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[General] Snowflake DSA-C03 Study Center, New DSA-C03 Test Answers

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【General】 Snowflake DSA-C03 Study Center, New DSA-C03 Test Answers

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Snowflake SnowPro Advanced: Data Scientist Certification Exam Sample Questions (Q46-Q51):NEW QUESTION # 46
You are tasked with predicting the sales price of houses based on their size (square footage) using linear regression in Snowflake. You have a table named 'HOUSE PRICES' with columns 'SQUARE FOOTAGE' and 'SALES PRICE'. You want to calculate the slope and intercept using Snowflake SQL. Which of the following queries, considering potential NULL values in the data, is the MOST robust and statistically sound for calculating the slope and intercept for a simple linear regression model?

  • A. Option E
  • B. Option D
  • C. Option C
  • D. Option B
  • E. Option A
Answer: C
Explanation:
REGR SLOPE and REGR INTERCEPT are built-in Snowflake functions specifically designed for calculating the slope and intercept of a linear regression. These functions handle NULL values appropriately by excluding pairs where either SALES PRICE or SQUARE FOOTAGE is NULL, giving a statistically valid result. While options A, D, and E are theoretically correct in ideal conditions, they don't inherently handle NULL values and would require explicit filtering or potentially lead to incorrect calculations if NULLs are present. Option B uses standard deviation, which doesn't directly compute covariance needed for the slope. Option C is the most concise and reliable solution.

NEW QUESTION # 47
You are designing a feature engineering pipeline using Snowpark Feature Store for a fraud detection model. You have a transaction table in Snowflake. One crucial feature is the 'average_transaction_amount_last_7_days' for each customer. You want to implement this feature using Snowpark Python and materialize it in the Feature Store. You have the following Snowpark DataFrame 'transactions_df containing 'customer_id' and 'transaction_amount'. Which of the following code snippets correctly defines and registers this feature in the Snowpark Feature Store, ensuring efficient computation and storage?
  • A.
  • B.
  • C.
  • D.
  • E.
Answer: E
Explanation:
Option E is correct. It uses "F.avg' for calculating the average, selects only the required columns ('customer_id', 'average_transaction_amount_last_7_days') in and then sets on to ensure the feature group is fully materialized before proceeding. 'blocking-True' is important for production pipelines to avoid race conditions.

NEW QUESTION # 48
You are building a fraud detection model using Snowflake and discover a severe class imbalance (99% legitimate transactions, 1% fraudulent). You plan to use down-sampling to address this. Which of the following strategies and Snowflake SQL commands would be MOST effective and efficient for down-sampling the majority class (legitimate transactions) in a large Snowflake table named 'TRANSACTIONS before training a model using Snowpark?
  • A. Randomly delete rows from the 'TRANSACTIONS table where 'IS FRAUD = FALSE until the class distribution is balanced. This avoids data duplication but can be slow on large tables.
  • B. Use Snowpark's function with replacement to create a balanced dataset. This is efficient within the Snowpark environment but might be slower than native SQL sampling for initial data preparation.
  • C. Create a new table 'BALANCED_TRANSACTIONS' by sampling the majority class and combining it with the minority class using 'UNION ALL'. Use the 'SAMPLE clause in Snowflake SQL for efficient sampling:
  • D. Manually iterate through the 'TRANSACTIONS' table using a Snowpark 'DataFrame' and randomly select rows from the majority class. This is the most efficient approach for very large tables.
  • E. Create a new table 'BALANCED TRANSACTIONS' by sampling the majority class and combining it with the minority class using UNION ALLS. Use the'SAMPLE clause in Snowflake SQL for efficient sampling:

Answer: E
Explanation:
Option B is the most effective and efficient. Using Snowflake's 'SAMPLE clause with 'BERNOULLI' sampling is optimized for large tables. Creating a new table with 'CREATE OR REPLACE TABLE AS SELECT ... UNION ALL avoids modifying the original data and combines the down-sampled majority class with the minority class. 'SAMPLE BERNOULLI (1)' samples 1% of the data, which can be adjusted to achieve the desired balance. Option A is slow, Option C less efficient than Snowflake sampling for initial prep, Option D samples the entire majority class and does not downsample, and Option E is generally less efficient than Snowflake SQL for large-scale sampling.

NEW QUESTION # 49
You are evaluating a binary classification model's performance using the Area Under the ROC Curve (AUC). You have the following predictions and actual values. What steps can you take to reliably calculate this in Snowflake, and which snippet represents a crucial part of that calculation? (Assume tables 'predictions' with columns 'predicted_probability' (FLOAT) and 'actual_value' (BOOLEAN); TRUE indicates positive class, FALSE indicates negative class). Which of the below code snippet should be used to calculate the 'True positive Rate' and 'False positive Rate' for different thresholds
  • A. Export the 'predicted_probability' and 'actual_value' columns to a local Python environment and calculate the AUC using scikit-learn.
  • B. The best way to calculate AUC is to randomly guess the probabilities and see how it performs.
  • C. Using only SQL, Create a temporary table with calculated True Positive Rate (TPR) and False Positive Rate (FPR) at different probability thresholds. Then, approximate the AUC using the trapezoidal rule.
  • D. Calculate AUC directly within a Snowpark Python UDF using scikit-learn's function. This avoids data transfer overhead, making it highly efficient for large datasets. No further SQL is needed beyond querying the predictions data.
  • E. The AUC cannot be reliably calculated within Snowflake due to limitations in SQL functionality for statistical analysis.
Answer: C,D
Explanation:
Options A and C are correct. Option A demonstrates calculating AUC directly within Snowflake using a Snowpark Python UDF and scikit-learn's . This is efficient for large datasets as it avoids data transfer. Option C correctly outlines the process of calculating TPR and FPR using SQL and approximating AUC using the trapezoidal rule, another viable approach within Snowflake. Option B is incorrect; AUC can be calculated reliably within Snowflake. Option D is inefficient due to data transfer. Option E is blatantly incorrect.

NEW QUESTION # 50
You are managing a machine learning model lifecycle in Snowflake using the Model Registry. Which of the following statements are true regarding model lineage and governance when utilizing the Model Registry for model versioning and deployment?
  • A. Model Registry automatically retrains models based on scheduled data updates, ensuring models are always up-to-date without manual intervention.
  • B. Integration with Snowflake's RBAC (Role-Based Access Control) allows for granular control over who can register, update, and deploy model versions.
  • C. Custom tags and metadata can be associated with each model version, enabling detailed documentation and traceability of model development and deployment.
  • D. The Model Registry provides a central repository to register, version, and manage models, enabling better collaboration and governance across data science teams.
  • E. The Model Registry automatically tracks the exact SQL queries used to train the model, allowing for full reproducibility of the training process.
Answer: B,C,D
Explanation:
Options B, C, and D are correct. The Model Registry offers a centralized repository for model management (B), supports custom tags for documentation and traceability (C), and integrates with Snowflake's RBAC for access control (D). Option A is incorrect because the Model Registry does not automatically track SQL queries used for training. While lineage is a part of model governance, Model Registry's lineage capabilities are not focused on capturing training queries but rather on tracking model versions, metrics, and associated metadata. Option E is incorrect; automated retraining is not a feature of the Model Registry itself but can be orchestrated using Snowflake Tasks or other scheduling tools in conjunction with the Model Registry.

NEW QUESTION # 51
......
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