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You have a group of data scientists who must create machine learning models and run periodic experiments on a large dataset.
You need to recommend an Azure Synapse Analytics pool for the data scientists. The solution must minimize costs.
Which type of pool should you recommend?
Answer : D
In Azure Synapse, training machine learning models can be performed on the Apache Spark Pools with tools like PySpark/Python, Scala, or .NET.
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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 defining an external table for the Parquet files and updating the query to use the table
Does this meet the goal?
Answer : B
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?
Answer : D
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
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?
Answer : B
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?
Answer : B
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.
