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Snowflake DSA-C02 Exam Questions
- Snowflake SnowPro Certifications
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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 Feb. 23, 2026 (see below)
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.
Mark the incorrect statement regarding usage of Snowflake Stream & Tasks?
All are correct except a standard-only stream tracks row inserts only.
A standard (i.e. delta) stream tracks all DML changes to the source object, including inserts, up-dates, and deletes (including table truncates).
What Can Snowflake Data Scientist do in the Snowflake Marketplace as Provider?
All are correct!
About the Snowflake Marketplace
You can use the Snowflake Marketplace to discover and access third-party data and services, as well as market your own data products across the Snowflake Data Cloud.
As a data provider, you can use listings on the Snowflake Marketplace to share curated data offer-ings with many consumers simultaneously, rather than maintain sharing relationships with each indi-vidual consumer. With Paid Listings, you can also charge for your data products.
As a consumer, you might use the data provided on the Snowflake Marketplace to explore and ac-cess the following:
Historical data for research, forecasting, and machine learning.
Up-to-date streaming data, such as current weather and traffic conditions.
Specialized identity data for understanding subscribers and audience targets.
New insights from unexpected sources of data.
The Snowflake Marketplace is available globally to all non-VPS Snowflake accounts hosted on Amazon Web Services, Google Cloud Platform, and Microsoft Azure, with the exception of Mi-crosoft Azure Government. Support for Microsoft Azure Government is planned.
Which of the following metrics are used to evaluate classification models?
Evaluation metrics are tied to machine learning tasks. There are different metrics for the tasks of classification and regression. Some metrics, like precision-recall, are useful for multiple tasks. Classification and regression are examples of supervised learning, which constitutes a majority of machine learning applications. Using different metrics for performance evaluation, we should be able to im-prove our model's overall predictive power before we roll it out for production on unseen data. Without doing a proper evaluation of the Machine Learning model by using different evaluation metrics, and only depending on accuracy, can lead to a problem when the respective model is deployed on unseen data and may end in poor predictions.
Classification metrics are evaluation measures used to assess the performance of a classification model. Common metrics include accuracy (proportion of correct predictions), precision (true positives over total predicted positives), recall (true positives over total actual positives), F1 score (har-monic mean of precision and recall), and area under the receiver operating characteristic curve (AUC-ROC).
Confusion Matrix
Confusion Matrix is a performance measurement for the machine learning classification problems where the output can be two or more classes. It is a table with combinations of predicted and actual values.
It is extremely useful for measuring the Recall, Precision, Accuracy, and AUC-ROC curves.
The four commonly used metrics for evaluating classifier performance are:
1. Accuracy: The proportion of correct predictions out of the total predictions.
2. Precision: The proportion of true positive predictions out of the total positive predictions (precision = true positives / (true positives + false positives)).
3. Recall (Sensitivity or True Positive Rate): The proportion of true positive predictions out of the total actual positive instances (recall = true positives / (true positives + false negatives)).
4. F1 Score: The harmonic mean of precision and recall, providing a balance between the two metrics (F1 score = 2 * ((precision * recall) / (precision + recall))).
These metrics help assess the classifier's effectiveness in correctly classifying instances of different classes.
Understanding how well a machine learning model will perform on unseen data is the main purpose behind working with these evaluation metrics. Metrics like accuracy, precision, recall are good ways to evaluate classification models for balanced datasets, but if the data is imbalanced then other methods like ROC/AUC perform better in evaluating the model performance.
ROC curve isn't just a single number but it's a whole curve that provides nuanced details about the behavior of the classifier. It is also hard to quickly compare many ROC curves to each other.
What Can Snowflake Data Scientist do in the Snowflake Marketplace as Provider?
All are correct!
About the Snowflake Marketplace
You can use the Snowflake Marketplace to discover and access third-party data and services, as well as market your own data products across the Snowflake Data Cloud.
As a data provider, you can use listings on the Snowflake Marketplace to share curated data offer-ings with many consumers simultaneously, rather than maintain sharing relationships with each indi-vidual consumer. With Paid Listings, you can also charge for your data products.
As a consumer, you might use the data provided on the Snowflake Marketplace to explore and ac-cess the following:
Historical data for research, forecasting, and machine learning.
Up-to-date streaming data, such as current weather and traffic conditions.
Specialized identity data for understanding subscribers and audience targets.
New insights from unexpected sources of data.
The Snowflake Marketplace is available globally to all non-VPS Snowflake accounts hosted on Amazon Web Services, Google Cloud Platform, and Microsoft Azure, with the exception of Mi-crosoft Azure Government. Support for Microsoft Azure Government is planned.
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