DSA-C03 Dumps Torrent: SnowPro Advanced: Data Scientist Certification Exam & DSA-C03 Real Questions

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Snowflake SnowPro Advanced: Data Scientist Certification Exam Sample Questions (Q123-Q128):

NEW QUESTION # 123
A data scientist is tasked with building a predictive maintenance model for industrial equipment. The data is collected from IoT sensors and stored in Snowflake. The raw sensor data is voluminous and contains noise, outliers, and missing values. Which of the following code snippets, executed within a Snowflake environment, demonstrates the MOST efficient and robust approach to cleaning and transforming this sensor data during the data collection phase, specifically addressing outlier removal and missing value imputation using robust statistics? Assume necessary libraries like numpy and pandas are available via Snowpark.

Answer: A

Explanation:
Option E is the MOST robust and efficient. It uses the interquartile range (IQR) method, which is less sensitive to extreme outliers than the z-score method in Option A. It also utilizes 'approx_quantile' and is therefore more optimized for Snowflake large datasets. The median is also a more robust measure of central tendency for imputation than the mean when dealing with outliers. Option C uses a hard-coded threshold for outlier removal and imputes with 0, which is not adaptive or robust. Option D skips data cleaning altogether.Option A uses z-score which may work however, since IoT has continuous streaming data quantile based outlier removal is better. It is more optimised for large dataset and better at handling streaming datasets.

NEW QUESTION # 124
You have trained a fraud detection model using scikit-learn and want to deploy it in Snowflake using the Snowflake Model Registry. You've registered the model as 'fraud _ model' in the registry. You need to create a Snowflake user-defined function (UDF) that loads and executes the model. Which of the following code snippets correctly creates the UDF, assuming the model is a serialized pickle file stored in a stage named 'model_stage'?

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

Answer: E

Explanation:
Option E is the most correct. It includes the correct Snowflake UDF syntax, specifies the required packages (snowflake-snowpark- python, scikit-learn, pandas), imports the model from the stage, and defines a handler class with a 'predict' method that loads the model using pickle and performs the prediction. It also correctly utilizes the to access files from the stage. Other options have errors in syntax, file access within the UDF environment or how input features are handled.

NEW QUESTION # 125
You are building a fraud detection model using Snowflake data'. One of the features is 'transaction_amount', which has a highly skewed distribution and contains outlier values. Which scaling technique is most appropriate to handle this situation effectively in Snowflake, considering the need to minimize the impact of outliers and preserve the shape of the distribution as much as possible, before feeding the data into a machine learning model? Assume you have sufficient compute resources.

A. RobustScaler (using interquartile range)
B. MinMaxScaler (Min-Max scaling)
C. Power Transformer (Yeo-Johnson or Box-Cox)
D. StandardScaler (Z-score normalization)
E. No scaling is needed as tree-based models are robust to skewed data.

Answer: A,C

Explanation:
RobustScaler is suitable for handling outliers as it uses the interquartile range, which is less sensitive to extreme values than the mean and standard deviation used by StandardScaler. PowerTransformer can also be useful for transforming skewed data to a more Gaussian-like distribution, which can improve the performance of some machine learning models. While tree-based models are generally more robust to skewed data than other models, scaling can still improve convergence speed or performance, especially when combined with other preprocessing techniques or models that are sensitive to feature scaling. Therefore, E is not a great choice. Using RobustScaler and PowerTransformer will lead to a better performance of model.

NEW QUESTION # 126
You have trained a classification model in Snowflake using Snowpark ML to predict customer churn. After deploying the model, you observe that the model performs well on the training data but poorly on new, unseen data'. You suspect overfitting. Which of the following strategies can be applied within Snowflake to detect and mitigate overfitting during model validation , considering the model is already deployed and receiving inference requests through a Snowflake UDF?

A. Since the model is already deployed, the only option is to collect inference requests and compare the distributions of predicted values in each batch with the predicted values on the training set. A large difference indicates overfitting; model must be retrained outside of the validation process.
B. Monitor the UDF execution time in Snowflake. A sudden increase in execution time indicates overfitting. Use the 'EXPLAIN' command on the UDF's underlying SQL query to identify performance bottlenecks and rewrite the query for optimization.
C. Calculate the Area Under the Precision-Recall Curve (AUPRC) using Snowflake SQL on both the training and validation datasets. A significant difference indicates overfitting. Then, retrain the model in Snowpark ML with added L1 or L2 regularization, adjusting the regularization strength based on validation set performance, and redeploy the UDF.
D. Create shadow UDFs that score data using alternative models. Compare the performance metrics (such as accuracy, precision, recall) between the production UDF and shadow UDFs using Snowflake's query capabilities. If shadow models consistently outperform the production model on certain data segments, retrain the production model incorporating those data segments with higher weights.
E. Implement k-fold cross-validation within the Snowpark ML training pipeline using Snowflake's distributed compute. Track the mean and standard deviation of the performance metrics (e.g., accuracy, Fl-score) across folds. A high variance suggests overfitting. Use this information to tune hyperparameters or select a simpler model architecture before deployment.

Answer: C,E

Explanation:
Options A and C are correct because they describe strategies for detecting and mitigating overfitting during the model validation process using Snowflake's capabilities. AUPRC is a good performance metric to compare the training vs validation set results to catch overfitting, and regularization can be used to avoid overfitting. Option C directly incorporates cross-validation into the model training workflow within Snowflake, allowing for early detection and mitigation of overfitting through hyperparameter tuning and model selection. Option B is incorrect because it focuses on performance optimization, not overfitting. Option D describes an AIB testing or champion-challenger setup which could be a strategy to use to detect data drift over time, but not overfitting. E is only partially correct as it describes one way to detect data drift, but not overfitting.

NEW QUESTION # 127
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. 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.
B. 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.
C. Use 'SNOWFLAKE.ML.MODEL REGISTRY.CREATE MODEL' with default settings, which automatically handles temporal partitioning based on the insertion timestamp of the data.
D. 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
E. 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.

Answer: E

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 # 128
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DSA-C03 DUMPS TORRENT: SNOWPRO ADVANCED: DATA SCIENTIST CERTIFICATION EXAM & DSA-C03 REAL QUESTIONS

DSA-C03 Dumps Torrent: SnowPro Advanced: Data Scientist Certification Exam & DSA-C03 Real Questions