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Snowflake SnowPro Advanced: Data Scientist Certification Exam Sample Questions (Q171-Q176):
NEW QUESTION # 171
You are working on a customer churn prediction project. One of the features you want to normalize is 'customer_age'. However, a Snowflake table constraint ensures that all 'customer_age' values are between 0 and 120 (inclusive). Furthermore, you want to avoid using any stored procedures and prefer a pure SQL approach for data transformation. Considering these constraints, which normalization technique and associated SQL query is the most appropriate in Snowflake for this scenario, guaranteeing that the scaled values remain within a predictable range?
- A. Z-score standardization:
- B. Z-score standardization after clipping values outside 1 and 99 percentile:
- C. Min-Max scaling directly to the range [0, 1] using the known bounds (0 and 120):
- D. Box-Cox transformation:
- E. Min-Max scaling to the range [0, 1]:
Answer: C
Explanation:
Option D is the most appropriate. Given the existing constraint on 'customer_age' (0-120), and the requirement to avoid stored procedures, directly scaling to the range [0, 1] using the known minimum and maximum values is efficient and guarantees the output remains within a predictable range. This approach avoids data-dependent calculations (like MIN and MAX over the entire dataset) which are unnecessary given the constraint. Option A won't guarantee values within [0, 1]. Option B is correct but option D is the efficient solution to get the expected outcome and avoid cost and complexity. Option C would not scale to between O and 1 and adds complexity. Option E is not a normalization technique.
NEW QUESTION # 172
You are building an automated model retraining pipeline for a sales forecasting model in Snowflake using Snowflake Tasks and Stored Procedures. After retraining, you want to validate the new model against a champion model already deployed. You need to define a validation strategy using the following models: champion model deployed as UDF "FORECAST UDF , and contender model deployed as UDF 'FORECAST UDF NEW'. Given the following objectives: (1) Minimal impact on production latency, (2) Ability to compare predictions on a large volume of real-time data, (3) A statistically sound comparison metric. Which of the following SQL statements best represents how to efficiently compare the forecasts of the two models on a sample dataset and calculate the Root Mean Squared Error (RMSE) to validate the new model?
- A.
- B.
- C.
- D.
- E.
Answer: C
Explanation:
Option E is the best approach. It samples the data using 'SAMPLE BERNOULLI(IO)' for minimal impact on production. Then, it calculates both the challenger RMSE (new model) and the champion RMSE on this sample data. This provides a direct comparison of the model performance against actual sales and also allows to minimise runtime to compute this metric compared to option C which computes a difference without evaluating if the new model has a better score. Sampling helps with minimal impact while comparison metric in this case needs the actual_sales column. This provides a statistically relevant comparison within Snowflake, minimizing external processing. Option A does not compare the model to the ground truth (actual sales). Option B only compares the challenger and champion models' predictions against each other on a small, limited dataset (1000 records), which may not be representative. Options C calculates the RMSE difference directly and has a SAMPLE size of 1, which is unlikely to reflect the reality and Option D filters based on RMSE, which makes the approach bias and makes it harder to evalute if the RMSE is statistically significant.
NEW QUESTION # 173
You are developing a real-time fraud detection system using Snowflake and an external function. The system involves scoring incoming transactions against a pre-trained TensorFlow model hosted on Google Cloud A1 Platform Prediction. The transaction data resides in a Snowflake stream. The goal is to minimize latency and cost. Which of the following strategies are most effective to optimize the interaction between Snowflake and the Google Cloud A1 Platform Prediction service via an external function, considering both performance and cost?
- A. Batch multiple transactions from the Snowflake stream into a single request to the external function. The external function then sends the batched transactions to the Google Cloud A1 Platform Prediction service in a single request. This increases throughput but might introduce latency.
- B. Implement a caching mechanism within the external function (e.g., using Redis on Google Cloud) to store frequently accessed model predictions, thereby reducing the number of calls to the Google Cloud A1 Platform Prediction service. This requires managing cache invalidation.
- C. Invoke the external function for each individual transaction in the Snowflake stream, sending the transaction data as a single request to the Google Cloud A1 Platform Prediction service.
- D. Use a Snowflake pipe to automatically ingest the data from the stream, and then trigger a scheduled task that periodically invokes a stored procedure to train the model externally.
- E. Implement asynchronous invocation of the external function from Snowflake using Snowflake's task functionality. This allows Snowflake to continue processing transactions without waiting for the response from the Google Cloud A1 Platform Prediction service, but requires careful monitoring and handling of asynchronous results.
Answer: A,B,E
Explanation:
Options B, C and E are correct. Caching (B) reduces calls to the external prediction service, minimizing both latency and cost, especially for redundant transactions. Batching (C) amortizes the overhead of invoking the external function and reduces the number of API calls to Google Cloud, improving throughput. Asynchronous invocation (E) allows Snowflake to continue processing without waiting, improving responsiveness. Option A is incorrect, as it will be a very slow and costly process. Option D mentions training the model which is unrelated to the prediction goal and would involve different steps involving the external function and model training.
NEW QUESTION # 174
A data scientist is using Snowflake to perform anomaly detection on sensor data from industrial equipment. The data includes timestamp, sensor ID, and sensor readings. Which of the following approaches, leveraging unsupervised learning and Snowflake features, would be the MOST efficient and scalable for detecting anomalies, assuming anomalies are rare events?
- A. Use a Support Vector Machine (SVM) with a radial basis function (RBF) kernel trained on the entire dataset to classify data points as normal or anomalous. Implement the SVM model as a Snowflake UDF.
- B. Use K-Means clustering to group sensor readings into clusters and identify data points that are far from the cluster centroids as anomalies. No model training necessary.
- C. Calculate the moving average of sensor readings over a fixed time window using Snowflake SQL and flag data points that deviate significantly from the moving average as anomalies. No ML model needed.
- D. Implement an Isolation Forest model. Train the Isolation Forest model on a representative sample of the sensor data and create UDF to score each row in snowflake.
- E. Apply Autoencoders to the sensor data using a Snowflake external function. Data points are considered anomalous if the reconstruction error from the autoencoder exceeds a certain threshold.
Answer: D
Explanation:
Isolation Forest is specifically designed for anomaly detection and performs well with high-dimensional data. Because anomalies are defined as 'few and different,' Isolation Forest builds an ensemble of trees and isolates observations by randomly selecting a feature and then randomly selecting a split value between the maximum and minimum values of the selected feature. Anomalies require fewer splits to be isolated and consequently have a shorter path length in the tree, where this path length is the measurement of 'solation'. It is scalable and well-suited for large datasets within Snowflake, especially when integrated via a UDF.SVM is computationally intensive. K-Means only effective when anomalies are caused by shifted data, no individual outliers. Calculationg the moving average is quick to compute, and has a faster throughput, but is extremely sensitive to outliers. Option A is computationally expensive and may not scale well. Options C is suitable for a high level initial assessment, and not for accuracy. Option E, Autoencoders would have difficulty training and might not perform well.
NEW QUESTION # 175
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. There's a temporal bias: the customer behavior patterns have changed since the training data was collected.
- 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. Your training and validation sets are not representative of the real-world production data due to sampling bias.
- D. The production environment has significantly more missing data compared to the training and validation environments.
- E. The AUC metric is inherently unreliable and should not be used for model evaluation.
Answer: A,B,C,D
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 # 176
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