Free DSA-C02 Exam Braindumps (page: 8)

Page 8 of 17

Consider a data frame df with columns ['A', 'B', 'C', 'D'] and rows ['r1', 'r2', 'r3'].
What does the ex- pression df[lambda x : x.index.str.endswith('3')] do?

  1. Returns the row name r3
  2. Results in Error
  3. Returns the third column
  4. Filters the row labelled r3

Answer(s): D

Explanation:

It will Filters the row labelled r3.



Consider a data frame df with 10 rows and index [ 'r1', 'r2', 'r3', 'row4', 'row5', 'row6', 'r7', 'r8', 'r9', 'row10'].
What does the expression g = df.groupby(df.index.str.len()) do?

  1. Groups df based on index values
  2. Groups df based on length of each index value
  3. Groups df based on index strings
  4. Data frames cannot be grouped by index values. Hence it results in Error.

Answer(s): D

Explanation:

Data frames cannot be grouped by index values. Hence it results in Error.



Which command is used to install Jupyter Notebook?

  1. pip install jupyter
  2. pip install notebook
  3. pip install jupyter-notebook
  4. pip install nbconvert

Answer(s): A

Explanation:

Jupyter Notebook is a web-based interactive computational environment. The command used to install Jupyter Notebook is pip install jupyter. The command used to start Jupyter Notebook is jupyter notebook.



Which of the following process best covers all of the following characteristics? · Collecting descriptive statistics like min, max, count and sum. · Collecting data types, length and recurring patterns. · Tagging data with keywords, descriptions or categories. · Performing data quality assessment, risk of performing joins on the data.
· Discovering metadata and assessing its accuracy.
Identifying distributions, key candidates, foreign-key candidates, functional dependencies, embedded value dependencies, and performing inter-table analysis.

  1. Data Visualization
  2. Data Virtualization
  3. Data Profiling
  4. Data Collection

Answer(s): C

Explanation:

Data processing and analysis cannot happen without data profiling--reviewing source data for con- tent and quality. As data gets bigger and infrastructure moves to the cloud, data profiling is increasingly important.
What is data profiling?
Data profiling is the process of reviewing source data, understanding structure, content and interrelationships, and identifying potential for data projects.
Data profiling is a crucial part of:
· Data warehouse and business intelligence (DW/BI) projects--data profiling can uncover data quality issues in data sources, and what needs to be corrected in ETL. · Data conversion and migration projects--data profiling can identify data quality issues, which you can handle in scripts and data integration tools copying data from source to target. It can also un- cover new requirements for the target system.
· Source system data quality projects--data profiling can highlight data which suffers from serious or numerous quality issues, and the source of the issues (e.g. user inputs, errors in interfaces, data corruption).
Data profiling involves:
· Collecting descriptive statistics like min, max, count and sum. · Collecting data types, length and recurring patterns. · Tagging data with keywords, descriptions or categories. · Performing data quality assessment, risk of performing joins on the data.

· Discovering metadata and assessing its accuracy.
· Identifying distributions, key candidates, foreign-key candidates, functional dependencies, embedded value dependencies, and performing inter-table analysis.



Page 8 of 17



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