Snowflake DSA-C02 Online Practice Questions and Exam Preparation

Snowflake DSA-C02 Online Questions & Answers

• Question 61:

  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.

  A. Data Visualization
  B. Data Virtualization
  C. Data Profiling
  D. Data Collection
  C. Data Profiling

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  Explanation/Reference:

  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--dataprofiling 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.

• Question 62:

  Secure Data Sharing do not let you share which of the following selected objects in a database in your account with other Snowflake accounts?

  A. Sequences
  B. Tables
  C. External tables
  D. Secure UDFs
  A. Sequences

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  Explanation/Reference:

  Secure Data Sharing lets you share selected objects in a database in your account with other Snow-flake accounts. You can share the following Snowflake database objects: Tables External tables Secure views Secure materialized views Secure UDFs Snowflake enables the sharing of databases through shares, which are created by data providers and "imported" by data consumers.

• Question 63:

  Which one is not Types of Feature Scaling?

  A. Economy Scaling
  B. Min-Max Scaling
  C. Standard Scaling
  D. Robust Scaling
  B. Min-Max Scaling

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  Explanation/Reference:

  Feature Scaling

  Feature Scaling is the process of transforming the features so that they have a similar scale. This is important in machine learning because the scale of the features can affect the performance of the model.

  Types of Feature Scaling:

  Min-Max Scaling: Rescaling the features to a specific range, such as between 0 and 1, by subtracting the minimum value and dividing by the range. Standard Scaling: Rescaling the features to have a mean of 0 and a standard deviation of 1

  by subtracting the mean and dividing by the standard deviation. Robust Scaling: Rescaling the features to be robust to outliers by dividing them by the interquartile range.

  Benefits of Feature Scaling:

  Improves Model Performance: By transforming the features to have a similar scale, the model can learn from all features equally and avoid being dominated by a few large features.

  Increases Model Robustness: By transforming the features to be robust to outliers, the model can become more robust to anomalies.

  Improves Computational Efficiency: Many machine learning algorithms, such as k-nearest neighbors, are sensitive to the scale of the features and perform better with scaled features.

  Improves Model Interpretability: By transforming the features to have a similar scale, it can be easier to understand the model's predictions.

• Question 64:

  There are a couple of different types of classification tasks in machine learning, Choose the Correct Classification which best categorized the below Application Tasks in Machine learning?

  To detect whether email is spam or not

  To determine whether or not a patient has a certain disease in medicine.

  To determine whether or not quality specifications were met when it comes to QA (Quality Assurance).

  A. Multi-Label Classification
  B. Multi-Class Classification
  C. Binary Classification
  D. Logistic Regression
  C. Binary Classification

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  Explanation/Reference:

  The Supervised Machine Learning algorithm can be broadly classified into Regression and Classification Algorithms. In Regression algorithms, we have predicted the output for continuous values, but to predict the categorical values, we need Classification algorithms. What is the Classification Algorithm? The Classification algorithm is a Supervised Learning technique that is used to identify the category of new observations on the basis of training data. In Classification, a program learns from the given dataset or observations and then classifies new observation into a number of classes or groups. Such as, Yes or No, 0 or 1, Spam or Not Spam, cat or dog, etc. Classes can be called as targets/labels or categories. Unlike regression, the output variable of Classification is a category, not a value, such as "Green or Blue", "fruit or animal", etc. Since the Classification algorithm is a Supervised learning technique, hence it takes labeled input data, which means it contains input with the corresponding output.

  In classification algorithm, a discrete output function(y) is mapped to input variable(x).

  y=f(x), where y = categorical output

  The best example of an ML classification algorithm is Email Spam Detector. The main goal of the Classification algorithm is to identify the category of a given dataset, and these algorithms are mainly used to predict the output for the

  categorical data. The algorithm which implements the classification on a dataset is known as a classifier.

  There are two types of Classifications:

  Binary Classifier: If the classification problem has only two possible outcomes, then it is called as Binary Classifier.

  Examples: YES or NO, MALE or FEMALE, SPAM or NOT SPAM, CAT or DOG, etc. Multi-class Classifier: If a classification problem has more than two outcomes, then it is called as Multi-class Classifier.

  Example: Classifications of types of crops, Classification of types of music. Binary classification in deep learning refers to the type of classification where we have two class labels - one normal and one abnormal. Some examples of binary

  classification use:

  To detect whether email is spam or not

  To determine whether or not a patient has a certain disease in medicine.

  To determine whether or not quality specifications were met when it comes to QA (Quality Assurance).

  For example, the normal class label would be that a patient has the disease, and the abnormal class label would be that they do not, or vice-versa. As is with every other type of classification, it is only as good as the binary classification

  dataset that it has ?or, in other words, the more training and data it has, the better it is.

• Question 65:

  Which of the following is a Python-based web application framework for visualizing data and analyzing results in a more efficient and flexible way?

  A. StreamBI
  B. Streamlit
  C. Streamsets
  D. Rapter
  B. Streamlit

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  Explanation/Reference:

  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.