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Microsoft DP-500 Designing and Implementing Enterprise-Scale Analytics Solutions Using Microsoft Azure and Microsoft Power BI Exam Practice Test
Question 1
You deploy a tabular model named DM! to a Power Bl Premium capacity. DM1 was created as an import model.
You change a fact table named Table1 into a hybrid table.
What else occurred on DM1 automatically after the change?
Answer : D
Question 2
You have a Power Bl workspace that contains one dataset and four reports that connect to the dataset. The dataset uses Import storage mode and contains the following data sources:
* A CSV file in an Azure Storage account
* An Azure Database for PostgreSQL database
You plan to use deployment pipelines to promote the content from development to test to production. There will be different data source locations for each stage. What should you include in the deployment pipeline to ensure that the appropriate data source locations are used during each stage?
Answer : A
Note: 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.
Question 3
You are attempting to configure certification for a Power BI dataset and discover that the certification setting for the dataset is unavailable.
What are two possible causes of the issue? Each correct answer presents a complete solution.
NOTE: Each correct selection is worth one point.
Question 4
Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution.
After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not appear in the review screen.
You have a Power Bl dataset named Dataset1.
In Dataset1, you currently have 50 measures that use the same time intelligence logic.
You need to reduce the number of measures, while maintaining the current functionality.
Solution: From Tabular Editor, you create a calculation group.
Does this meet the goal?
Answer : B
Solution: From DAX Studio, you write a query that uses grouping sets.
A grouping is a set of discrete values that are used to group measure fields.
Question 5
You need to recommend a solution to resolve the query issue of the serverless SQL pool. The solution must minimize impact on the users.
What should you in the recommendation?
Answer : D
Users indicate that queries against the serverless SQL pool fail occasionally because the size of tempdb has been exceeded.
In the dedicated SQL pool resource, temporary tables offer a performance benefit because their results are written to local rather than remote storage.
Temporary tables in serverless SQL pool.
Temporary tables in serverless SQL pool are supported but their usage is limited. They can't be used in queries which target files.
For example, you can't join a temporary table with data from files in storage. The number of temporary tables is limited to 100, and their total size is limited to 100 MB.
Question 6
After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not appear in the review screen.
You are using an Azure Synapse Analytics serverless SQL pool to query a collection of Apache Parquet files by using automatic schema inference. The files contain more than 40 million rows of UTF-8-encoded business names, survey names, and participant counts. The database is configured to use the default collation.
The queries use open row set and infer the schema shown in the following table.
You need to recommend changes to the queries to reduce I/O reads and tempdb usage.
Solution: You recommend using openrowset with to explicitly define the collation for businessName and surveyName as Latim_Generai_100_BiN2_UTF8.
Does this meet the goal?
Answer : A
Query Parquet files using serverless SQL pool in Azure Synapse Analytics.
Important
Ensure you are using a UTF-8 database collation (for example Latin1_General_100_BIN2_UTF8) because string values in PARQUET files are encoded using UTF-8 encoding. A mismatch between the text encoding in the PARQUET file and the collation may cause unexpected conversion errors. You can easily change the default collation of the current database using the following T-SQL statement: alter database current collate Latin1_General_100_BIN2_UTF8'.
Note: If you use the Latin1_General_100_BIN2_UTF8 collation you will get an additional performance boost compared to the other collations. The Latin1_General_100_BIN2_UTF8 collation is compatible with parquet string sorting rules. The SQL pool is able to eliminate some parts of the parquet files that will not contain data needed in the queries (file/column-segment pruning). If you use other collations, all data from the parquet files will be loaded into Synapse SQL and the filtering is happening within the SQL process. The Latin1_General_100_BIN2_UTF8 collation has additional performance optimization that works only for parquet and CosmosDB. The downside is that you lose fine-grained comparison rules like case insensitivity.
Question 7
You use an Apache Spark notebook in Azure Synapse Analytics to filter and transform data.
You need to review statistics for a DataFrame that includes:
The column name
The column type
The number of distinct values
Whether the column has missing values
Which function should you use?
Answer : B
display(df) statistic details
You can use display(df, summary = true) to check the statistics summary of a given Apache Spark DataFrame that include the column name, column type, unique values, and missing values for each column. You can also select on specific column to see its minimum value, maximum value, mean value and standard deviation.
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