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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: Apr. 18, 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

Ashlyn

12 days ago
Data sampling techniques were covered. Know stratified sampling, random sampling, and when to apply each for model training.
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Catrice

28 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

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

1 months 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

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

2 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

3 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

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

3 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

4 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

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

4 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

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

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

5 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

5 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

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

6 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

6 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

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

7 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

7 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.
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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

8 months ago
Just passed the Snowflake SnowPro Advanced: Data Scientist exam! Thanks Pass4Success for the spot-on practice questions.
upvoted 0 times
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Hortencia

10 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

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

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

11 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.
upvoted 0 times
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Glenn

12 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 Apr. 18, 2025 (see below)

Question #1

Which one is not the feature engineering techniques used in ML data science world?

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Correct Answer: D

Feature engineering is the pre-processing step of machine learning, which is used to transform raw data into features that can be used for creating a predictive model using Machine learning or statistical Modelling.

What is a feature?

Generally, all machine learning algorithms take input data to generate the output. The input data re-mains in a tabular form consisting of rows (instances or observations) and columns (variable or at-tributes), and these attributes are often known as features. For example, an image is an instance in computer vision, but a line in the image could be the feature. Similarly, in NLP, a document can be an observation, and the word count could be the feature. So, we can say a feature is an attribute that impacts a problem or is useful for the problem.

What is Feature Engineering?

Feature engineering is the pre-processing step of machine learning, which extracts features from raw data. It helps to represent an underlying problem to predictive models in a better way, which as a result, improve the accuracy of the model for unseen data. The predictive model contains predictor variables and an outcome variable, and while the feature engineering process selects the most useful predictor variables for the model.

Some of the popular feature engineering techniques include:

1. Imputation

Feature engineering deals with inappropriate data, missing values, human interruption, general errors, insufficient data sources, etc. Missing values within the dataset highly affect the performance of the algorithm, and to deal with them 'Imputation' technique is used. Imputation is responsible for handling irregularities within the dataset.

For example, removing the missing values from the complete row or complete column by a huge percentage of missing values. But at the same time, to maintain the data size, it is required to impute the missing data, which can be done as:

For numerical data imputation, a default value can be imputed in a column, and missing values can be filled with means or medians of the columns.

For categorical data imputation, missing values can be interchanged with the maximum occurred value in a column.

2. Handling Outliers

Outliers are the deviated values or data points that are observed too away from other data points in such a way that they badly affect the performance of the model. Outliers can be handled with this feature engineering technique. This technique first identifies the outliers and then remove them out.

Standard deviation can be used to identify the outliers. For example, each value within a space has a definite to an average distance, but if a value is greater distant than a certain value, it can be considered as an outlier. Z-score can also be used to detect outliers.

3. Log transform

Logarithm transformation or log transform is one of the commonly used mathematical techniques in machine learning. Log transform helps in handling the skewed data, and it makes the distribution more approximate to normal after transformation. It also reduces the effects of outliers on the data, as because of the normalization of magnitude differences, a model becomes much robust.

4. Binning

In machine learning, overfitting is one of the main issues that degrade the performance of the model and which occurs due to a greater number of parameters and noisy data. However, one of the popular techniques of feature engineering, 'binning', can be used to normalize the noisy data. This process involves segmenting different features into bins.

5. Feature Split

As the name suggests, feature split is the process of splitting features intimately into two or more parts and performing to make new features. This technique helps the algorithms to better understand and learn the patterns in the dataset.

The feature splitting process enables the new features to be clustered and binned, which results in extracting useful information and improving the performance of the data models.

6. One hot encoding

One hot encoding is the popular encoding technique in machine learning. It is a technique that converts the categorical data in a form so that they can be easily understood by machine learning algorithms and hence can make a good prediction. It enables group the of categorical data without losing any information.


Question #2

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

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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 #3

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

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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 #4

Which ones are the type of visualization used for Data exploration in Data Science?

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Correct Answer: A, D, E

Type of visualization used for exploration:

* Correlation heatmap

* Class distributions by feature

* Two-Dimensional density plots.

All the visualizations are interactive, as is standard for Plotly.

For More details, please refer the below link:

https://towardsdatascience.com/data-exploration-understanding-and-visualization-72657f5eac41


Question #5

Which command manually triggers a single run of a scheduled task (either a standalone task or the root task in a DAG) independent of the schedule defined for the task?

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Correct Answer: C

The EXECUTE TASK command manually triggers a single run of a scheduled task (either a standalone task or the root task in a DAG) independent of the schedule defined for the task. A successful run of a root task triggers a cascading run of child tasks in the DAG as their precedent task completes, as though the root task had run on its defined schedule.

This SQL command is useful for testing new or modified standalone tasks and DAGs before you enable them to execute SQL code in production.

Call this SQL command directly in scripts or in stored procedures. In addition, this command sup-ports integrating tasks in external data pipelines. Any third-party services that can authenticate into your Snowflake account and authorize SQL actions can execute the EXECUTE TASK command to run tasks.



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