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You have an Azure subscription that contains an Azure Synapse Analytics workspace. You create an Azure Data Lake Storage Gen2 account and upload a CSV file named Filel.csv. You need to use Synapse Studio to query the data in Filel.csv by using a serverless SQL pool. Which Transact-SQL operator should you include in the query?
You are using a Python notebook in an Apache Spark pool in Azure Synapse Analytics.
You need to present the data distribution statistics from a DataFrame in a tabular view.
Which method should you invoke on the DataFrame?
See the explanation below.
pandas.DataFrame.corr computes pairwise correlation of columns, excluding NA/null values.
Incorrect:
* freqItems
pyspark.sql.DataFrame.freqItems
Finding frequent items for columns, possibly with false positives. Using the frequent element count algorithm described in https://doi.org/10.1145/762471.762473, proposed by Karp, Schenker, and Papadimitriou.'
* summary is used for index.
* There is no panda method for rollup. Rollup would not be correct anyway.
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?
See the explanation below.
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.
You are using a Python notebook in an Apache Spark pool in Azure Synapse Analytics.
You need to present the data distribution statistics from a DataFrame in a tabular view.
Which method should you invoke on the DataFrame?
See the explanation below.
pandas.DataFrame.describe
Descriptive statistics include those that summarize the central tendency, dispersion and shape of a dataset's distribution, excluding NaN values.
Analyzes both numeric and object series, as well as DataFrame column sets of mixed data types. The output will vary depending on what is provided.
You are using a Python notebook in an Apache Spark pool in Azure Synapse Analytics.
You need to present the data distribution statistics from a DataFrame in a tabular view.
Which method should you invoke on the DataFrame?
See the explanation below.
The aggregating statistic can be calculated for multiple columns at the same time with the describe function.
Example:
titanic[['Age', 'Fare']].describe()
Out[6]:
Age Fare
count 714.000000 891.000000
mean 29.699118 32.204208
std 14.526497 49.693429
min 0.420000 0.000000
25% 20.125000 7.910400
50% 28.000000 14.454200
75% 38.000000 31.000000
max 80.000000 512.329200
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