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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 Mar. 21, 2025 (see below)
As Data Scientist looking out to use Reader account, Which ones are the correct considerations about Reader Accounts for Third-Party Access?
Data sharing is only supported between Snowflake accounts. As a data provider, you might want to share data with a consumer who does not already have a Snowflake account or is not ready to be-come a licensed Snowflake customer.
To facilitate sharing data with these consumers, you can create reader accounts. Reader accounts (formerly known as ''read-only accounts'') provide a quick, easy, and cost-effective way to share data without requiring the consumer to become a Snowflake customer.
Each reader account belongs to the provider account that created it. As a provider, you use shares to share databases with reader accounts; however, a reader account can only consume data from the provider account that created it.
So, Data Sharing is possible between Snowflake & Non-snowflake accounts via Reader Account.
Mark the correct steps for saving the contents of a DataFrame to a Snowflake table as part of Moving Data from Spark to Snowflake?
Moving Data from Spark to Snowflake
The steps for saving the contents of a DataFrame to a Snowflake table are similar to writing from Snowflake to Spark:
1. Use the write() method of the DataFrame to construct a DataFrameWriter.
2. Specify SNOWFLAKE_SOURCE_NAME using the format() method.
3. Specify the connector options using either the option() or options() method.
4. Use the dbtable option to specify the table to which data is written.
5. Use the mode() method to specify the save mode for the content.
Examples
1. df.write
2. .format(SNOWFLAKE_SOURCE_NAME)
3. .options(sfOptions)
4. .option('dbtable', 't2')
5. .mode(SaveMode.Overwrite)
6. .save()
All Snowpark ML modeling and preprocessing classes are in the ________ namespace?
All Snowpark ML modeling and preprocessing classes are in the snowflake.ml.modeling namespace. The Snowpark ML modules have the same name as the corresponding module from the sklearn namespace. For example, the Snowpark ML module corresponding to sklearn.calibration is snow-flake.ml.modeling.calibration.
The xgboost and lightgbm modules correspond to snowflake.ml.modeling.xgboost and snow-flake.ml.modeling.lightgbm, respectively.
Not all of the classes from scikit-learn are supported in Snowpark ML.
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).
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.
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