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NEW QUESTION # 39
You are building a machine learning model using Snowpark Python to predict house prices. The dataset contains a feature column named 'location' which contains free-form text descriptions of house locations. You want to leverage a pre-trained Large Language Model (LLM) hosted externally to extract structured location features like city, state, and zip code from the free-form text within Snowpark. You want to minimize the data transferred out of Snowflake. Which approach is most efficient and secure?
Answer: D
Explanation:
Using a Snowflake External Function is the most efficient and secure way to interact with an external LLM API for this task. Here's why: Efficiency: External Functions allow Snowflake to directly call the external service in parallel, leveraging Snowflake's compute resources. This minimizes data transfer between Snowflake and the client environment. Security: External Functions support secure communication with external services using API integration objects, which handle authentication and authorization. Data Governance: Keeps all processing within Snowflake's secure environment, reducing the risk of data leakage. Options A, C, and E involve transferring the data outside of Snowflake, which is less secure and less performant. Option D is not a valid approach for integrating with an external LLM API.
NEW QUESTION # 40
You are building a machine learning model to predict customer churn for a telecommunications company. One of the features is 'tariff_plan', which is a string representing different tariff plans (e.g., 'Basic', 'Premium', 'Unlimited'). You need to encode this feature for your model, but you also want to handle potential new tariff plans that might appear in future data'. Which encoding method and Snowflake SQL approach would be MOST suitable to minimize dimensionality and address unseen values effectively, assuming the number of plans is moderately high (around 20-30)?
Answer: D
Explanation:
Hash Encoding is the most suitable approach. It offers dimensionality reduction compared to One-Hot Encoding, inherently handles unseen values via the hashing function (avoiding errors or requiring explicit 'Unknown' categories), and doesn't suffer from the target leakage risks associated with Target Encoding. Label Encoding works but doesn't inherently handle unseen values and can imply ordinal relationships that may not exist. Binary encoding may be helpful, but can be more computationally expensive compared to hash encoding. One-hot encoding could be considered as a possibility as well, but in cases of larger numbers of categorical variables, it might be less beneficial.
NEW QUESTION # 41
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.
Answer: B,E
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 # 42
You are building a fraud detection model using transaction data stored in Snowflake. The dataset includes features like transaction amount, merchant category, location, and time. Due to regulatory requirements, you need to ensure personally identifiable information (PII) is handled securely and compliantly during the data collection and preprocessing phases. Which of the following combinations of Snowflake features and techniques would be MOST suitable for achieving this goal?
Answer: A,E
Explanation:
Options A and E are the MOST suitable. Option A directly addresses PII protection by leveraging Snowflake's masking policies to redact sensitive data before it is used for model training. Role-based access control provides an additional layer of security by limiting access to the unmasked data. Option E applies differential privacy to protect individual transaction data while still enabling useful model training and combines it with Row Access policies to restrict access to sensitive transaction records. Option B is partially correct but insufficient, as it only addresses which columns are seen, not protection within those columns. Option C protects the entire database but doesn't address PII handling during model training. Option D is highly risky and non-compliant, as it exposes PII to a third party without adequate protection.
NEW QUESTION # 43
A data scientist needs to analyze website session data stored in a Snowflake table named 'WEB SESSIONS'. The table contains columns like 'SESSION D', 'USER_ID, 'PAGE_VIEWS', 'TIME SPENT_SECONDS', and 'TIMESTAMP. They want to identify potential bot traffic by analyzing the correlation between 'PAGE VIEWS' and 'TIME SPENT SECONDS'. Which of the following Snowflake SQL queries is the MOST efficient and statistically sound way to calculate the Pearson correlation coefficient between these two columns, handling potential NULL values appropriately?
Answer: B
Explanation:
The 'CORR function in Snowflake directly calculates the Pearson correlation coefficient and implicitly handles NULL values by excluding rows where either input is NULL. Option A is incorrect because it does not explicitly filter NULL values, though the 'CORR' function itself handles it, Option B is mathematically correct but less concise. Option C uses 'APPROX CORR, which is useful for large datasets where approximate results are acceptable, but for a general scenario without size constraints, 'CORR is preferred for accuracy. While Option E correctly calculates the correlation coefficient using covariance and standard deviation, it uses approximation functions which may impact accuracy without a necessary tradeoff.
NEW QUESTION # 44
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