DP-700 Multiple Select — 30 Questions

Question 1

You are managing a Microsoft Fabric capacity. You need to identify which users are consuming the most compute resources and determine if any throttled requests are occurring. Which TWO tools should you use?

Choose an answer to begin — your selection is scored in the full session.

Accepted Answer

Fabric Capacity Metrics app. Monitoring capacity health requires a combination of real-time metrics and long-term diagnostic logs. The Fabric Capacity Metrics app provides granular visual insights into capacity utilization and throttling, while the Log Analytics workspace offers detailed diagnostic logs for query-level analysis. Using both tools allows engineers to correlate high-level capacity health with specific user-driven workloads that may trigger system-wide throttling or degradation.

Answer

Microsoft Purview

Answer

Service Health Dashboard

Answer

Power BI Desktop Performance Analyzer

Question 2

You need to quickly identify all failed Data Factory pipeline runs and Spark jobs across multiple workspaces in your Microsoft Fabric environment. Which TWO actions should you perform in the Fabric Monitoring Hub?

Accepted Answer

Apply a filter for the 'Failed' status.. The Fabric Monitoring Hub acts as a centralized dashboard for tracking the health of all activities. It allows users to filter by status and item type across the entire environment. This centralized view is essential for data engineers who need to manage multiple pipelines and notebooks without navigating to each individual workspace to check logs.

Answer

Export the logs to an Azure Log Analytics workspace.

Answer

Schedule a daily refresh for the Monitoring Hub dashboard.

Answer

Configure an alert in the Capacity Metrics app.

Question 3

A data engineering team is setting up Git integration for a Microsoft Fabric workspace. They need to ensure that the synchronization process is robust and follows best practices for collaborative development. Which TWO actions should the team perform to successfully manage version control for Fabric items? (Choose TWO)

Accepted Answer

Connect the workspace to an Azure DevOps Git repository and branch.. Git integration in Fabric requires both a connection to a supported remote repository and an understanding of how workspace items are serialized into metadata. Proper synchronization involves committing local workspace changes to the remote branch and managing conflicts through standard Git workflows. This process bridges the gap between low-code workspace editing and professional software development practices for data teams.

Answer

Manually export item .bim files and upload them to a GitHub repository.

Answer

Assign the Workspace Admin role to the Service Principal used for Git.

Answer

Convert all Lakehouses to Warehouses to enable Git compatibility.

Question 4

A data engineering team is using a service principal to automate the deployment and management of Fabric items via the REST API. Which THREE of the following actions must be completed to enable a service principal to work with Fabric APIs?

Accepted Answer

Register an application in Microsoft Entra ID.. Using service principals for automation is a standard practice in enterprise environments. It requires a specific set of configuration steps across both Microsoft Entra ID and the Fabric Admin portal. Ensuring all these steps are completed is necessary for successful authentication and authorization of the automated scripts.

Answer

Grant the service principal the 'Global Administrator' role in Entra ID.

Answer

Assign a Power BI Pro license directly to the service principal.

Question 5

You are setting up a Microsoft Fabric environment for a development team. You want to use Git integration to allow developers to work in their own branches. Which THREE Fabric items are currently supported for Git integration?

Accepted Answer

Lakehouse. Not all Fabric items are currently supported by Git integration. Knowing which items can be synchronized is vital for planning your development lifecycle and source control strategy. As Fabric evolves, more items are added, but the core items like Lakehouses, Notebooks, and Semantic Models form the basis of most synchronization workflows.

Answer

Data Pipeline

Answer

KQL Queryset

Question 6

You are optimizing a Fabric Warehouse for complex analytical queries involving large fact tables. Which TWO actions should you perform to improve query performance?

Accepted Answer

Implement clustered columnstore indexes on the fact table.. Optimizing warehouse performance requires a combination of effective indexing strategies and data distribution management. By choosing the right clustering columns, you reduce data scanning requirements, while partition pruning ensures the engine only reads relevant segments. These techniques are fundamental for scaling analytical workloads in Microsoft Fabric, as they directly influence the amount of data processed during query execution and help maintain consistent performance under high concurrency.

Answer

Convert all tables to heap structures.

Answer

Increase the number of rows per file to 1 billion.

Answer

Disable statistics collection for all columns.

Question 7

You are managing a Microsoft Fabric environment and need to implement 'Endorsement' for several high-quality datasets to help users find reliable data. Which TWO levels of endorsement can you apply to Fabric items?

Accepted Answer

Promoted. Endorsement helps users identify trustworthy and high-quality items in Fabric. It is a key part of data governance. There are two specific levels of endorsement that signify different degrees of validation, helping to distinguish between casual development items and certified, authoritative business assets used for critical reporting.

Answer

Verified

Answer

Approved

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Validated

Question 8

You are configuring a Fabric Pipeline. Which TWO activities can be used to perform conditional logic within your data ingestion workflow?

Accepted Answer

If-Condition activity. Conditional logic is essential for building resilient data pipelines. The If-Condition activity allows for simple branching based on Boolean expressions, while the Switch activity enables multi-way branching based on discrete values. These activities allow engineers to build sophisticated workflows that react to data quality results, source availability, or specific environmental flags, ensuring that the pipeline behaves appropriately under various conditions without manual oversight.

Answer

Lookup activity

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Stored Procedure activity

Answer

Wait activity

Question 9

You are designing a data transformation layer using Spark in Microsoft Fabric. Which THREE of the following are primary benefits of using Delta Lake format over traditional Parquet files? (Choose three)

Accepted Answer

ACID transaction support. Delta Lake adds a transaction log (the Delta Log) to the Parquet file format. This enables ACID transactions, preventing data corruption and partial writes. It also supports schema evolution, allowing the data structure to change over time without breaking downstream applications. Finally, 'Time Travel' allows users to query previous versions of data, which is invaluable for auditing, debugging, and reproducing analytical results. These features are critical for modern, reliable data platforms.

Answer

Smaller storage footprint than Parquet

Answer

Faster cold-start execution for Spark clusters

Question 10

You are developing a Notebook in Microsoft Fabric to process high-volume sensor data. You need to optimize performance using Delta Lake features. Which TWO actions should you perform? (Choose two)

Accepted Answer

Enable Z-Ordering on frequently filtered columns. Optimizing Delta Lake tables involves managing file sizes and data distribution. Z-Ordering optimizes multi-column queries by co-locating related data, significantly reducing read amplification. Compaction (using OPTIMIZE) merges small files into larger, more efficient files, which directly improves scan performance. Implementing these features is essential for maintaining query speed as data grows in the Lakehouse, ensuring that the Spark engine spends less time scanning unnecessary file blocks during execution.

Answer

Manually delete old Parquet files from the file system

Answer

Convert the table to CSV format

Answer

Increase the number of partitions to equal total file count

Question 11

You are optimizing a Spark job in Fabric. Which TWO of the following techniques will help reduce the execution time? (Choose two)

Accepted Answer

Cache frequently used DataFrames. Reducing execution time in Spark requires efficient resource utilization and minimizing unnecessary data processing. Caching DataFrames that are reused multiple times prevents redundant computations, while enabling adaptive query execution allows Spark to dynamically optimize the physical plan based on runtime statistics. Together, these strategies significantly lower latency and resource consumption, leading to a much faster and more cost-effective data transformation process within the Fabric Notebook environment.

Answer

Always set the number of partitions to 1

Answer

Convert all data to JSON

Answer

Increase the idle timeout

Question 12

You are configuring a Dataflow Gen2 to perform incremental updates for a large fact table. Which TWO steps are essential to ensure the dataflow only processes new or changed records during each run?

Accepted Answer

Create a parameter to store the last successful refresh date.. Incremental loading in Dataflows Gen2 requires a mechanism to identify new data and a logic to handle how that data is integrated into the destination. This typically involves using parameters to filter source data based on a 'last modified' date and configuring the destination to append new records.

Answer

Enable 'High Concurrency' mode for the Dataflow.

Answer

Use the 'Remove Duplicates' transformation on the entire table.

Answer

Configure a 'Tumbling Window' trigger in the Lakehouse.

Question 13

You are optimizing a Fabric pipeline that processes data from a high-frequency sensor source. You notice significant data skew during the join operation. Which three strategies should you implement to mitigate this skew?

Accepted Answer

Apply salting to the skewed join key.. Data skew occurs when one partition or key carries significantly more data than others, causing some executors to work much longer than others. Using salting to redistribute skewed keys, enabling skewed join optimization, or pre-aggregating the skewed dataset are proven techniques to balance the workload across the cluster. Implementing these strategies ensures that no single worker becomes a bottleneck, leading to more predictable execution times and preventing memory failures in large-scale data processing jobs.

Answer

Increase the number of partitions to the maximum.

Answer

Change the file format to CSV.

Question 14

You are analyzing a performance bottleneck using the 'Timepoint Detail' page in the Fabric Capacity Metrics app. Which THREE pieces of information can you find here to help identify the specific cause of a capacity overage? (Select THREE)

Accepted Answer

The name of the operation and the item that triggered it.. The Timepoint Detail page is the most granular view in the Capacity Metrics app. It allows you to see exactly what was happening during a specific 30-second window when the capacity was under load. This detail is essential for identifying 'noisy neighbors' or specific jobs that are consuming more than their fair share of resources.

Answer

The physical IP address of the Spark executor nodes.

Answer

The SQL execution plan for every query in the timepoint.

Question 15

You are optimizing a Data Warehouse in Microsoft Fabric. Which TWO strategies will effectively improve the performance of read-heavy workloads?

Accepted Answer

Implement Clustered Columnstore Indexes.. For read-heavy workloads in a data warehouse, indexing and proper table design are critical. Clustered Columnstore Indexes provide massive data compression and are optimized for analytical queries that scan large volumes of data. Additionally, implementing partitions allows the engine to prune data at the storage level, ensuring that only relevant partitions are scanned, thus reducing the total I/O and accelerating query execution.

Answer

Increase the number of rows per transaction.

Answer

Use row-based storage exclusively.

Answer

Disable data compression.

Question 16

You are configuring a deployment pipeline to move a Lakehouse from a Test workspace to a Production workspace. Which THREE types of deployment rules can you apply to ensure the Lakehouse functions correctly in the Production environment?

Accepted Answer

Data source rules. Deployment rules are essential for tailoring Fabric items to their destination environment. For Lakehouses and semantic models, you can define rules that change the underlying data sources or parameters. This prevents the Production environment from accidentally referencing Test data and allows for a seamless transition that maintains data integrity and security across the lifecycle stages.

Answer

Lakehouse name rules

Answer

User permission rules

Question 17

You are implementing a security strategy for Microsoft Fabric. You need to ensure that data access is restricted based on sensitivity labels and that these labels persist when data is exported to Excel. Which TWO statements describe the behavior of sensitivity labels in Fabric? (Choose TWO)

Accepted Answer

Labels applied to a semantic model can be automatically inherited by downstream reports.. Sensitivity labels from Microsoft Purview Information Protection are integrated into Fabric to provide end-to-end data governance. These labels help classify data and can apply encryption or watermarks that follow the data even after it leaves the service. Understanding how these labels propagate and protect data is essential for maintaining compliance in a modern analytics solution.

Answer

Sensitivity labels in Fabric only affect the UI color and do not provide protection.

Answer

Labels are only available for Power BI items and cannot be applied to Lakehouses.

Answer

Sensitivity labels automatically delete any data that is older than three years.

Question 18

You are optimizing a Spark Notebook in Fabric that processes large Parquet files. Which TWO actions will improve the performance of data transformations?

Accepted Answer

Enable V-Order on the Delta tables.. Optimizing Spark performance in Fabric involves managing how data is distributed across the cluster and how it is physically stored. Using V-Order ensures that files are optimized for Fabric's compute engines, while proper partitioning prevents data skew and allows for efficient parallel processing across the available Spark nodes in the workspace.

Answer

Increase the 'isSequential' property to True.

Answer

Convert all files to the Avro format for faster reads.

Answer

Disable the Spark 'High Concurrency' mode.

Question 19

You are optimizing the ingestion of a large dataset into a Microsoft Fabric Lakehouse using a Spark Notebook. To ensure optimal query performance for downstream consumers, you want to apply the V-Order optimization and partition the data. Which TWO actions should you take to achieve this?

Accepted Answer

Set the spark.sql.parquet.vorder.enabled configuration to true.. Optimizing data in a Fabric Lakehouse involves both physical file organization and metadata enhancements. V-Order is a Microsoft proprietary optimization that improves read performance for Delta tables by reordering data within Parquet files. Partitioning organizes data into folders based on specific columns, which allows the Spark engine to prune unnecessary data during query execution, significantly reducing I/O.

Answer

Create a shortcut to the data in an Azure Data Lake Storage Gen2 account.

Answer

Convert the files to CSV format before ingestion to reduce overhead.

Answer

Apply a Z-Order index on the primary key column during the write operation.

Question 20

You are configuring a Data Factory Pipeline to perform an incremental load from an Azure SQL Database to a Fabric Warehouse. Which THREE components or features are essential for implementing a watermark-based incremental load? (Choose THREE)

Accepted Answer

A Lookup activity to retrieve the last stored watermark value.. Incremental loading requires tracking the last processed record to avoid re-processing the entire dataset. A watermark-based approach uses a value (like a timestamp or ID) to identify new data. This necessitates a way to store the last watermark, a way to retrieve the new maximum watermark, and a way to filter the source query accordingly.

Answer

A Shortcut to the Azure SQL Database.

Answer

Mirroring for Azure SQL Database.