DSA-C03 exam dumps

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  • Exam Code: DSA-C03
  • Exam Name: SnowPro Advanced: Data Scientist Certification Exam
  • No. of Questions: 289 Questions and Answers
  • Updated: Oct 09, 2026

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Snowflake DSA-C03 Exam Syllabus Topics:

SectionObjectives
Topic 1: Data Science Fundamentals in Snowflake- Data preprocessing and transformation in Snowflake
- Applied statistics and data exploration
Topic 2: Machine Learning with Snowpark- Using Snowpark for Python-based ML workflows
- Model training and evaluation workflows
Topic 3: Model Deployment and Operationalization- Model deployment in Snowflake ecosystem
- Monitoring and lifecycle management
Topic 4: Data Engineering for Machine Learning- SQL-based feature engineering
- Data pipelines using Snowflake
Topic 5: Advanced Analytics and Optimization- Scalable analytics design patterns
- Performance optimization of data queries

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Recommended: SnowPro Core Certification or equivalent Snowflake experience

Vendor requirements do change, so before you commit to an exam date, confirm the latest eligibility rules on the official Snowflake exam page.

The Snowflake SnowPro Advanced: Data Scientist Certification syllabus is split into 5 domains, anchored by Machine Learning with Snowpark, Data Science Fundamentals in Snowflake, and Model Deployment and Operationalization. The full breakdown sits in the syllabus section above — treat it as the checklist that decides where your study hours go.

The DSA-C03 exam, officially the SnowPro Advanced: Data Scientist Certification Exam, is Snowflake's benchmark for proving you can work with its technologies in professional settings; passing it awards the SnowPro Advanced: Data Scientist certification, which sits at the Professional level. Because the credential is issued by the vendor itself, it carries weight with employers that self-assessed skills simply cannot match. It also opens the road toward SnowPro Core Certification.

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Snowflake SnowPro Advanced: Data Scientist Certification Sample Questions:

You are using Snowflake ML to train a binary classification model. After training, you need to evaluate the model's performance. Which of the following metrics are most appropriate to evaluate your trained model, and how do they differ in their interpretation, especially when dealing with imbalanced datasets?

  • A. Mean Squared Error (MSE): The average squared difference between the predicted and actual values. R-squared: Represents the proportion of variance in the dependent variable that is predictable from the independent variables. These are great for regression tasks.
  • B. AUC-ROC: Measures the ability of the model to distinguish between classes. It is less sensitive to class imbalance than accuracy. Log Loss: Measures the performance of a classification model where the prediction input is a probability value between 0 and 1.
  • C. Confusion Matrix: A table that describes the performance of a classification model by showing the counts of true positive, true negative, false positive, and false negative predictions. This isnt a metric but representation of the metrics.
  • D. Accuracy: It measures the overall correctness of the model. Precision: It measures the proportion of positive identifications that were actually correct. Recall: It measures the proportion of actual positives that were identified correctly. Fl-score: It is the harmonic mean of precision and recall.
  • E. Precision, Recall, F I-score, AUC-ROC, and Log Loss: Precision focuses on the accuracy of positive predictions; Recall focuses on the completeness of positive predictions; Fl-score balances Precision and Recall; AUC-ROC evaluates the separability of classes and Log Loss quantifies the accuracy of probabilities, especially valuable for imbalanced datasets because they provide a more nuanced view of performance than accuracy alone.
Reveal Solution  Discussion  0

Correct Answer: E  🗳️

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A data scientist is exploring customer purchase data in Snowflake to identify high-value customer segments. They have a table named 'CUSTOMER TRANSACTIONS with columns 'CUSTOMER ID', 'TRANSACTION_DATE', and 'PURCHASE_AMOUNT'. They want to calculate the interquartile range (IQR) of 'PURCHASE AMOUNT for each customer. Which SQL query using Snowsight is the most efficient and accurate way to calculate and display the IQR for each 'CUSTOMER ID?

  • A. Option A
  • B. Option B
  • C. Option E
  • D. Option C
  • E. Option D
Reveal Solution  Discussion  0

Correct Answer: C  🗳️

Explanation: Only visible for Actual4Labs members. You can sign-up / login (it's free).

You've built a machine learning model in scikit-learn and want to deploy it to Snowflake for real-time inference. You have the following options for deploying the model. Select all that apply and are considered a best practice for cost and time optimization:

  • A. Package the scikit-learn model using 'joblib' or 'pickle' , store it in a Snowflake stage, and create a Snowflake UDF (User-Defined Function) in Python to load the model from the stage and perform inference.
  • B. Implement a custom microservice that reads data from Snowflake, performs inference using the scikit-learn model, and writes the predictions back to Snowflake.
  • C. Use Snowflake's Snowpark Python API to directly load the model from a stage and execute inference using Snowpark DataFrames, which will implicitly handle the distributed processing of the data.
  • D. Create a Snowflake external function that calls a cloud-based (AWS SageMaker, Azure Machine Learning, GCP Vertex A1) endpoint for inference, passing the input data to the endpoint and receiving the prediction back.
  • E. Migrate your entire Snowflake data warehouse to a different platform which better supports real-time ML inference.
Reveal Solution  Discussion  0

Correct Answer: A,C  🗳️

Explanation: Only visible for Actual4Labs members. You can sign-up / login (it's free).

You're working with a Snowflake stage named that contains several versions of your machine learning model, named 'model_vl .pkl' , 'model_v2.pkl' , and You want to programmatically list all files in the stage and retrieve the creation time of the latest version (i.e., using SnowSQL. Which of the following approaches is most efficient and correct?

  • A. Option A
  • B. Option B
  • C. Option E
  • D. Option C
  • E. Option D
Reveal Solution  Discussion  0

Correct Answer: D  🗳️

Explanation: Only visible for Actual4Labs members. You can sign-up / login (it's free).

You are building a fraud detection model in Snowflake using Snowpark Python. You want to evaluate the model's performance, particularly focusing on identifying instances of fraud (minority class). Which combination of metrics provides the most comprehensive assessment for this imbalanced classification problem within the Snowflake environment, considering the need to minimize both false positives (legitimate transactions flagged as fraudulent) and false negatives (fraudulent transactions missed)?

  • A. Accuracy and ROC AUC.
  • B. ROC AUC and Recall.
  • C. Precision, Recall, and Fl-score.
  • D. Precision and Fl-score.
  • E. Accuracy and Recall.
Reveal Solution  Discussion  0

Correct Answer: C  🗳️

Explanation: Only visible for Actual4Labs members. You can sign-up / login (it's free).

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