DP-700 Practice Test

Answer choices

1. A. Assign a larger F SKU immediately without reviewing workload utilization, for the described technical objective and its associated operational control requirements, in practice.
2. B. Use the Microsoft Fabric Capacity Metrics app to review utilization, throttling, and workload patterns before resizing the capacity
3. C. Increase the number of concurrent users so the capacity is used more efficiently, for the required operational result and control objective.
4. D. Configure Spark pool autoscaling only, because all Fabric capacity pressure comes from Spark jobs, as the selected response to the described condition.

Wrong-answer review

• A. Assign a larger F SKU immediately without reviewing workload utilization, for the described technical objective and its associated operational control requirements, in practice.: Resizing immediately can overcorrect if the underlying issue is a specific workload or schedule.
• C. Increase the number of concurrent users so the capacity is used more efficiently, for the required operational result and control objective.: Adding users increases demand and does not solve capacity pressure.
• D. Configure Spark pool autoscaling only, because all Fabric capacity pressure comes from Spark jobs, as the selected response to the described condition.: Spark pool autoscaling applies only to Spark workloads and does not diagnose all Fabric capacity usage.

Answer choices

1. A. Configure a custom Spark pool with a fixed number of executors and a higher memory allocation per executor, based on the observed peak resource utilization during initial testing, and assign this pool to the data pipeline
2. B. Create a new, dedicated Spark pool with a larger number of driver nodes and a higher memory allocation per executor, and then assign this pool to all existing data pipelines, for the described technical objective and its associated operational control requirements, as configured.
3. C. Increase the auto-scaling limits of the default Spark pool to dynamically adjust the number of executors based on workload demand, while maintaining the existing memory allocation per executor, as the primary implementation for the described business requirement.
4. D. Implement a cost-saving strategy by reducing the number of executors in the default Spark pool and monitoring performance; if issues arise, increase the number of executors as needed, for the described technical objective and its associated operational control requirements.

Answer choices

1. A. Configure permissions separately on each OneLake folder and file for every user and group, under the organization’s defined implementation and exception-management process.
2. B. Apply an organization-wide default that overrides all item-level security roles, for the described technical objective and its associated operational control requirements, for evaluation.
3. C. Use the default OneLake security role for baseline access and create item-level OneLake security roles for specific table and folder exceptions
4. D. Turn off item-scoped access roles and manage permissions only from the organization scope, for the described technical objective and its associated operational control requirements, as configured.

Answer choices

1. A. Manually rewrite the T-SQL query to use different join algorithms before reviewing the optimizer's plan, under the stated decision criteria.
2. B. Use SHOWPLAN_XML in SQL Server Management Studio to review the query execution plan and identify scan, join, or non-scalable operations, as configured.
3. C. Add query hints immediately to force a specific execution plan before reviewing the query behavior, as the selected approach for the stated technical and business outcome.
4. D. Increase the Fabric capacity size before identifying whether the query is affected by plan shape, cold cache, or data volume, as the primary proposed approach.

Answer choices

1. A. kusto Events | summarize count() by DeviceType | project TimeGenerated=now(), DeviceType, ClickCount=count_, within the defined security and accountability boundaries.
2. B. kusto Events | summarize count() by DeviceType, bin(TimeGenerated, 5m) | project TimeGenerated, DeviceType, ClickCount=count_, for the described technical objective and its associated operational control requirements, as configured.
3. C. kusto Events | where TimeGenerated >= ago(5m) | summarize count() by DeviceType | project TimeGenerated=now(), DeviceType, ClickCount=count_, as the proposed ingest and transform data approach.
4. D. kusto Events | extend TimeGenerated = bin(TimeGenerated, 5m) | summarize count() by DeviceType, TimeGenerated | project TimeGenerated, DeviceType, ClickCount=count_, for this task.

Answer choices

1. A. Use a PowerShell script to call the Fabric REST API endpoint for updating dataflow compute properties after the deployment pipeline completes, for review.
2. B. Modify the dataflow definition file (.fabriclake) to include the compute settings and deploy the entire package, for the stated implement and manage an analytics solution requirement.
3. C. Create a custom Azure Function triggered by the deployment pipeline to update the dataflow's compute settings, as the primary proposed approach.
4. D. Utilize a Fabric Deployment Pipeline rule to set the compute properties as part of the deployment process, for the required operational result and control objective.

Answer choices

1. A. Fabric Activity Logs, focusing on detailed error messages and timestamps related to the Copy activity, for the described technical objective and its associated operational control requirements, for this requirement.
2. B. Fabric Workspace Settings, checking network configurations and firewall rules to ensure connectivity to the on-premises SQL Server, within the defined security and accountability boundaries.
3. C. Monitor Hub, examining pipeline run details, activity run states, and associated error messages to pinpoint the failing Copy activity and its error details, under end-to-end security-and-governance requirements.
4. D. Lakehouse SQL Analytics Endpoint query history, analyzing query performance metrics to identify potential bottlenecks impacting the Copy activity, for the described technical objective and its associated operational control requirements, as proposed.

Answer choices

1. A. Define a pipeline parameter named 'partitions' and pass its value to the Spark notebook using the 'display name' property in the notebook activity, for the described technical objective and its associated operational control requirements, within the described context.
2. B. Create a dataflow Gen2 to dynamically generate a JSON file containing the partition count for each region and then use a Copy Activity to pass this file as input to the Spark notebook, under organization-wide implementation-governance requirements.
3. C. Use a lookup activity to retrieve the partition count from a configuration table in a SQL Analytics endpoint and then pass the result to the Spark notebook using the 'display name' property in the notebook activity
4. D. Define a pipeline parameter named 'partitions' and pass its value to the Spark notebook using the 'arguments' property in the notebook activity, for the described technical objective and its associated operational control requirements, for the stated implementation and support requirements.

Answer choices

1. A. Create a new domain specifically for the Finance team and migrate all their workspaces, under end-to-end security-and-governance requirements.
2. B. Associate the Finance workspace with a newly created sub-domain within the 'Global Retail' domain, under the described implement and manage an analytics solution criteria.
3. C. Remove the Finance workspace from the 'Global Retail' domain and create it as a standalone workspace, within cross-functional operational-accountability boundaries.
4. D. Configure workspace-level access controls and data policies directly within the Finance workspace, without changing its domain association, as described.

Answer choices

1. A. Examine the Spark History Server's 'Jobs' tab, specifically looking at the overall job duration and the number of tasks completed, under the documented operational and governance requirements.
2. B. Check the Fabric workspace's auto-scaling settings for the default Spark pool to ensure sufficient resources are allocated, for the described technical objective and its associated operational control requirements.
3. C. Analyze the Spark History Server's 'Applications' tab to identify the user who submitted the job and their associated permissions, within the proposed design.
4. D. Review the Spark History Server to analyze the 'Stages' tab, focusing on task durations, shuffle read/write times, and executor metrics for each stage, for evaluation.