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[General] Valid DSA-C03 Braindumps & New DSA-C03 Exam Objectives

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【General】 Valid DSA-C03 Braindumps & New DSA-C03 Exam Objectives

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Snowflake SnowPro Advanced: Data Scientist Certification Exam Sample Questions (Q126-Q131):NEW QUESTION # 126
A data scientist is preparing customer churn data for a machine learning model in Snowflake. The dataset contains a 'Contract_Type' column with values 'Month-to-Month', 'One Year', and 'Two Year'. They want to use label encoding to transform this categorical feature into numerical values. Which of the following SQL statements correctly implements label encoding for the 'Contract_Type' column, assigning 'Month-to-Month' to 0, 'One Year' to 1, and 'Two Year' to 2, and creates a new column named 'Contract_Type_Encoded'? Additionally, the data scientist wants to handle potential NULL values in 'Contract_Type' by assigning them the value of -1.

  • A. Option D
  • B. Option E
  • C. Option A
  • D. Option B
  • E. Option C
Answer: B
Explanation:
Both options B and D achieve the desired label encoding and handle NULL values correctly. Option B creates a new table based on the transformed dataset, which is a common approach for preserving the original data. Option D directly updates the existing table which could be appropriate depending on the team and security structure.

NEW QUESTION # 127
You are building a predictive model for customer churn using linear regression in Snowflake. You have identified several features, including 'CUSTOMER AGE', 'MONTHLY SPEND', and 'NUM CALLS'. After performing an initial linear regression, you suspect that the relationship between 'CUSTOMER AGE and churn is not linear and that older customers might churn at a different rate than younger customers. You want to introduce a polynomial feature of "CUSTOMER AGE (specifically, 'CUSTOMER AGE SQUARED') to your regression model within Snowflake SQL before further analysis with python and Snowpark. How can you BEST create this new feature in a robust and maintainable way directly within Snowflake?

  • A. Option D
  • B. Option A
  • C. Option C
  • D. Option E
  • E. Option B
Answer: C
Explanation:
Creating a VIEW (option C) is the BEST approach for several reasons. It doesn't modify the underlying data, which is crucial for data govemance and prevents unintended side effects. The feature is calculated on-the-fly whenever the view is queried, ensuring that the feature is always up-to-date if the underlying changes. Options A, D, and E permanently alter the table, potentially leading to data redundancy and requiring manual updates if the column changes. Option B creates a temporary table, which is suitable for short-lived experiments but not ideal for a feature that will be used repeatedly. Using 2) is equivalent to CUSTOMER_AGE CUSTOMER_AGE. Views are efficient because Snowflake's query optimizer can often push down computations into the underlying table. Option C also avoids needing to manage the lifecycle of updated calculated columns.

NEW QUESTION # 128
You are building a predictive model on customer churn using Snowflake data'. You observe that the distribution of 'TIME SINCE LAST PURCHASE' is heavily left-skewed. Which of the following strategies would be MOST appropriate to handle this skewness before feeding the data into a linear regression model to improve its performance? (Select TWO)
  • A. Apply a logarithmic transformation to the 'TIME SINCE LAST PURCHASE' column.
  • B. Apply a square root transformation to the 'TIME_SINCE_LAST_PURCHASE' column.
  • C. Standardize the 'TIME_SINCE_LAST_PURCHASE' column using Z-score normalization.
  • D. Remove all records with 'TIME SINCE LAST PURCHASE' values below the mean.
  • E. Use a winsorization technique to cap extreme values in the 'TIME SINCE LAST PURCHASE' column at a predefined percentile (e.g., 99th percentile).
Answer: B,E
Explanation:
For left-skewed data, a square root transformation (Option B) can help reduce the impact of smaller values and bring the distribution closer to normal. Winsorization (Option C) can mitigate the influence of extreme values on the left tail of the distribution, making the data more suitable for linear regression. Logarithmic transformation is more suitable for right-skewed data (Option A). Z-score normalization (Option D) centers the data around zero but doesn't change the skewness. Removing records below the mean (Option E) is generally not a good practice as it can introduce bias and lose valuable information.

NEW QUESTION # 129
A data scientist is analyzing sales data in Snowflake to identify seasonal trends. The 'SALES TABLE' contains columns 'SALE DATE' (DATE) and 'SALE _ AMOUNT' (NUMBER). They want to calculate the average daily sales amount for each month and year in the dataset. Which of the following SQL queries will correctly achieve this, while also handling potential NULL values in 'SALE AMOUNT?

  • A. Option E
  • B. Option A
  • C. Option D
  • D. Option B
  • E. Option C
Answer: A,C,D
Explanation:
Options B, D and E correctly calculate the average daily sales for each month and year. Options B uses 'COALESCE' to replace NULL SALE_AMOUNT values with 0 before calculating the average. Option D utilizes ' NVL', which is a synonym for COALESCE in Snowflake. Option E uses ZEROIFNULL' which is another way to handle NULL values. Option A does not handle NULL values, potentially skewing the average. Option C incorrectly uses "TO_CHAR which results in string format for date, but that is fine, it also tries to use 'IFF which is acceptable to handle Null, but SIFF function may lead to string conversion issues when calculating the average.

NEW QUESTION # 130
You are working with a dataset of customer transaction logs stored in Snowflake. Due to legal restrictions, you are unable to directly access or analyze the entire dataset. However, you can query aggregate statistics. You need to estimate the standard error of the mean transaction amount using bootstrapping. Knowing that you cannot retrieve the individual transaction amounts directly, which of the following approaches, while technically feasible within Snowflake and its stored procedure capabilities, is the least appropriate and potentially misleading application of bootstrapping?
  • A. Attempt to apply the central limit theorem rather than bootstrapping.
  • B. Construct a stored procedure that uses the available aggregated statistics (e.g., mean, standard deviation, and sample size) to generate bootstrap samples based on an assumed parametric distribution (e.g., gamma or log-normal) fitted to the data, and then estimate the standard error from these resamples.
  • C. Use the available aggregate statistics to create many synthetic datasets, all adhering to the same mean, variance, and total sample size. Then, compute the statistic of interest (mean transaction amount) for each of these synthetic datasets, and use this collection to estimate the standard error. This is a valid approach.
  • D. Develop a stored procedure that generates random samples from a normal distribution with the same mean and standard deviation as the aggregated transaction data available to you, then calculates the standard error of the mean from these synthetic resamples.
  • E. Even without individual transaction data, bootstrapping is fundamentally impossible in this scenario, as bootstrapping requires resampling from the original data . All given options are therefore equally inappropriate.
Answer: D
Explanation:
Option A is the least appropriate. Generating random samples from a normal distribution with the same mean and standard deviation as the aggregated data, fundamentally violates the principle of bootstrapping. Bootstrapping relies on resampling from the original data to approximate the sampling distribution of a statistic. Creating data from a pre-defined distribution removes the inherent characteristics of the true data generating process and produces potentially very misleading results. Option B, using a parametric distribution, while still based on assumptions, is slightly better than A as it attempts to fit a distribution to the known data characteristics, but still relies on potentially incorrect distribution assumptions. Option C is not correct. Even the most inappropriate usage will give an answer. Option D is a valid approach, but it not Bootstrapping. Option E follows the basic idea of bootstrapping.

NEW QUESTION # 131
......
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