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You are tasked with optimizing the hyperparameter tuning process for a complex deep learning model within Snowflake using Snowpark Python. The model is trained on a large dataset stored in Snowflake, and you need to efficiently explore a wide range of hyperparameter values to achieve optimal performance. Which of the following approaches would provide the MOST scalable and performant solution for hyperparameter tuning in this scenario, considering the constraints and capabilities of Snowflake?
A. Implementing a custom hyperparameter optimization algorithm using Snowpark Python UDFs, manually distributing the hyperparameter search across multiple Snowflake virtual warehouses, and aggregating the results in a central table.
B. Using 'Hyperopt' within a Snowpark Python UDF, relying on its sequential optimization approach without any form of parallelization or distribution of the workload.
C. Using a simple 'for' loop to iterate through a predefined set of hyperparameter combinations, training the model for each combination within a Snowpark Python UDF, and storing the results in a Snowflake table.
D. Leveraging a distributed hyperparameter tuning framework like Ray Tune or Dask-ML within Snowpark Python, utilizing Snowflake's compute resources for parallel training and evaluation of different hyperparameter configurations.
E. Employing Scikit-learn's 'GridSearchCV' or 'RandomizedSearchCV' within a single Snowpark Python UDF, processing the entire dataset and hyperparameter search sequentially.
Answer: D
Explanation:
Option B is the most scalable and performant solution. Distributed hyperparameter tuning frameworks like Ray Tune or Dask-ML are designed to efficiently parallelize the hyperparameter search process across multiple compute resources. By integrating these frameworks with Snowpark Python, you can leverage Snowflake's scalable compute infrastructure to train and evaluate multiple hyperparameter configurations simultaneously, significantly reducing the overall tuning time. Option A is inefficient as it relies on a serial process. Option C is limited by the computational resources of a single Snowpark Python UDF. Option D is complex and requires manual management of distributed tasks, making it less efficient and scalable than using a dedicated framework. Option E is also limited by its sequential nature and does not take advantage of Snowflake's distributed computing capabilities.
NEW QUESTION # 89
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?
A. Export the entire customer dataset to an external data lake for exploratory analysis using Spark and Python. Apply data masking in Spark before analysis.
B. Apply differential privacy techniques using Snowpark to add noise to the summary statistics generated from the customer data, masking the individual contributions of each customer while revealing overall trends.
C. Directly query the raw customer data using SQL and Snowpark, computing descriptive statistics like mean, median, and standard deviation for all numeric columns and frequency counts for categorical columns. Store the results in a temporary table for further analysis.
D. Create a masked view of the customer data using Snowflake's dynamic data masking features. This view masks sensitive PII columns while allowing you to compute aggregate statistics and identify patterns using SQL and Snowpark functions. Columns like 'email' are masked using and columns like are masked using .
E. Utilize Snowpark to create a sampled dataset (e.g., 1% of the original data) and perform all exploratory data analysis on the sample to reduce the data volume and potential exposure of PII.
Answer: B,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 # 90
You have a Snowflake table called 'website visits' with columns 'user id', 'visit_date', and You need to identify users who consistently spend a large amount of time on specific page URLs. You want to calculate the average time spent per user on each page URL and then find the top 10 page URLs where users, on average, spend the most time. Which of the following approaches is the MOST efficient and accurate for achieving this in Snowflake?
A.
B.
C.
D.
E.
Answer: A
Explanation:
Option D is the most accurate and efficient solution. It first calculates the average time spent by each user on each page URL using a CTE (Common Table Expression) called UserPageAvg' . Then, it calculates the average of these averages across all users for each page URL and orders the results to find the top 10. It avoids using temporary tables, making it more efficient than option A. Option A uses a temporary table, which can add overhead. Option B incorrectly calculates the average time across all visits to a page, rather than the average of each user's average time, giving undue weight to pages with many visits. Option C's use of OVER() without a PARTITION BY clause is incorrect within the context of the AVG function and desired result. Option E uses , which sacrifices accuracy for speed and may not be appropriate when precise average time is required. Also, it directly averages the time spent per visit rather than averaging the average time spent by each user on that page.
NEW QUESTION # 91
You are tasked with estimating the 95% confidence interval for the median annual income of Snowflake customers. Due to the non-normal distribution of incomes and a relatively small sample size (n=50), you decide to use bootstrapping. You have a Snowflake table named 'customer_income' with a column 'annual_income'. Which of the following SQL code snippets, when correctly implemented within a Python script interacting with Snowflake, would most accurately achieve this using bootstrapping with 1000 resamples and properly calculate the confidence interval?
A.
B.
C.
D.
E.
Answer: A
Explanation:
Option A is the correct answer. It accurately implements bootstrapping by: (1) Resampling with replacement from the original data. (2) Calculating the median of each resample. (3) Computing the 2.5th and 97.5th percentiles of the bootstrap medians to obtain the 95% confidence interval. Option B calculates the mean instead of the median, and uses 'random.sample' without replacement, which is incorrect for bootstrapping. Option C doesn't resample at all, just calculates the mean of the original data repeatedly. Option D calculates the mean instead of the median. Option E calculates 90% confidence interval instead of 95%.
NEW QUESTION # 92
You are tasked with feature engineering a dataset containing customer transaction data stored in a Snowflake table named 'CUSTOMER TRANSACTIONS'. This table includes columns like 'CUSTOMER ID', 'TRANSACTION DATE, and 'TRANSACTION AMOUNT. You need to create a new feature representing the 'Recency' of the customer, which is the number of days since their last transaction. Using Snowpark Pandas, which of the following code snippets will correctly calculate the Recency feature as a new column in a Snowpark DataFrame?
A. Option E
B. Option D
C. Option B
D. Option A
E. Option C
Answer: A
Explanation:
Option E is the only fully correct approach. It correctly groups by 'CUSTOMER_ID and finds the maximum transaction date. It calculates the Recency by using 'datediff, , and casting 'LAST_TRANSACTION_DATE' with Without the cast to , it is possible to run into error in 'datediff function. 'datediff function will cause issues when used on a timestamp. The 'recency_sdf dataframe will only have customer_id and recency.
NEW QUESTION # 93
......
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