Snowflake SnowPro Advanced DSA-C03

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Snowflake SnowPro Advanced: Data Scientist Certification 認定 DSA-C03 試験問題:

1. You have a binary classification model deployed in Snowflake to predict customer churn. The model outputs a probability score between 0 and 1. You've calculated the following confusion matrix on a holdout set: I I Predicted Positive I Predicted Negative I --1 1 Actual Positive | 80 | 20 | I Actual Negative | 10 | 90 | What are the Precision, Recall, and Accuracy for this model, and what do these metrics tell you about the model's performance? SELECT statement given for true and false condition (True Positive, True Negative, False Positive, False Negative)

A) Precision = 0.89, Recall = 0.80, Accuracy = 0.85. The model is slightly better at avoiding false positives than identifying true positives.

B) Precision = 0.89, Recall = 0.80, Accuracy = 0.85. The model has good overall performance with balanced precision and recall.
C) Precision = 0.90, Recall = 0.80, Accuracy = 0.80. The model has good overall performance but needs to be adjusted to improve the false negative rate.
D) Precision = 0.80, Recall = 0.89, Accuracy = 0.85. The model is slightly better at identifying true positives than avoiding false positives.
E) Precision = 0.80, Recall = 0.90, Accuracy = 0.90. The model is performing poorly, with a high rate of both false positives and false negatives.

2. A data scientist is tasked with predicting customer churn for a telecommunications company using Snowflake. The dataset contains call detail records (CDRs), customer demographic information, and service usage data'. Initial analysis reveals a high degree of multicollinearity between several features, specifically 'total_day_minutes', 'total_eve_minutes', and 'total_night_minutes'. Additionally, the 'state' feature has a large number of distinct values. Which of the following feature engineering techniques would be MOST effective in addressing these issues to improve model performance, considering efficient execution within Snowflake?

A) Use a variance threshold to remove highly correlated CDR features and create a feature representing the geographical region (e.g., 'Northeast', 'Southwest') based on the 'state' feature using a custom UDF.
B) Apply Principal Component Analysis (PCA) to reduce the dimensionality of the CDR features ('total_day_minutes', 'total_eve_minutes', 'total_night_minutes') and use one-hot encoding for the 'state' feature.
C) Create interaction features by multiplying 'total_day_minutes' with 'customer_service_calls' and applying a target encoding to the 'state' feature.
D) Apply min-max scaling to the CDR features to normalize them and use label encoding for the 'state' feature. Train a decision tree model, as it is robust to multicollinearity.
E) Calculate the Variance Inflation Factor (VIF) for each CDR feature and drop the feature with the highest VIE Apply frequency encoding to the 'state' feature.

3. You are working with a dataset in Snowflake containing customer reviews stored in a 'REVIEWS' table. The 'SENTIMENT SCORE column contains continuous values ranging from -1 (negative) to 1 (positive). You need to create a new column, 'SENTIMENT CATEGORY, based on the following rules: 'Negative': 'SENTIMENT SCORE < -0.5 'Neutral': -0.5 'SENTIMENT SCORE 0.5 'Positive': 'SENTIMENT SCORE > 0.5 You also want to binarize this 'SENTIMENT CATEGORY column into three separate columns: 'IS NEGATIVE, 'IS NEUTRAL', and 'IS POSITIVE. Which of the following SQL statements correctly implements both the categorization and subsequent binarization?

A) Option E
B) Option D
C) Option C
D) Option B
E) Option A

4. You are building a machine learning model using Snowpark for Python and have a feature column called 'TRANSACTION AMOUNT' in your 'transaction_df DataFrame. This column contains some missing values ('NULL). Your model is sensitive to missing data'. You want to impute the missing values using the median "TRANSACTION AMOUNT, but ONLY for specific customer segments (e.g., customers with a 'CUSTOMER TIER of 'Gold' or 'Platinum'). For other customer tiers, you want to impute with the mean. Which of the following Snowpark Python code snippets BEST achieves this selective imputation?

A)

B)

C)

D)

E)

5. You're tasked with building an image classification model on Snowflake to identify defective components on a manufacturing assembly line using images captured by high-resolution cameras. The images are stored in a Snowflake table named 'ASSEMBLY LINE IMAGES', with columns including 'image_id' (INT), 'image_data' (VARIANT containing binary image data), and 'timestamp' (TIMESTAMP NTZ). You have a pre-trained image classification model (TensorFlow/PyTorch) saved in Snowflake's internal stage. To improve inference speed and reduce data transfer overhead, which approach provides the MOST efficient way to classify these images using Snowpark Python and UDFs?

A) Use Snowflake's external function feature to offload the image classification task to a serverless function hosted on AWS Lambda, passing the and 'image_icf to the function for processing.
B) Create a Python UDF that takes a single 'image_id' as input, retrieves the corresponding 'image_data' from the table, preprocesses the image, loads the pre-trained model, performs classification, and returns the result. This UDF will be called for each image individually.
C) Create a vectorized Python UDF that takes a batch of 'image_id' values as input, retrieves the corresponding 'image_data' from the 'ASSEMBLY LINE IMAGES table using a JOIN, preprocesses the images in a vectorized manner, loads the pre-trained model once at the beginning, performs classification on the batch, and returns the results.
D) Create a Java UDF that loads the pre-trained model and preprocesses the images. Call this Java UDF from a Python UDF to perform the image classification. Since Java is faster than Python, this will optimize performance.
E) Create a Python UDF that loads the entire table into memory, preprocesses the images, loads the pre-trained model, and performs classification for all images in a single execution.

質問と回答：