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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 B
B. Option E
C. Option C
D. Option D
E. Option A
Answer: A,B
Explanation:
Options B and E are correct. Option B correctly uses a CTE to first categorize the sentiment and then perform one-hot encoding using the ' IFF function. This approach is efficient and readable. Option E correctly categorizes and implements one-hot encoding using Boolean expressions and casting them to integers (0 or 1). Option A attempts to perform the one-hot encoding in the same SELECT statement as the categorization, which will result in error. Because it is referencing a column it just defined, so it wont find it. Option C is incorrect because it uses both WHEN SENTIMENT SCORE < -0.5 THEN 'Negative' and 'WHEN SENTIMENT SCORE BETWEEN -0.5 AND 0.5 THEN 'Neutral" which could include duplicates. Option D is incorrect, because it includes 'ELSE 'Unknown" that is not needed, as it should only be three rules.
NEW QUESTION # 95
A marketing analyst is building a propensity model to predict customer response to a new product launch. The dataset contains a 'City' column with a large number of unique city names. Applying one-hot encoding to this feature would result in a very high-dimensional dataset, potentially leading to the curse of dimensionality. To mitigate this, the analyst decides to combine Label Encoding followed by binarization techniques. Which of the following statements are TRUE regarding the benefits and challenges of this combined approach in Snowflake compared to simply label encoding?
A. Label encoding introduces an arbitrary ordinal relationship between the cities, which may not be appropriate. Binarization alone cannot remove this artifact.
B. Label encoding followed by binarization will reduce the memory required to store the 'City' feature compared to one-hot encoding, and Snowflake's columnar storage optimizes storage for integer data types used in label encoding.
C. While label encoding itself adds an ordinal relationship, applying binarization techniques like binary encoding (converting the label to binary representation and splitting into multiple columns) after label encoding will remove the arbitrary ordinal relationship.
D. Binarizing a label encoded column using a simple threshold (e.g., creating a 'high_city_id' flag) addresses the curse of dimensionality by reducing the number of features to one, but it loses significant information about the individual cities.
E. Binarization following label encoding may enhance model performance if a specific split based on a defined threshold is meaningful for the target variable (e.g., distinguishing between cities above/below a certain average income level related to marketing success).
Answer: A,B,D,E
Explanation:
Option A is true because label encoding converts strings into integers, which are more memory-efficient than storing numerous one-hot encoded columns. Snowflake's columnar storage further optimizes integer storage. Option B is also true; label encoding inherently creates an ordinal relationship that might not be valid for nominal features like city names. Option C is incorrect; simple binarization (e.g., > threshold) of label encoded data doesn't remove the arbitrary ordinal relationship; more complex binarization techniques would be needed. Option D is accurate; binarization reduces dimensionality but sacrifices granularity, leading to information loss. Option E is correct because carefully chosen thresholds might correlate with the target variable and improve predictive power.
NEW QUESTION # 96
You've trained a model using Snowflake ML and want to deploy it for real-time predictions using a Snowflake UDF. To ensure minimal latency, you need to optimize the UDF's performance. Which of the following strategies and considerations are most important when creating and deploying a UDF for model inference in Snowflake to minimize latency, especially when the model is large (e.g., > 100MB)?
Select all that apply.
A. Use smaller warehouse size for UDF evaluation in order to reduce latency and compute costs.
B. Use a Snowflake Stage to store the model file and load the model within the UDF using 'snowflake.snowpark.files.SnowflakeFile' to minimize memory footprint.
C. Ensure the UDF code is written in Python and utilizes vectorized operations with libraries like NumPy to process data in batches efficiently.
D. Utilize a Snowflake external function instead of a UDF if the model requires access to resources outside of Snowflake's environment.
E. Store the trained model as a BLOB within the UDF code itself to avoid external dependencies.
Answer: B,C
Explanation:
Options A and C are the most important strategies. Option A: Vectorized operations in Python using libraries like NumPy can significantly improve the performance of UDFs, especially for large datasets. Option C: Storing the model in a Snowflake Stage and loading it within the UDF helps manage memory usage efficiently, especially when dealing with large models. Option B is not recommended as embedding large BLOB data within UDF code increases UDF size. Option D: External functions introduce additional latency due to the need to communicate with external resources. Option E is incorrect because smaller warehouses may lead to longer processing times.
NEW QUESTION # 97
A data scientist is performing exploratory data analysis on a table named 'CUSTOMER TRANSACTIONS. They need to calculate the standard deviation of transaction amounts C TRANSACTION AMOUNT) for different customer segments CCUSTOMER SEGMENT). The 'CUSTOMER SEGMENT column can contain NULL values. Which of the following SQL statements will correctly compute the standard deviation, excluding NULL transaction amounts, and handling NULL customer segments by treating them as a separate segment called 'Unknown'? Consider using Snowflake-specific functions where appropriate.
A. Option B
B. Option C
C. Option D
D. Option E
E. Option A
Answer: A,B
Explanation:
Options B and C correctly calculates the standard deviation. Option B utilizes 'NVL' , which is the equivalent of 'COALESCE or ' IFNULL', to handle NULL Customer Segment values, and 'STDDEV_SAMP' for sample standard deviation, which is generally the correct function to use when dealing with a sample of the entire population. Option C also uses 'COALESCE and utilizes the 'STDDEV POP function, which returns the population standard deviation, assuming the data represents the whole population. Option A uses IFNULL, which works, and STDDEV, which is an alias for either STDDEV SAMP or STDDEV POP. The exact behavior will depend on session variable setting. Option D also uses 'CASE WHEN' construct which works to identify Unknown segments. STDDEV is again aliased. Option E calculates the variance and not Standard deviation.
NEW QUESTION # 98
You are building a customer support chatbot using Snowflake Cortex and a large language model (LLM). You want to use prompt engineering to improve the chatbot's ability to answer complex questions about product features. You have a table PRODUCT DETAILS with columns 'feature_name', Which of the following prompts, when used with the COMPLETE function in Snowflake Cortex, is MOST likely to yield the best results for answering user questions about specific product features, assuming you are aiming for concise and accurate responses focused solely on providing the requested feature description and avoiding extraneous chatbot-like conversation?
A. Option B
B. Option C
C. Option D
D. Option E
E. Option A
Answer: B
Explanation:
Option C is the best prompt because it directly instructs the LLM to act as a product expert and provide only the feature description, minimizing extraneous conversation or information. Options A and B lack specific instructions, potentially leading to verbose responses. Option D includes all product details in the prompt, which might overwhelm the LLM. Option E tries to fetch a specific feature description, but the SQL is incorrect. Correct SQL will increase token usage and may not lead to a concise response.
NEW QUESTION # 99
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