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Snowflake DSA-C02 Exam Questions
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- Topic 1: Data Science Concepts: This portion of the test includes basic machine learning principles, problem types, the machine learning lifecycle, and statistical ideas that are crucial for data science workloads for analysts and data scientists. It guarantees that applicants comprehend data science theory inside the framework of Snowflake's platform.
- Topic 2: Data Pipelining: This domain focuses on creating efficient data science pipelines and enhancing data through data-sharing sources for data engineers and ETL specialists. It evaluates the capacity to establish reliable data flows throughout the ecosystem of Snowflake.
- Topic 3: Data Preparation and Feature Engineering: This section of the test includes data cleansing, exploratory data analysis, feature engineering, and data visualization using Snowflake for data analysts and machine learning developers. It evaluates proficiency in data preparation for model building and stakeholder presentation.
- Topic 4: Model Deployment: For MLOps engineers and data scientists, this domain covers the process of moving models into production, assessing model effectiveness, retraining models, and understanding model lifecycle management tools. It ensures candidates can operationalize machine learning models in a Snowflake-based production environment.
Free Snowflake DSA-C02 Exam Actual Questions
Note: Premium Questions for DSA-C02 were last updated On Aug. 22, 2026 (see below)
What is the formula for measuring skewness in a dataset?
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.
Which object records data manipulation language (DML) changes made to tables, including inserts, updates, and deletes, as well as metadata about each change, so that actions can be taken using the changed data of Data Science Pipelines?
A stream object records data manipulation language (DML) changes made to tables, including inserts, updates, and deletes, as well as metadata about each change, so that actions can be taken using the changed data. This process is referred to as change data capture (CDC). An individual table stream tracks the changes made to rows in a source table. A table stream (also referred to as simply a ''stream'') makes a ''change table'' available of what changed, at the row level, between two transactional points of time in a table. This allows querying and consuming a sequence of change records in a transactional fashion.
Streams can be created to query change data on the following objects:
* Standard tables, including shared tables.
* Views, including secure views
* Directory tables
* Event tables
Which of the Following is not type of Windows function in Snowflake?
Window Functions
A window function operates on a group (''window'') of related rows.
Each time a window function is called, it is passed a row (the current row in the window) and the window of rows that contain the current row. The window function returns one output row for each input row. The output depends on the individual row passed to the function and the values of the other rows in the window passed to the function.
Some window functions are order-sensitive. There are two main types of order-sensitive window functions:
Rank-related functions.
Window frame functions.
Rank-related functions list information based on the ''rank'' of a row. For example, if you rank stores in descending order by profit per year, the store with the most profit will be ranked 1; the second-most profitable store will be ranked 2, etc.
Window frame functions allow you to perform rolling operations, such as calculating a running total or a moving average, on a subset of the rows in the window.
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).
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.
Which of the learning methodology applies conditional probability of all the variables with respec-tive the dependent variable?
Supervised learning methodology applies conditional probability of all the variables with respective the dependent variable and generally conditional probability of variables is nothing but a basic method of estimating the statistics for few random experiments.
Conditional probability is thus the likelihood of an event or outcome occurring based on the occurrence of some other event or prior outcome. Two events are said to be independent if one event occurring does not affect the probability that the other event will occur.
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