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Microsoft DP-500: Designing and Implementing Enterprise-Scale Analytics Solutions Using Microsoft Azure and Microsoft Power BI is a certification exam that measures an individual's ability to design and implement enterprise-scale data solutions using the Microsoft Azure platform and Microsoft Power BI tools. DP-500 exam is designed for solution architects, data engineers, and data professionals who are looking to validate their skills in designing, building, and deploying analytics solutions.
NEW QUESTION # 69
You have a deployment pipeline for a Power BI workspace. The workspace contains two datasets that use import storage mode.
A database administrator reports a drastic increase in the number of queries sent from the Power Bi service to an Azure SQL database since the creation of the deployment pipeline.
An investigation into the issue identifies the following:
One of the datasets is larger than 1 GB and has a fact table that contains more than 500 million rows.
When publishing dataset changes to development, test, or production pipelines, a refresh is triggered against the entire dataset.
You need to recommend a solution to reduce the size of the queries sent to the database when the dataset changes are published to development, test, or production.
What should you recommend?
- A. Configure the dataset to use a composite model that has a DirectQuery connection to the fact table.
- B. From Capacity settings in the Power Bi Admin portal, reduce the Max Intermediate Row Set Count setting.
- C. Request the authors of the deployment pipeline datasets to reduce the number of datasets republished during development.
- D. In the dataset, delete the fact table.
Answer: A
Explanation:
Explanation
Previously in Power BI Desktop, when you used a DirectQuery in a report, no other data connections, whether DirectQuery or import, were allowed for that report. With composite models, that restriction is removed. A report can seamlessly include data connections from more than one DirectQuery or import data connection, in any combination you choose.
The composite models capability in Power BI Desktop consists of three related features:
* Composite models: Allows a report to have two or more data connections from different source groups, such as one or more DirectQuery connections and an import connection, two or more DirectQuery connections, or any combination thereof.
* Etc.
Reference: https://docs.microsoft.com/en-us/power-bi/transform-model/desktop-composite-models
NEW QUESTION # 70
What should you configure in the deployment pipeline?
- A. auto-binding
- B. a data source rule
- C. a selective deployment
- D. a backward deployment
Answer: B
Explanation:
Development Process Requirements
Litware identifies the following development process requirements:
SQLDW and datalake1 will act as the development environment. Once feature development is complete, all entities in synapseworkspace1 will be promoted to a test workspace, and then to a production workspace.
Power BI content must be deployed to test and production by using deployment pipelines.
Create deployment rules
When working in a deployment pipeline, different stages may have different configurations. For example, each stage can have different databases or different query parameters. The development stage might query sample data from the database, while the test and production stages query the entire database.
When you deploy content between pipeline stages, configuring deployment rules enables you to allow changes to content, while keeping some settings intact. For example, if you want a dataset in a production stage to point to a production database, you can define a rule for this. The rule is defined in the production stage, under the appropriate dataset. Once the rule is defined, content deployed from test to production, will inherit the value as defined in the deployment rule, and will always apply as long as the rule is unchanged and valid.
You can configure data source rules and parameter rules.
Incorrect:
Not B: if you already have a steady production environment, you can deploy it backward (to Test or Dev, based on your need) and set up the pipeline. The feature is not limited to any sequential orders.
Topic 3, Fabrikam, Inc.
Overview
Fabrikam, Inc. is a software company that develops educational software for higher education.
Fabrikam has the following business units:
* Finance and Accounting
* Sales and Marketing
* Operations
* Product
The Product business unit contains the following groups:
* User experience designers
* Software engineers
* Product managers
* Testers
The Operations business unit contains an information technology (IT) group. The IT group contains an enterprise analytics team and an information security team.
Fabrikam has three Azure Synapse Analytics workspaces named workspace! prod, workspace 1 test, and workspaceldev. Each workspace is connected to an Azure Data Lake Storage account and contains a lake database that is accessed by using the built-in serverless SQL pool. The data in the Data Lake Storage accounts is available in the lake databases for analysts from every business unit to query and analyze by using Power Bl.
The company imports the following files into the Data Lake Storage accounts:
* User experience data stored as JSON files
* Finance data stored as CSV files
* Sales data stored as CSV files
Fabrikam has a Power Bl tenant that contains 30 workspaces in Pro license mode. The data in the workspaces is a mix of Import and DirectQuery datasets. All reports are interactive.
Fabrikam has three frequently used workspaces as shown in the following table.
The Corporate Data Models workspace contains a dataset named Financial Model that is used by reports in the P&L workspace. Financial Model is maintained by the enterprise analytics team. The Corporate Data Models workspace and the User Experience workspace have corresponding development and test workspaces.
Administrators report an increase in the maintenance of Power Bl tenant assets due to analysts in the Finance and Accounting business unit who create new Power Bl datasets when the existing datasets already meet their needs.
Analysts in the Product business unit report the following issues:
* Datasets are published to the User Experience workspace, while the data sources reference workspace! test.
* The parsing of user experience data in Power Query is very slow.
The enterprise analytics team identifies two DAX measures in the Financial Model dataset that are consistently slow to execute. The team must identify all the reports that use the Financial Model dataset and notify the report owners of changes to the measures.
Members of the enterprise analytics team report that creating Power Bl reports and adjusting tables and views in Azure Synapse is tedious because they must switch between the Power Bl workspaces and the Azure Synapse workspaces.
The information security team identifies that the user experience data is being shared externally.
Fabrikam plans to implement the following changes:
* Power Bl will be registered as a data source in Microsoft Purview.
* The analysts in the Product business unit will create a more automated process for deploying Power Bl reports and datasets to the User Experience workspace.
The enterprise analytics team plans to perform the following tasks:
* Update the DAX calculations in the Financial Model dataset.
* Create views in the Azure Synapse workspaces to speed up the parsing of user experience data.
* Create and document the change management process for shared Power Bl datasets.
From Microsoft Purview, analysts in all the business units must be able to see all the assets in the Power Bl tenant and the Azure Synapse workspaces. Power Bl asset information must include lineage to identify the data sources used by each report.
The information security team must identify all the Power Bl reports and datasets that contain Personally Identifiable Information (Pll).
Fabrikam requires a security solution for the Power Bl tenant. The solution must meet the following requirements:
* Access to the tenant by external users must be approved by a manager and granted by the IT group.
* The external users must be prevented from publishing or modifying content in the tenant
* Users must be prevented from sharing Power Bl reports publicly to the internet.
The new process for deploying Power Bl reports and datasets to the User Experience workspace must ensure that the datasets point to the lake database to which the relevant dataset is deployed. The views in each lake database must present the data in a tabular format.
NEW QUESTION # 71
You have a dataset that contains a table named UserPermissions. UserPermissions contains the following data.
You plan to create a security role named User Security for the dataset. You need to filter the dataset based on the current users. What should you include in the DAX expression?
- A. [User] = USERNAME()
- B. [UserPermissions] - USERPRINCIPALNAME()
- C. [UserPermissions] - USERNAME()
- D. [User] = USERPRINCIPALNAME()
- E. [User] = USEROBJECTID()
Answer: A
Explanation:
USERNAME() returns the domain name and username from the credentials given to the system at connection time.
It should be compared to column name of User, which in DAX is expressed through [User].
NEW QUESTION # 72
You are optimizing a Power Bl data model by using DAX Studio.
You need to capture the query events generated by a Power Bl Desktop report.
What should you use?
- A. a Query Plan trace
- B. an All Queries trace
- C. the DMV list
- D. a Server Timings trace
Answer: B
Explanation:
The All Queries trace in Dax Studio supports capturing the query events from all client tools (not just queries sent from DAX Studio like the Query Plan and Server Timings features do). The 'All Queries" trace is really useful when you wish to see the queries that are generated by a client tool like Power BI Desktop.
NEW QUESTION # 73
You are configuring Azure Synapse Analytics pools to support the Azure Active Directory groups shown in the following table.
Which type of pool should each group use? To answer, drag the appropriate pool types to the groups. Each pool type may be used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation
Box 1: Apache Spark pool
An Apache Spark pool provides open-source big data compute capabilities. After you've created an Apache Spark pool in your Synapse workspace, data can be loaded, modeled, processed, and distributed for faster analytic insight.
Box 2: Dedicated SQL Pool
Dedicated SQL Pool - Data is stored in relational tables
Box 3: Serverless SQL pool
Serverless SQL pool - Cost is incurred for the data processed per query Reference:
https://docs.microsoft.com/en-us/azure/synapse-analytics/quickstart-create-apache-spark-pool-portal
https://www.royalcyber.com/blog/data-services/dedicated-sql-pool-vs-serverless-sql/
NEW QUESTION # 74
You have the following code in an Azure Synapse notebook.
Use the drop-down menus to select the answer choice that completes each statement based on the information presented in the code.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation
Box 1: three scatterplots
Compare Plots
Example, Draw two plots on the same figure:
import matplotlib.pyplot as plt
import numpy as np
#day one, the age and speed of 13 cars:
x = np.array([5,7,8,7,2,17,2,9,4,11,12,9,6])
y = np.array([99,86,87,88,111,86,103,87,94,78,77,85,86])
plt.scatter(x, y)
#day two, the age and speed of 15 cars:
x = np.array([2,2,8,1,15,8,12,9,7,3,11,4,7,14,12])
y = np.array([100,105,84,105,90,99,90,95,94,100,79,112,91,80,85])
plt.scatter(x, y)
plt.show()
Result:
Chart, scatter chart Description automatically generated
Box 2: three marker symbols
One for each scatterplot. One default, and two defined.
Default is point.
v is triangle down.
^ is triangle up.
Reference: https://www.w3schools.com/python/matplotlib_scatter.asp
https://matplotlib.org/stable/api/markers_api.html
NEW QUESTION # 75
You are using DAX Studio to analyze a slow-running report query. You need to identify inefficient join operations in the query. What should you review?
- A. the query plan
- B. the server timings
- C. the query history
- D. the query statistics
Answer: A
Explanation:
Explanation
Open DAX Studio.
Paste the query there, enable Query Plan display and Server Timings, run your query (with clear cache), and then study the query plan for large row counts. Once the culprit is identified you can decide how to rewrite your DAX to make that part faster.
Reference: https://community.powerbi.com/t5/Power-Query/DAX-Query-taking-longer-time/td-p/1171961
https://www.sqlbi.com/wp-content/uploads/DAX-Query-Plans.pdf
NEW QUESTION # 76
You have a Power BI report and dataset in Power BI Desktop. The dataset contains a calculation group named CG1 that contains four calculation items. The Current calculation item is shown in the following exhibit.
Answer:
Explanation:
NEW QUESTION # 77
You are optimizing a dataflow in a Power Bl Premium capacity. The dataflow performs multiple joins. You need to reduce the load time of the dataflow.
Which two actions should you perform? Each correct answer presents part of the solution. NOTE: Each correct selection is worth one point.
- A. Execute non-foldable operations before foldable operations.
- B. Place the ingestion operations and transformation operations in a single dataflow.
- C. Execute foldable operations before non-foldable operations.
- D. Reduce the memory assigned to the dataflows.
- E. Place the ingestion operations and transformation operations in separate dataflows.
Answer: C,E
Explanation:
Using the compute engine to improve performance
Take the following steps to enable workloads trigger the compute engine, and always improve performance:
For computed and linked entities in the same workspace:
Ensure you perform the operations that fold, such as merges, joins, conversion, and others.
For ingestion focus on getting the data into the storage as fast as possible, using filters only if they reduce the overall dataset size. It's best practice to keep your transformation logic separate from this step, and allow the engine to focus on the initial gathering of ingredients. Next, separate your transformation and business logic into a separate dataflow in the same workspace, using linked or computed entities; doing so allows for the engine to activate and accelerate your computations. In our analogy, it's like food preparation in the kitchen: food preparation is typically a separate and distinct step from gathering your raw ingredients, and a pre-requisite for putting the food in the oven. Similarly, your logic needs to be prepared separately before it can take advantage of the compute engine.
NEW QUESTION # 78
You have the Power BI workspaces shown in the following exhibit.
Use the drop-down menus to select the answer choice that completes each statement based on the information presented in the graphic.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation
Box 1: Infrastrucrue Svcs
Infrastrucrue Svcs is a Premium workspace.
If users have a free license and the workspace is stored in Premium (dedicated) capacity, they will be able to view and interact with the content in that workspace.
If users have a free license and the workspace is stored in shared capacity (not premium), they will not be able to see the content in shared workspace, only "My workspace".
If users have pro license, they will be able to view and interact with the content in that workspace.
Box 2: Admin
We need to activate the Orpaned workspace.
An orphaned workspace is one that does not have an admin assigned.
If you're a Service Admin, you can now view all of your organization's workspaces through the Admin Portal in the user interface.
Graphical user interface, table Description automatically generated with medium confidence
It's easy to Recover an orphan from this screen. Simply select the workspace and click Recover, then add yourself or another user as an admin.
Reference:
https://community.powerbi.com/t5/Service/Difference-between-Public-and-Private-workspace/m-p/1382219
https://docs.microsoft.com/en-us/power-bi/admin/service-admin-portal-workspaces
NEW QUESTION # 79
You have an Azure Synapse Analytics workspace that contains an Apache Spark pool. You create a notebook and configure a cell that runs the following SparkSQL query.
SELECT ProductID, ProductName, Category From products.
You need to create a column chart by using the built-in charting capability. The solution must visualize the distribution of product IDs across product categories.
Answer:
Explanation:
Explanation
NEW QUESTION # 80
You are running a diagnostic against a query as shown in the following exhibit.
What can you identify from the diagnostics query?
- A. All the query steps are folding.
- B. Some query steps are folding.
- C. Elevated permissions are being used to query records.
- D. The query is timing out.
Answer: A
Explanation:
Understanding folding with Query Diagnostics
One of the most common reasons to use Query Diagnostics is to have a better understanding of what operations were 'pushed down' by Power Query to be performed by the back-end data source, which is also known as 'folding'. If we want to see what folded, we can look at what is the 'most specific' query, or queries, that get sent to the back-end data source. We can look at this for both ODATA and SQL.
NEW QUESTION # 81
You need to build a Transact-SQL query to implement the planned changes for the internal users.
How should you complete the Transact-SQL query? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation
Box 1: PREDICT
Provide internal users with the ability to incorporate machine learning models loaded to the dedicated SQL pool.
The example below shows a sample query using prediction function. An additional column with name Score and data type float is created containing the prediction results. All the input data columns as well as output prediction columns are available to display with the select statement.
-- Query for ML predictions
SELECT d.*, p.Score
FROM PREDICT(MODEL = (SELECT Model FROM Models WHERE Id = 1),
DATA = dbo.mytable AS d, RUNTIME = ONNX) WITH (Score float) AS p;
Box 2: WITH
Reference:
https://docs.microsoft.com/en-us/azure/synapse-analytics/sql-data-warehouse/sql-data-warehouse-predict
NEW QUESTION # 82
You need to recommend a solution for the customer workspaces to support the planned changes.
Which two configurations should you include in the recommendation? Each correct answer presents part of the
solution.
NOTE: Each correct selection is worth one point.
- A. Publish the financial data to the web.
- B. Set Use datasets across workspaces to Enabled
- C. Configure the FinData workspace to use a Power Bl Premium capacity.
- D. Grant the Build permission for the financial data to each customer.
Answer: D
NEW QUESTION # 83
You are using an Azure Synapse notebook to create a Python visual.
You run the following code cell to import a dataset named Iris.
A sample of the data is shown in the following table.
You need to create the visual shown in the exhibit. (Click the Exhibit tab.)
How should you complete the Python code? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation
Box 1: pairplot
A pairs plot allows us to see both distribution of single variables and relationships between two variables. Pair plots are a great method to identify trends for follow-up analysis and, fortunately, are easily implemented in Python!
Example, let's plot data using pairplot:
From the picture below, we can observe the variations in each plot. The plots are in matrix format where the row name represents x axis and column name represents the y axis. The main-diagonal subplots are the univariate histograms (distributions) for each attribute.
A picture containing diagram Description automatically generated
Box 2: sepal_width
sepal_width is displayed with a height of 2.5 (between 2.0 and 4.5).
Reference: https://medium.com/analytics-vidhya/pairplot-visualization-16325cd725e6
NEW QUESTION # 84
You have a Power Bl data model.
You need to refresh the data from the source every 15 minutes.
What should you do first?
- A. Define an incremental refresh policy.
- B. Configure a scheduled refresh.
- C. Change the storage mode of the dataset.
- D. Enable the XMLA endpoint.
Answer: D
Explanation:
You cannot schedule a refresh on 15-minute increments. Only 30, unless using XMLA endpoint.
NEW QUESTION # 85
You are using Azure Synapse Studio to explore a dataset that contains data about taxi trips.
You need to create a chart that will show the total trip distance according to the number of passengers as shown in the following exhibit.
How should you configure the chart? To answer, select the appropriate options in the answer are a. NOTE: Each correct selection is worth one point.
Answer:
Explanation:
NEW QUESTION # 86
You are building a Power Bl dataset that contains a table named Calendar. Calendar contains the following calculated column.
pfflag = IF('Calendar'[Date] < TOOAYQ, "Past", "Future")
You need to create a measure that will perform a fiscal prior year-to-date calculation that meets the following requirements:
* Returns the fiscal prior year-to-date value for [sales Amount]
* Uses a fiscal year end of June 30
* Produces no result for dates in the future
How should you complete the DAX expression? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Reference:
https://docs.microsoft.com/en-us/dax/sameperiodlastyear-function-dax
https://docs.microsoft.com/en-us/dax/datesytd-function-dax
NEW QUESTION # 87
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