Reasons to Choose Our Microsoft DP-500 Exam Dumps
Microsoft DP-500 Exam Dumps - Curated by Subject Matter Experts
Are you tired of getting Microsoft DP-500 dumps with wrong answers? Don’t worry now because our Designing and Implementing Enterprise-Scale Analytics Solutions Using Microsoft Azure and Microsoft Power BI exam dumps are curated by subject matter experts ensuring every question has the right answer
Prepare Your Exam with Microsoft DP-500 Dumps on Any Device
We facilitate you by offering our Microsoft DP-500 exam dumps in three different formats (PDF file, Offline, and Online Practice Test Software)
Self-Assess Your Azure Enterprise Data Analyst Associate Exam Preparation
Self-Assess Your Microsoft DP-500 exam preparation with our DP-500 dumps enriched with various features such as time limit, personalized result page, etc
Eliminate Risk of Failure with Microsoft DP-500 Exam Dumps
Schedule your time wisely to provide yourself sufficient time each day to prepare for the Microsoft DP-500 exam. Make time each day to study in a quiet place, as you'll need to thoroughly cover the material for the Designing and Implementing Enterprise-Scale Analytics Solutions Using Microsoft Azure and Microsoft Power BI exam. Our actual Azure Enterprise Data Analyst Associate exam dumps help you in your preparation. Prepare for the Microsoft DP-500 exam with our DP-500 dumps every day if you want to succeed on your first try.
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
Are You Looking for More Updated and Actual Microsoft DP-500 Exam Questions?
If you want a more premium set of actual Microsoft DP-500 Exam Questions then you can get them at the most affordable price. Premium Azure Enterprise Data Analyst Associate exam questions are based on the official syllabus of the Microsoft DP-500 exam. They also have a high probability of coming up in the actual Designing and Implementing Enterprise-Scale Analytics Solutions Using Microsoft Azure and Microsoft Power BI exam.
You will also get free updates for 90 days with our premium Microsoft DP-500 exam. If there is a change in the syllabus of Microsoft DP-500 exam our subject matter experts always update it accordingly.