2025 HIGH DSA-C03 QUALITY | EFFICIENT 100% FREE RELIABLE DSA-C03 TEST TOPICS

2025 High DSA-C03 Quality | Efficient 100% Free Reliable DSA-C03 Test Topics

2025 High DSA-C03 Quality | Efficient 100% Free Reliable DSA-C03 Test Topics

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Snowflake SnowPro Advanced: Data Scientist Certification Exam Sample Questions (Q237-Q242):

NEW QUESTION # 237
You are tasked with deploying a time series forecasting model within Snowflake using Snowpark Python. The model requires significant pre-processing and feature engineering steps that are computationally intensive. These steps include calculating rolling statistics, handling missing values with imputation, and applying various transformations. You aim to optimize the execution time of these pre- processing steps within the Snowpark environment. Which of the following techniques can significantly improve the performance of your data preparation pipeline?

  • A. Write the feature engineering logic directly in SQL and create a view. Use the Snowpark DataFrame API to query the view, avoiding Python code execution within Snowpark.
  • B. Ensure that all data used is small enough to fit within the memory of the client machine running the Snowpark Python script, thus removing the need for distributed computing.
  • C. Utilize Snowpark's vectorized UDFs and DataFrame operations to leverage Snowflake's distributed computing capabilities.
  • D. Force single-threaded execution by setting to avoid overhead associated with parallel processing.
  • E. Convert the Snowpark DataFrame to a Pandas DataFrame using and perform all pre-processing operations using Pandas functions before loading the processed data back to Snowflake.

Answer: A,C

Explanation:
Vectorized UDFs and SQL Views are the key to optimizing data pre-processing. Options B and E are correct. B - Utilize Snowpark's vectorized UDFs and DataFrame operations: Snowpark is designed to push computation down to Snowflake's distributed compute engine. Vectorized UDFs allow you to execute Python code in a parallel and efficient manner directly within Snowflake. E - SQL View: Snowpark DataFrame API can query the view from SQL directly. Writing the data preparation logic in SQL leverages the snowflake's engine more effectively than Pandas or Python on a client machine. Options A, C, and D are generally incorrect: Option A is incorrect as it defeats the purpose of using Snowpark. Parallel execution is generally much faster. Option C is incorrect as moving data outside of snowflake is costly. Option D is incorrect. Snowpark is designed to manage a large scale of data.


NEW QUESTION # 238
You are building a fraud detection model using Snowflake data'. The dataset 'TRANSACTIONS' contains billions of records and is partitioned by 'TRANSACTION DATE'. You want to use cross-validation to evaluate your model's performance on different subsets of the data and ensure temporal separation of training and validation sets. Given the following Snowflake table structure:

Which approach would be MOST appropriate for implementing time-based cross-validation within Snowflake to avoid data leakage and ensure robust model evaluation? (Assume using Snowpark Python to develop)

  • A. Create a UDF that assigns each row to a fold based on the 'TRANSACTION DATE column using a modulo operation. This is then passed to the 'cross_validation' function in Snowpark ML.
  • B. Implement a custom splitting function within Snowpark, creating sequential folds based on the 'TRANSACTION DATE column and use that with Snowpark ML's cross_validation. Ensure each fold represents a distinct time window without overlap.
  • C. Explicitly define training and validation sets based on date ranges within the Snowpark Python environment, performing iterative training and evaluation within the client environment before deploying a model to Snowflake. No built-in cross-validation used
  • D. Use 'SNOWFLAKE.ML.MODEL REGISTRY.CREATE MODEL' with default settings, which automatically handles temporal partitioning based on the insertion timestamp of the data.
  • E. Utilize the 'SNOWFLAKE.ML.MODEL REGISTRY.CREATE MODEL' with the 'input_colS argument containing 'TRANSACTION DATE'. Snowflake will automatically infer the temporal nature of the data and perform time-based cross-validation.

Answer: B

Explanation:
Option E is the most suitable because it explicitly addresses the temporal dependency and prevents data leakage by creating sequential, non-overlapping folds based on 'TRANSACTION DATE. Options A and D rely on potentially incorrect assumptions by Snowflake about time series data and are unlikely to provide the correct cross-validation folds. Option B can introduce leakage because it treats dates as categorical variables and performs random assignment. Option C performs the cross validation entirely outside of Snowflake, which negates the benefits of Snowflake's scalability and data proximity.


NEW QUESTION # 239
You are tasked with performing exploratory data analysis on a table named containing daily sales transactions. The table includes columns like 'transaction_date', 'product_id', 'quantity' , and 'price'. Your goal is to identify potential data quality issues and understand the distribution of sales. Which of the following SQL queries using Snowflake's statistical functions and features would be MOST effective for quickly identifying outliers in the 'quantity' column, potential data skewness, and missing values?

  • A. Option B
  • B. Option E
  • C. Option C
  • D. Option A
  • E. Option D

Answer: A,C

Explanation:
Options B and C are the most effective. Option B provides the total record count, the count of non-null quantity values (helping identify missing data), an approximate median, and the approximate distinct count of product IDs. This gives a good overview of data completeness and product diversity. Option C provides the min, max, variance and skew which together help identify possible outliers and understand the data distribution.


NEW QUESTION # 240
You are training a fraud detection model on a dataset containing millions of transactions. To ensure robust generalization, you've decided to implement a train-validation-holdout split using Snowflake's capabilities. Given the following requirements: Temporal Split: The dataset contains a 'transaction date' column. You want to ensure that the validation and holdout sets contain transactions after the training data'. This is crucial because fraud patterns evolve over time. Stratified Sampling (Within Training): The training set should maintain the original proportion of fraudulent vs. non-fraudulent transactions. The column indicates if a transaction is fraudulent (1) or not (0). Deterministic Splits: You need a repeatable process to ensure consistency across model iterations. Which of the following SQL code snippets best achieves these requirements, considering performance and best practices within Snowflake?

  • A. Option E
  • B. Option C
  • C. Option A
  • D. Option B
  • E. Option D

Answer: A

Explanation:
Option E is the most efficient and accurate. It correctly splits data based on 'transaction_date' into train, validation, and holdout sets. Critically, it uses 'SAMPLE ROW(... USING HASH_AGG(is_fraud))' to perform stratified sampling on the training set. The REPEATABLE(123)' clause guarantees deterministic and reproducible splits. Other Options are not correct because Option A does not address stratified sampling.Option B uses SAMPLE without hash aggregation for stratification, which is incorrect for maintaining proportions.Option C does not use ' REPEATABLE for deterministic splitting. Option DOS attempt at stratification is overly complex and inefficient, and the count calculation will likely be incorrect.


NEW QUESTION # 241
You've built a customer churn prediction model in Snowflake, and are using the AUC as your primary performance metric. You notice that your model consistently performs well (AUC > 0.85) on your validation set but significantly worse (AUC < 0.7) in production. What are the possible reasons for this discrepancy? (Select all that apply)

  • A. The production environment has significantly more missing data compared to the training and validation environments.
  • B. Your model is overfitting to the validation data. This causes to give high performance on validation set but less accurate in the real world.
  • C. The AUC metric is inherently unreliable and should not be used for model evaluation.
  • D. There's a temporal bias: the customer behavior patterns have changed since the training data was collected.
  • E. Your training and validation sets are not representative of the real-world production data due to sampling bias.

Answer: A,B,D,E

Explanation:
A, B, C, and D are all valid reasons for performance degradation in production. Sampling bias (A) means the training/validation data doesn't accurately reflect the production data. Temporal bias (B) arises when customer behavior changes over time. Overfitting (C) leads to good performance on the training/validation set but poor generalization to new data. Missing data (D) can negatively impact the model's ability to make accurate predictions. AUC is a reliable metric, especially when combined with other metrics, so E is incorrect.


NEW QUESTION # 242
......

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