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Snowflake DSA-C02 Exam Questions

Exam Name: SnowPro Advanced: Data Scientist Certification Exam
Exam Code: DSA-C02
Related Certification(s):
  • Snowflake SnowPro Certifications
  • Snowflake SnowPro Advanced Certifications
Certification Provider: Snowflake
Number of DSA-C02 practice questions in our database: 65 (updated: Mar. 21, 2025)
Expected DSA-C02 Exam Topics, as suggested by Snowflake :
  • 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.
Disscuss Snowflake DSA-C02 Topics, Questions or Ask Anything Related

Catrice

4 days ago
Ensemble methods were tested in-depth. Understand Random Forests, Gradient Boosting, and how they compare to single decision trees.
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Lucia

5 days ago
Passed on my first try! Pass4Success's exam questions were spot-on. Highly recommend!
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Kirk

18 days ago
Model evaluation metrics were a key focus. Know when to use accuracy, precision, recall, F1-score, and ROC AUC for different problem types.
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Emilio

1 months ago
Snowflake's external functions came up. Practice integrating with cloud services for extended data science capabilities.
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Leonie

1 months ago
Just became a certified Snowflake Data Scientist! Pass4Success made studying efficient and effective.
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Dierdre

2 months ago
The exam had questions on anomaly detection techniques. Study both statistical and machine learning approaches for identifying outliers.
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Chanel

2 months ago
Data visualization best practices were tested. Understand which chart types are best for different data distributions and analysis goals.
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Brandee

2 months ago
Aced the Snowflake certification! Pass4Success's practice questions were incredibly helpful.
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Keva

2 months ago
Happy to share that I passed the Snowflake SnowPro Advanced: Data Scientist Certification Exam. Thanks to Pass4Success practice questions, I felt confident. A challenging question was related to Data Pipelining. It asked about the differences between data lakes and data warehouses and their respective use cases. I was a bit unsure about the specifics but managed to get through.
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Melodie

3 months ago
Encountered questions on A/B testing methodologies. Know how to design experiments and interpret results. Pass4Success materials were spot-on for this!
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Cherelle

3 months ago
Snowflake's integration with machine learning frameworks was a hot topic. Study how to use Snowpark for model training and deployment.
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Juan

3 months ago
Whew! Made it through the Snowflake exam. Couldn't have done it without Pass4Success's help.
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Jordan

3 months ago
I am pleased to announce that I passed the Snowflake SnowPro Advanced: Data Scientist Certification Exam. The Pass4Success practice questions were very useful. One question that I found difficult was about Data Science Concepts. It asked about the bias-variance tradeoff and how it affects model performance. I had to think carefully about the implications of each.
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Mirta

4 months ago
Dimension reduction techniques like PCA were tested. Understand when and how to apply these methods to high-dimensional datasets.
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Margurite

4 months ago
Just passed the Snowflake SnowPro Advanced: Data Scientist Certification Exam! Pass4Success practice questions were a big help. There was a question on Model Deployment that asked about the differences between batch and real-time deployment. I was unsure about the specific use cases for each but still managed to pass.
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Curt

4 months ago
Natural Language Processing questions were tricky. Focus on text preprocessing steps and basic sentiment analysis techniques.
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Boris

4 months ago
Passed the SnowPro Advanced: Data Scientist exam! Pass4Success's materials were invaluable.
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Katie

4 months ago
I passed the Snowflake SnowPro Advanced: Data Scientist Certification Exam, and the Pass4Success practice questions were instrumental in my success. A question that puzzled me was related to Data Preparation and Feature Engineering. It asked about handling missing data and which imputation method is best for categorical variables. I had to guess between mode imputation and using a placeholder.
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Clement

4 months ago
The exam included questions on clustering algorithms. Know the differences between K-means, DBSCAN, and hierarchical clustering. Thanks to Pass4Success for covering these topics!
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Dick

5 months ago
Excited to share that I passed the Snowflake SnowPro Advanced: Data Scientist Certification Exam with the help of Pass4Success practice questions. One question that caught me off guard was about Model Development. It asked about the different types of cross-validation techniques and when to use each. I wasn't entirely sure about the k-fold vs. stratified k-fold.
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Meghann

5 months ago
Tough exam, but Pass4Success's questions were key to my success. Grateful for the quick prep!
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Renea

5 months ago
Snowflake's support for Python UDFs came up multiple times. Practice writing and optimizing UDFs for data preprocessing tasks.
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Ariel

5 months ago
I am thrilled to announce that I passed the Snowflake SnowPro Advanced: Data Scientist Certification Exam. The Pass4Success practice questions were a great help. There was a question on Data Pipelining that asked about the ETL process and the best tools to use for each stage. I was a bit confused about the Extract stage tools but still managed to get through.
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Carlee

6 months ago
Time series forecasting was a key topic. Study ARIMA models and seasonality decomposition. The exam tests your ability to interpret results, not just implement models.
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Jackie

6 months ago
Happy to share that I passed the Snowflake SnowPro Advanced: Data Scientist Certification Exam. Thanks to Pass4Success practice questions, I felt well-prepared. One challenging question was related to Data Science Concepts, specifically about the difference between supervised and unsupervised learning. It asked for an example of each, and I had to think hard about the best examples to provide.
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Johna

6 months ago
Nailed the Snowflake certification! Pass4Success made prep a breeze with their relevant materials.
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Scarlet

6 months ago
Encountered several questions on feature engineering techniques. Brush up on encoding methods and scaling algorithms. Pass4Success practice tests really helped me prepare!
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Gladis

6 months ago
Just cleared the Snowflake SnowPro Advanced: Data Scientist Certification Exam! The Pass4Success practice questions were a lifesaver. There was a tricky question on Model Deployment that asked about the steps to deploy a model using Snowflake's Snowpark. I wasn't entirely sure about the sequence of steps, but I managed to pass the exam.
upvoted 0 times
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Olene

7 months ago
Just passed the Snowflake SnowPro Advanced: Data Scientist exam! The questions on statistical analysis were challenging. Make sure you understand hypothesis testing and p-values thoroughly.
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Leslie

7 months ago
I recently passed the Snowflake SnowPro Advanced: Data Scientist Certification Exam, and I must say, the Pass4Success practice questions were incredibly helpful. One question that stumped me was about the best practices for feature scaling in Data Preparation and Feature Engineering. It asked which scaling method is most suitable for a dataset with outliers, and I was unsure whether to choose Min-Max Scaling or Robust Scaler.
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Beatriz

7 months ago
Just passed the Snowflake SnowPro Advanced: Data Scientist exam! Thanks Pass4Success for the spot-on practice questions.
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Hortencia

9 months ago
Whew, passed the Snowflake exam! Pass4Success's materials were crucial for my quick preparation. Thanks!
upvoted 0 times
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Junita

10 months ago
SnowPro Advanced: Data Scientist certified! Pass4Success, your exam prep was invaluable. Thank you!
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Talia

10 months ago
Success! SnowPro Advanced: Data Scientist exam conquered. Pass4Success, your questions were key. Appreciate it!
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Abraham

10 months ago
Passed the SnowPro Advanced: Data Scientist exam! Pass4Success's questions were spot-on. Thanks for the quick prep!
upvoted 0 times
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Aron

11 months ago
Time series analysis was another important area. Questions may involve forecasting techniques and handling seasonal data. Brush up on concepts like ARIMA models and how to implement them in Snowflake. Pass4Success's exam materials were spot-on and significantly contributed to my success in passing the certification.
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Glenn

11 months ago
Challenging exam, but I made it! Grateful for Pass4Success's relevant practice questions. Time-saver!
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Free Snowflake DSA-C02 Exam Actual Questions

Note: Premium Questions for DSA-C02 were last updated On Mar. 21, 2025 (see below)

Question #1

As Data Scientist looking out to use Reader account, Which ones are the correct considerations about Reader Accounts for Third-Party Access?

Reveal Solution Hide Solution
Correct Answer: D

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.


Question #2

Mark the correct steps for saving the contents of a DataFrame to a Snowflake table as part of Moving Data from Spark to Snowflake?

Reveal Solution Hide Solution
Correct Answer: C

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()


Question #3

All Snowpark ML modeling and preprocessing classes are in the ________ namespace?

Reveal Solution Hide Solution
Correct Answer: D

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.


Question #4

Mark the incorrect statement regarding usage of Snowflake Stream & Tasks?

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Correct Answer: D

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).


Question #5

Which of the following metrics are used to evaluate classification models?

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Correct Answer: D

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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