duration to 1 year
E. Configure incremental refresh for the dataflow, setting the refresh duration to 1 month
Answer: E
Explanation:
The key issue Litware is facing involves performance degradation in their data processing pipeline due to inefficient handling of large volumes of historical and new data together. Specifically, the sales data ingestion happens every six hours, and during periods of high sales activity, processing the entire dataset (including historical data) causes slowdowns. The engineering goal is to reduce the volume of data processed per refresh, thereby improving overall efficiency.
This is a classic scenario for implementing incremental refresh, which is designed to address exactly this problem.
Understanding Incremental Refresh
Incremental refresh is a feature available in Power BI and Dataflows Gen2 within Microsoft Fabric that allows the system to:
Refresh only new or changed data instead of reprocessing the entire dataset.
Store historical data over a defined duration (e.g., 1 month, 1 year) without reprocessing it repeatedly.
Dramatically reduce refresh time, increase efficiency, and lower resource consumption.
To implement this properly, two parameters must be configured:
Storage period – How far back to retain historical data in storage.
Refresh period – The window of time to refresh data incrementally.
Evaluation of Options
A. Split the dataflow into two separate dataflows:
While breaking down a dataflow can occasionally help with modularization or team collaboration, it does not inherently solve the issue of processing redundant historical data. It also adds complexity without directly addressing the inefficiency caused by processing old data repeatedly.B. Configure a scheduled refresh for the dataflow:
A scheduled refresh determines when the data is refreshed but does not limit how much data is processed. Therefore, this approach still results in processing all historical data, which is the core performance bottleneck.C. Configure incremental refresh, storage duration = 1 month:
This option only sets the retention policy for data, meaning how long historical data is stored. It does not address how frequently or how much new data is refreshed, and alone it won't reduce refresh volume.D. Configure incremental refresh, refresh duration = 1 year:
This sets a very large refresh window, meaning each refresh will include all data from the last year. This would still include large volumes of data, which defeats the goal of reducing data volume processed during each refresh cycle.E. Configure incremental refresh, refresh duration = 1 month:
This is the most optimal solution. Setting the refresh duration to 1 month means that only the data from the past 30 days will be processed during each refresh. This aligns perfectly with Litware’s requirement to focus processing on recent data only, minimizing overhead and improving performance. Historical data outside of the refresh window is preserved but not reprocessed, which ensures system stability during high-volume periods.
Why Option E is Correct
It directly reduces the volume of data being refreshed.
It avoids reprocessing stable historical records.
It aligns with real-time or near-real-time data ingestion patterns (e.g., every six hours).
It is a best practice in large-scale data management within Microsoft Fabric and Power BI environments.
To optimize the performance of the dataflow by reducing the amount of data processed per refresh and avoid delays, the most effective solution is to configure incremental refresh with a refresh duration of 1 month.
The correct answer is E.
Question 7:
You are working with a Microsoft Fabric eventstream ingesting real-time data. The data is stored in a table named Bike_Location, with columns including BikepointID, Street, Neighbourhood, No_Bikes, No_Empty_Docks, and Timestamp.
To meet a business requirement, you need to filter the data to include only entries where:
The Neighbourhood is 'Sands End'.
No_Bikes is greater than or equal to 20.
The result must be sorted in descending order by the number of bikes.
What is the correct query to implement this logic?**
A.
bike_location
| filter Neighbourhood == "Sands End" and No_Bikes >= 20
| order by No_Bikes desc
| project BikepointID, Street, Neighbourhood, No_Bikes, No_Empty_Docks, Timestamp
B.
bike_location
| filter Neighbourhood == "Sands End" and No_Bikes >= 20
| order by No_Bikes asc
| project BikepointID, Street, Neighbourhood, No_Bikes, No_Empty_Docks, Timestamp
C.
bike_location
| filter Neighbourhood == "Sands End"
| order by No_Bikes desc
| project BikepointID, Street, Neighbourhood, No_Bikes, No_Empty_Docks, Timestamp
D.
bike_location
| project BikepointID, Street, Neighbourhood, No_Bikes, No_Empty_Docks, Timestamp
| filter No_Bikes >= 20
| order by No_Bikes asc
Answer: A
Explanation:
This scenario is a classic application of Kusto Query Language (KQL) in Microsoft Fabric's Eventstream to filter, sort, and project data for analytical or operational use. Let’s walk through the required transformation step-by-step and then evaluate each option to determine which one best implements the logic.
Step-by-Step Breakdown of Requirements:
Filter for Neighbourhood = 'Sands End':
This requires a filter condition to match the Neighbourhood column to "Sands End".Filter for No_Bikes >= 20:
This is a second condition to be applied using and No_Bikes >= 20 in the same filter clause.Sort by No_Bikes in descending order:
This requires an order by No_Bikes desc clause to rank records from the highest to lowest number of bikes.Project only necessary columns:
The project clause should include the relevant fields for downstream use:
BikepointID, Street, Neighbourhood, No_Bikes, No_Empty_Docks, Timestamp.
Evaluation of Options:
Option A:
bike_location
| filter Neighbourhood == "Sands End" and No_Bikes >= 20
| order by No_Bikes desc
| project BikepointID, Street, Neighbourhood, No_Bikes, No_Empty_Docks, Timestamp
This option correctly:
Filters by both Neighbourhood and No_Bikes
Sorts in descending order (as required)
Projects all relevant fields
This query fully satisfies all the requirements and uses valid KQL syntax.
Option B:
bike_location
| filter Neighbourhood == "Sands End" and No_Bikes >= 20
| order by No_Bikes asc
| project BikepointID, Street, Neighbourhood, No_Bikes, No_Empty_Docks, Timestamp
This query is almost identical to Option A, except the sorting is in ascending order. Since the requirement is to sort descending by No_Bikes, this does not meet the criteria.
Incorrect sorting direction.
Option C:
bike_location
| filter Neighbourhood == "Sands End"
| order by No_Bikes desc
| project BikepointID, Street, Neighbourhood, No_Bikes, No_Empty_Docks, Timestamp
This query omits the second filter: No_Bikes >= 20. As a result, it may return entries with fewer than 20 bikes, which violates the requirement.
Missing the condition to filter No_Bikes >= 20.
Option D:
bike_location
| project BikepointID, Street, Neighbourhood, No_Bikes, No_Empty_Docks, Timestamp
| filter No_Bikes >= 20
| order by No_Bikes asc
This query has two major problems:
It does not filter by Neighbourhood, which is a required condition.
It sorts in ascending order, when descending is required.
Fails to filter by Neighbourhood and has incorrect sort direction.
Why Option A is Correct:
The filter conditions are accurate and combined in a single filter clause using and.
It sorts results in descending order by No_Bikes, which is what the business requirement specifies.
It uses the project statement correctly to return only the relevant fields.
Syntax is valid for KQL and matches the context of Microsoft Fabric Eventstream usage.
This combination of correct logic, syntax, and adherence to business requirements makes Option A the correct choice.
The correct answer is A.
Question 8:
You are working with a dataset in Microsoft Fabric that uses the medallion architecture. Litware, Inc. is ingesting raw data from various sources in the bronze layer and transforming it into more refined datasets in the silver and gold layers.
The company has a requirement to store and process only clean data in the silver layer, ensuring minimal data duplication. What is the most efficient way to achieve this?
A. Use dataflow transformations to clean the data as it is ingested into the silver layer
B. Create a separate pipeline to clean the data before moving it into the silver layer
C. Use an incremental refresh strategy in the silver layer to ensure data is only processed when required
D. Leverage real-time streaming to only process the latest clean data
Answer: A
Explanation:
This question is based on a modern data architecture pattern called the medallion architecture, which is commonly implemented in Microsoft Fabric. It divides data processing into structured stages—Bronze, Silver, and Gold—to enable organized and scalable data transformations.
Here’s a breakdown of the three layers in the medallion architecture:
Bronze layer stores raw, unprocessed data directly from source systems. It contains all records as they are ingested, regardless of quality or duplication.
Silver layer holds cleaned, validated, and enriched data. This layer ensures that the data is deduplicated, consistent, and ready for business-level analytics or further transformation.
Gold layer is optimized for consumption—dashboards, machine learning, and reporting. It typically contains aggregated, business-ready data.
Requirement
The company needs to:
Ensure only clean data is stored and processed in the silver layer.
Minimize data duplication, which is crucial for efficient analytics and compliance.
Follow efficient data transformation best practices in the Microsoft Fabric environment.
Evaluation of Options
A. Use dataflow transformations to clean the data as it is ingested into the silver layer
This is the most efficient and aligned approach within Fabric’s architecture.Dataflows in Microsoft Fabric (especially Dataflow Gen2) are purpose-built for ETL tasks such as cleaning, filtering, removing duplicates, standardizing formats, and enriching data.
By performing these transformations as part of ingestion into the silver layer, you ensure that only clean, deduplicated, high-quality data is passed from bronze to silver.
This also reduces storage and compute costs since unclean or redundant data does not persist into higher layers.
Additionally, dataflows can be scheduled, parameterized, and managed within pipelines, offering robust data orchestration.
B. Create a separate pipeline to clean the data before moving it into the silver layer
While technically valid, this introduces unnecessary complexity.Pipelines are primarily used to orchestrate movement and transformation tasks (e.g., triggering dataflows, notebooks), not to directly perform data cleaning.
Cleaning within a separate pipeline step means you add latency and risk processing data more than once or storing intermediate unclean results.
C. Use an incremental refresh strategy in the silver layer to ensure data is only processed when required
Incremental refresh is a performance optimization technique, not a data cleaning strategy.While helpful for reducing compute on repeated refreshes, it does not ensure data quality or prevent duplication.
It assumes the data is already in a clean, reliable state for incremental processing.
D. Leverage real-time streaming to only process the latest clean data
Real-time streaming is suitable for low-latency, real-time scenarios, but it doesn’t inherently clean or deduplicate the data.Streaming data can be dirty or duplicate, especially if sourced from varied real-time systems.
Additional logic would be required to filter, enrich, or validate the data, which is better done in dataflows or notebooks.
Why Option A is Correct
Using dataflow transformations during ingestion into the silver layer aligns perfectly with the medallion architecture principles:
It promotes separation of concerns, with raw data residing in bronze and clean data in silver.
It improves efficiency by avoiding multiple passes over the data.
It ensures quality and consistency at the silver level.
It reduces data duplication, fulfilling the governance requirement.
The most efficient and scalable way to ensure that only clean, deduplicated data enters the silver layer is to use dataflow transformations during ingestion. This method balances performance, governance, and architectural alignment within Microsoft Fabric.
The correct answer is A.
Question 9:
You are tasked with creating a real-time data pipeline for Litware, Inc., which uses a Kusto database to process sales data from multiple retail and online sources. You need to set up a real-time ingestion process while minimizing costs.
Which of the following options should you choose to efficiently ingest and process real-time data without storing the entire dataset at once?
A. Create a delta lake for continuous ingestion of live data
B. Use a live stream data connector with incremental processing
C. Set up a batch processing pipeline with periodic refresh intervals
D. Use a dataflow with manual refresh triggers for real-time updates
Answer: B
Explanation:
In this scenario, Litware, Inc. needs to ingest and process real-time data from multiple sources, including retail and online sources. The goal is to achieve real-time ingestion while also minimizing storage costs and avoiding the need to store the entire dataset at once. To choose the most efficient option, let's evaluate each of the given solutions in the context of real-time data processing and cost-efficiency.
Key Considerations:
Real-time ingestion: The solution needs to handle data continuously as it arrives.
Efficient data processing: The solution must process only the most recent data without storing excessive amounts of historical data.
Cost minimization: Storing the entire dataset at once should be avoided to minimize costs, especially if not all of the data is needed for immediate analysis.
Evaluation of Options:
A. Create a delta lake for continuous ingestion of live data
Delta Lake is an excellent option for managing large datasets, particularly for batch or micro-batch ingestion. However, it focuses more on storing historical data in a lakehouse architecture and does not inherently specialize in real-time ingestion or minimizing costs by not storing the entire dataset. While it is useful for data consistency and streaming updates, it is not specifically designed for processing real-time, low-latency data without maintaining a large storage footprint.
Not optimized for real-time ingestion without storing large datasets.B. Use a live stream data connector with incremental processing
This is the most efficient option for handling real-time data ingestion while also minimizing storage costs.Live stream data connectors (e.g., using Kusto with Azure Event Hubs or Kafka streams) allow continuous ingestion of real-time data.
Incremental processing means that only new or changed data is processed, minimizing storage usage and avoiding the need to store the entire dataset.
This approach allows real-time data updates without requiring the full dataset to be held in storage continuously, aligning perfectly with the business requirements of processing live data efficiently and cost-effectively.
C. Set up a batch processing pipeline with periodic refresh intervals
Batch processing typically operates in fixed intervals (e.g., every few minutes or hours) rather than providing real-time updates.While this method could be useful for less time-sensitive use cases, it does not meet the requirement of real-time data processing.
Batch processes can result in delays and often involve higher costs due to the frequent need to store large chunks of data for each batch.
D. Use a dataflow with manual refresh triggers for real-time updates
While dataflows can be used to manage data transformation, manual refresh triggers introduce latency and do not provide the real-time ingestion required.Refreshing manually or in a scheduled manner is not optimal for real-time data that needs to be ingested and processed continuously.
This approach does not inherently minimize costs or support a continuous data stream efficiently.
Why Option B is Correct
Using a live stream data connector with incremental processing provides the most efficient and cost-effective solution for real-time ingestion:
The live stream connector supports continuous ingestion of data in real time, reducing the need for expensive batch processing and excessive storage of old data.
Incremental processing ensures that only the latest changes are processed, which minimizes the need to store large amounts of historical data and optimizes both performance and cost.
This approach directly aligns with the goal of processing only the necessary data and avoiding the storage of the entire dataset at once.
To efficiently ingest and process real-time data while minimizing costs, the live stream data connector with incremental processing is the best approach. It enables real-time updates without storing all the data, keeping costs low and processing efficient.
Question 10:
You are working on a Microsoft Fabric project where you need to aggregate and transform data from a large number of sensors in a factory environment. The data includes temperature readings, sensor IDs, timestamps, and status flags. The goal is to calculate the average temperature for each sensor, but only for sensors that have been active (status flag is 'True') in the past 24 hours.
What is the best way to implement this?
A.
sensor_data
| where Status == "True" and Timestamp > ago(24h)
| summarize avg(Temperature) by SensorID
B.
sensor_data
| where Status == "True"
| summarize avg(Temperature) by SensorID
| where Timestamp > ago(24h)
C.
sensor_data
| summarize avg(Temperature) by SensorID, Status
| where Status == "True" and Timestamp > ago(24h)
D.
sensor_data
| summarize avg(Temperature) by SensorID
| where Status == "True" and Timestamp > ago(24h)
Answer: A
Explanation:
In this scenario, the goal is to calculate the average temperature for each sensor, but only for those that have been active (status flag is "True") in the last 24 hours. Let's evaluate each option to see which one meets the requirements.
Key Components of the Query:
Filtering Active Sensors: The Status == "True" condition filters out only those sensors that are active.
Filtering Data from the Last 24 Hours: The condition Timestamp > ago(24h) ensures that only data from the past 24 hours is considered.
Aggregating by Sensor: The aggregation function summarize avg(Temperature) by SensorID computes the average temperature for each sensor.
Evaluation of Options:
A.
sensor_data
| where Status == "True" and Timestamp > ago(24h)
| summarize avg(Temperature) by SensorID
This query is correct.
It first filters the data where the Status is "True" (active sensors) and the Timestamp is within the last 24 hours.
Then, it calculates the average temperature for each sensor using summarize avg(Temperature) by SensorID.
This approach is efficient because it combines the necessary filtering steps before performing the aggregation, ensuring that the data used for the calculation is already constrained to the correct timeframe and active sensors.
B.
sensor_data
| where Status == "True"
| summarize avg(Temperature) by SensorID
| where Timestamp > ago(24h)
This query is incorrect.
While it correctly filters for active sensors and performs the aggregation, the where Timestamp > ago(24h) clause comes after the summarize operation.
The summarize operation first computes the average temperature across all data for each sensor, and then the where clause filters out sensors based on their timestamp.
This is incorrect because the Timestamp condition needs to be applied before the aggregation, not after.
C.
sensor_data
| summarize avg(Temperature) by SensorID, Status
| where Status == "True" and Timestamp > ago(24h)
This query is incorrect.
The summarize operation is grouping by both SensorID and Status, which means it will calculate the average temperature for each combination of sensor and status.
This is not necessary because we only care about sensors that have been active (status "True") in the last 24 hours, and we don’t need to group by Status.
Additionally, the where clause is applied after the aggregation, which is incorrect for the same reason as option B.
D.
sensor_data
| summarize avg(Temperature) by SensorID
| where Status == "True" and Timestamp > ago(24h)
This query is incorrect.
While it correctly aggregates by SensorID, it applies the where clause after the aggregation, which is incorrect because it doesn't filter for active sensors and data from the last 24 hours before performing the aggregation.
It should filter the data first, then aggregate, to avoid using unnecessary or incorrect data in the calculation.
Option A is the most efficient and correct solution because it applies both the status filter and the timestamp filter before performing the aggregation, ensuring that only data from active sensors within the past 24 hours is considered when calculating the average temperature for each sensor.
