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NEW QUESTION # 25
You are working with a large sales transaction dataset in Snowflake, stored in a table named 'SALES DATA'. This table contains columns such as 'TRANSACTION_ID (unique identifier), 'CUSTOMER_ID', 'PRODUCT_ID, 'TRANSACTION_DATE' , and 'AMOUNT'. Due to a system error, some transactions were duplicated in the table. Your goal is to remove these duplicates efficiently using Snowpark for Python. You want to use the 'window.partitionBy()' and functions. Which of the following code snippets correctly removes duplicates based on all columns, while also creating a new column 'ROW NUM' to indicate the row number within each partition?
Answer: D
Explanation:
Option A is the correct answer because it correctly partitions the data by all columns using 'sales_df.columns' within the function. It then assigns a row number within each partition using Finally, it filters the data to keep only the first row (ROW_NUM = 1) within each partition, effectively removing duplicates. The removes the temporary column and saves the unique data to a new table. Option B is incorrect because it uses 'orderBy' instead of 'partitionBy' , which does not group identical rows together for duplicate removal. Option C is incorrect because it uses ' F.rank()' instead of 'rank()' assigns the same rank to identical rows within a partition, potentially keeping more than one duplicate. Option D is incorrect because unpacking the dataframe column in partitionby using sales_df.columns causes TypeError: Column is not iterable. Option E is incorrect because passing the entire sales_df to partitionBy is not valid.
NEW QUESTION # 26
You are tasked with performing data profiling on a large customer dataset in Snowflake to identify potential issues with data quality and discover initial patterns. The dataset contains personally identifiable information (PII). Which of the following Snowpark and SQL techniques would be most appropriate to perform this task while minimizing the risk of exposing sensitive data during the exploratory data analysis phase?
Answer: A,D
Explanation:
Options C and D provide the most secure and effective ways to perform exploratory data analysis while protecting PII. Differential privacy (C) ensures that aggregate statistics do not reveal too much information about individuals. Masked views (D) prevent direct access to sensitive data, replacing it with masked values during the analysis. A is dangerous because it exposes the raw data. B while reduces the volume, still exposes raw data. E is risky because it involves exporting sensitive data outside of Snowflake.
NEW QUESTION # 27
You are developing a model to predict equipment failure in a factory using sensor data stored in Snowflake. The data is partitioned by 'EQUIPMENT ID' and 'TIMESTAMP. After initial model training and cross-validation using the following code snippet:
You observe significant performance variations across different equipment groups when evaluating on out-of-sample data'. Which of the following strategies could you employ to address this issue within the Snowflake environment to improve the model's generalization ability across all equipment?
Answer: C,D
Explanation:
Options C and E are the most effective strategies. Option C (Feature Engineering): By creating interaction terms between EQUIPMENT _ ICY and other sensor features, the model can learn equipment-specific patterns. This enables the model to account for the unique characteristics of each equipment group, improving its ability to generalize across all equipment. For example, the optimal temperature threshold for triggering a failure might differ significantly between EQUIPMENT_ID' groups, and this can be captured using interaction terms. Option E (Seperate models per Equipment ID) : Hyperparameter tuning and training separate models per equipment ID enables you to optimize and customize the model specific to each equipment ID. The downsize is that we need to create and manage more models. Options A and D are less effective or may have limitations: Option A (Increase Training Data Size): While increasing the training data size can sometimes improve model performance, it doesn't guarantee that the model will learn to differentiate between the equipment groups effectively, especially if some groups have significantly different data characteristics. This can also consume a lot of resources unnecessarily. Option D (Custom cross Validation) : While it's valid, it is difficult to implement and the built in Snowflake cross validation features is much more performant and easier to use.
NEW QUESTION # 28
You are using Snowpark Pandas to prepare data for a machine learning model. You have a Snowpark DataFrame named 'transactions df that contains transaction data, including 'transaction id', 'product id', 'customer id', and 'transaction_amount'. You want to create a new feature that represents the average transaction amount per customer. However, you are concerned about potential skewness in the 'transaction_amount' and want to apply a log transformation to reduce its impact before calculating the average. Which of the following steps using Snowpark Pandas would achieve this transformation and calculation most efficiently within Snowflake?
Answer: C
Explanation:
Option B is the most efficient solution because it performs both the log transformation and the average calculation entirely within Snowflake using Snowpark functions. This avoids the overhead of transferring the data to the client side. It uses F.logl p() to apply the log transformation to the 'transaction_amount' column, handling potential zero values gracefully. It groups by 'customer_id' and uses F.mean() to calculate the average of the transformed transaction amounts.
NEW QUESTION # 29
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?
Answer: C
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 # 30
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