[Q44-Q66] DSA-C03 100% Guarantee Download DSA-C03 Exam PDF Q&A [Jan 26, 2026]

Q44. A Snowflake table named ‘SALES DATA contains a ‘TRANSACTION DATE column stored as VARCHAR. The data in this column is inconsistent; some rows have dates in ‘YYYY-MM-DD’ format, others in ‘MM/DD/YYYY’ format, and some contain invalid date strings like ‘N/A’. You need to standardize all dates to ‘YYYY-MM-DD’ format and store them in a new column called FORMATTED DATE in a new table ‘STANDARDIZED_SALES DATA. Which of the following approaches, using Snowpark Python and SQL, most effectively handles these inconsistencies and minimizes errors during data transformation? Select all that apply:

Q45. You are troubleshooting an external function in Snowflake that calls a model hosted on Google Cloud A1 Platform. The external function consistently returns ‘SQL compilation error: External function error: HTTP 400 Bad Request’. You have verified the API integration is correctly configured, and the Google Cloud project has the necessary permissions. Which of the following is the most likely cause of this error, and how would you best diagnose it?

Q46. You’re working on a fraud detection system for an e-commerce platform. You have a table ‘TRANSACTIONS with a ‘TRANSACTION AMOUNT column. You want to bin the transaction amounts into several risk categories (‘Low’, ‘Medium’, ‘High’, ‘Very High’) using explicit boundaries. You want the bins to be inclusive of the lower boundary and exclusive of the upper boundary (e.g., [0, 100), [100, 500), etc.). Which of the following SQL statements using the ‘WIDTH BUCKET function correctly bins the transaction amounts into these categories, assuming these boundaries: 0, 100, 500, 1000, and infinity, and assigns appropriate labels?

Q47. You are tasked with predicting sales (SALES AMOUNT’) for a retail company using linear regression in Snowflake. The dataset includes features like ‘ADVERTISING SPEND’, ‘PROMOTIONS’, ‘SEASONALITY INDEX’, and ‘COMPETITOR PRICE’. After training a linear regression model named ‘sales model’, you observe that the model performs poorly on new data, indicating potential issues with multicollinearity or overfitting. Which of the following strategies, applied directly within Snowflake, would be MOST effective in addressing these issues and improving the model’s generalization performance? Choose ALL that apply.

Q48. You are tasked with deploying a fraud detection model in Snowflake using the Model Registry. The model is trained on a dataset that is updated daily. You need to ensure that your deployed model uses the latest approved version and that you can easily roll back to a previous version if any issues arise. Which of the following approaches would provide the most robust and maintainable solution for model versioning and deployment, considering minimal downtime during updates and rollback?

Q49. You are working with a dataset of customer transaction logs stored in Snowflake. Due to legal restrictions, you are unable to directly access or analyze the entire dataset. However, you can query aggregate statistics. You need to estimate the standard error of the mean transaction amount using bootstrapping. Knowing that you cannot retrieve the individual transaction amounts directly, which of the following approaches, while technically feasible within Snowflake and its stored procedure capabilities, is the least appropriate and potentially misleading application of bootstrapping?

Q50. A team is using Snowflake to build a supervised machine learning model for image classification. The images are stored in a Snowflake table, and the labels are in a separate table. The goal is to train a model using Snowpark Python. Which of the following code snippets represents the MOST efficient way to join the image data with its corresponding labels, pre-process the images (resize and normalize), and prepare the data for model training using Snowpark DataFrame transformations? Assume contains image data as binary, ‘label df contains the image labels, and ‘resize normalize udf’ is a UDF that handles resizing and normalization.

Q51. You are analyzing sales data in Snowflake using Snowpark to identify seasonality. You have a table named ‘SALES DATA with columns ‘SALE DATE (TIMESTAMP NTZ) and ‘AMOUNT (NUMBER). You want to calculate the rolling average sales for each week over a period of 12 weeks using a Snowpark DataFrame. Which of the following Snowpark code snippets correctly implements this calculation?

Q52. You are using Snowflake Cortex to analyze customer reviews. You have created a vector embedding for each review using a UDF that calls a remote LLM inference endpoint. Now you need to perform a similarity search to identify reviews that are similar to a given query review. Which of the following SQL queries leveraging vector functions in Snowflake is the MOST efficient and appropriate way to achieve this, assuming the ‘REVIEW EMBEDDINGS’ table has columns ‘review_id’ and ’embedding’ (a VECTOR column) and query_embedding’ is a pre-computed vector embedding?

Q53. You are using Snowpark for Python to perform feature engineering on a large dataset stored in a Snowflake table named ‘transactions’. You need to create a new feature called ‘transaction_size category’ based on the ‘transaction_amount’ column. The categories are defined as follows: Small (amount < 10), Medium (10 <= amount < 100), and Large (amount 100). You want to optimize performance by leveraging Snowflake’s parallel processing capabilities. Which of the following Snowpark for Python code snippets is the MOST efficient and Pythonic way to achieve this?

Q54. You’re developing a fraud detection system in Snowflake. You’re using Snowflake Cortex to generate embeddings from transaction descriptions, aiming to cluster similar fraudulent transactions. Which of the following approaches are MOST effective for optimizing the performance and cost of generating embeddings for a large dataset of millions of transaction descriptions using Snowflake Cortex, especially considering the potential cost implications of generating embeddings at scale? Select two options.

Q55. You have a dataset in Snowflake containing customer reviews. One of the columns, ‘review_text’, contains free-text customer feedback. You want to perform sentiment analysis on these reviews and include the sentiment score as a feature in your machine learning model. Furthermore, you wish to categorize the sentiment into ‘Positive’, ‘Negative’, and ‘Neutral’. Given the need for scalability and efficiency within Snowflake, which methods could be employed?

Q56. You are tasked with optimizing the hyperparameter tuning process for a complex deep learning model within Snowflake using Snowpark Python. The model is trained on a large dataset stored in Snowflake, and you need to efficiently explore a wide range of hyperparameter values to achieve optimal performance. Which of the following approaches would provide the MOST scalable and performant solution for hyperparameter tuning in this scenario, considering the constraints and capabilities of Snowflake?

Q57. You are building a model training pipeline in Snowflake using Snowpark Python. You want to leverage a pre-trained model from Hugging Face Transformers for a text classification task, fine-tuning it with your own labeled data stored in a Snowflake table named ‘training_data’. You’ve chosen the ‘transformers’ library and plan to use a ‘transformers.pipeline’ for inference. Which of the following code snippets, when integrated into your Snowpark Python application, will correctly download the pre trained model and tokenizer, prepare the data, perform fine-tuning, and then save the fine-tuned model to a Snowflake stage?

Q58. You are analyzing customer transaction data in Snowflake to identify fraudulent activities. The ‘TRANSACTION AMOUNT’ column exhibits a right-skewed distribution. Which of the following Snowflake queries is MOST effective in identifying outliers based on the Interquartile Range (IQR) method, specifically targeting unusually large transaction amounts? Assume IQR is already calculated as variable and QI as and Q3 as in snowflake session.

Q59. You have a Snowflake Model Registry set up and are managing multiple versions of a machine learning model. You want to programmatically retrieve a specific version of the model and load it for inference within a Snowflake Snowpark Python UDE Assume your registry name is ‘my_registry’, the model name is ‘credit risk_model’, and you want to retrieve version ‘v2’. How would you achieve this using Snowpark Python?

Q60. You have trained a complex Random Forest model in Snowflake to predict loan default risk. You wish to understand the individual and combined effects of ‘credit_score’ and ‘debt_to_income_ratio’ on the predicted probability of default. Which approach is MOST suitable for visualizing and interpreting these relationships?

Q61. You are tasked with forecasting the daily sales of a specific product for the next 30 days using Snowflake. You have historical sales data for the past 3 years, stored in a Snowflake table named ‘SALES DATA’, with columns ‘SALE DATE (DATE type) and ‘SALES AMOUNT’ (NUMBER type). You want to use the Prophet library within a Snowflake User-Defined Function (UDF) for forecasting. The Prophet model requires the input data to have columns named ‘ds’ (for dates) and ‘y’ (for values). Which of the following code snippets demonstrates the CORRECT way to prepare and pass your data to the Prophet UDF in Snowflake, assuming you’ve already created the Python UDF ‘prophet_forecast’?

Q62. You are developing a churn prediction model and want to track its performance across different model versions using the Snowflake Model Registry. After registering a new model version, you need to log evaluation metrics (e.g., AUC, F 1-score) and custom tags associated with the training run. Assuming you have a registered model named ‘churn_model’ with version ‘v2’, which of the following code snippets demonstrates the correct way to log these metrics and tags using the Snowflake Python Connector and the ‘ModelRegistry’ API?

Q63. You are using Snowpark to build a collaborative filtering model for product recommendations. You have a table ‘USER_ITEM INTERACTIONS with columns ‘USER ID’, ‘ITEM ID’, and ‘INTERACTION TYPE’. You want to create a sparse matrix representation of this data using Snowpark, suitable for input into a matrix factorization algorithm. Which of the following code snippets best achieves this while efficiently handling large datasets within Snowflake?

Q64. You are tasked with validating a regression model predicting customer lifetime value (CLTV). The model uses various customer attributes, including purchase history, demographics, and website activity, stored in a Snowflake table called ‘CUSTOMER DATA. You want to assess the model’s calibration specifically, whether the predicted CLTV values align with the actual observed CLTV values over time. Which of the following evaluation techniques would be MOST suitable for assessing the calibration of your CLTV regression model in Snowflake?

Q65. You have developed a customer churn prediction model using Python and deployed it as a Snowflake UDE You are monitoring its performance and notice a significant drop in accuracy over time. To address this, you need to implement automated model retraining with regular validation. Which of the following steps and validation techniques are MOST critical for ensuring the retrained model is effective and avoids overfitting to recent data? (Select THREE)

Q66. You have trained a logistic regression model in Python using scikit-learn and plan to deploy it as a Python stored procedure in Snowflake. You need to serialize the model for deployment. Consider the following code snippet: