Snowflake DSA-C02 Practice Exam Questions & Answers

Explanation

Streamlit is a Python-based web application framework for visualizing data and analyzing results in a more efficient and flexible way. It is an open source library that assists data scientists and academics to develop Machine Learning (ML) visualization dashboards in a short period of time. We can build and deploy powerful data applications with just a few lines of code.

Why Streamlit?

Currently, real-world applications are in high demand and developers are developing new libraries and frameworks to make on-the-go dashboards easier to build and deploy. Streamlit is a library that reduces your dashboard development time from days to hours. Following are some reasons to choose the Streamlit:

It is a free and open-source library.

Installing Streamlit is as simple as installing any other python package

It is easy to learn because you won't need any web development experience, only a basic under-standing of Python is enough to build a data application.

It is compatible with almost all machine learning frameworks, including Tensorflow and Pytorch, Scikit-learn, and visualization libraries such as Seaborn, Altair, Plotly, and many others.

Explanation

By using ethnicity as a sensitive field, and comparing disparity between selection rates and performance metrics for each ethnicity value, you can evaluate the fairness of the model.

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.

Explanation

Data visualization is the visual depiction of data through the use of graphs, plots, and informational graphics. Its practitioners use statistics and data science to convey the meaning behind data in ethical and accurate ways.

Explanation When handling missing or corrupted data, you have multiple valid approaches depending on your situation. You can remove rows with missing values if the loss is acceptable. You can impute missing values by filling them with the mean, median, or mode of the column. For corrupted data, you might flag it, replace it with a placeholder, or delete it entirely. Some techniques use forward fill or backward fill for time series data. Since the question asks how to handle these issues and multiple strategies are legitimate, all of the above captures the reality that different approaches work in different contexts rather than a single best answer.

Explanation

Since the normal curve is symmetric about its mean, its skewness is zero. This is a theoretical expla-nation for mathematical proofs, you can refer to books or websites that speak on the same in detail.

Explanation

NULL values are ignored unless all the records are NULL, in which case a NULL value is returned