Reliable DSA-C03 Test Pattern: SnowPro Advanced: Data Scientist Certification Exam - Trustable Snowflake Pdf DSA-C03 Exam Dump

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Snowflake SnowPro Advanced: Data Scientist Certification Exam Sample Questions (Q265-Q270):

NEW QUESTION # 265
A data scientist at 'Polaris Analytics' wants to estimate the average transaction value of all online purchases made during the Black Friday sale. Due to the enormous volume of data in Snowflake, they decide to use the Central Limit Theorem (CLT). They randomly sample 1000 transactions daily for 30 days and calculate the sample mean for each day. The sample mean values are stored in a Snowflake table named Which of the following SQL queries, assuming the table has a column of 'FLOAT' type, will provide the best estimate of the population mean and its confidence interval using the CLT?

A. Option D
B. Option C
C. Option E
D. Option A
E. Option B

Answer: C

Explanation:
The Central Limit Theorem states that the distribution of sample means approaches a normal distribution as the sample size increases, regardless of the population's distribution. The standard error of the mean is calculated as the population standard deviation divided by the square root of the sample size. In this case, we are estimating the standard deviation from a sample of sample means, hence using 'STDDEV_SAMP', and the sample size here is the number of days, i.e. 30.

NEW QUESTION # 266
You are using a Snowflake Notebook to analyze customer churn for a telecommunications company. You have a dataset with millions of rows and want to perform feature engineering using a combination of SQL transformations and Python code. Your goal is to create a new feature called 'average_monthly call_duration' which calculates the average call duration for each customer over the last 3 months. You are using the Snowpark DataFrame API within your notebook. Given the following code snippet to start with:

A. Option A
B. Option C
C. Option B
D. Option D
E. Option E

Answer: B,D

Explanation:
Option C and D demonstrate the most efficient approaches using Snowpark DataFrame operations and window functions. Option B is highly inefficient due to the use of UDFs and looping.Option E mixes pandas and snowpark operations which requires intermediate conversion of data into dataframe and it is not recommended for large datasets and is not aligned with Snowpark best practices. Option A just presents the base code and not a solution.

NEW QUESTION # 267
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 C
C. Option E
D. Option A
E. Option B

Answer: C

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 # 268
You are preparing a dataset in Snowflake for a K-means clustering algorithm. The dataset includes features like 'age', 'income' (in USD), and 'number of_transactions'. 'Income' has significantly larger values than 'age' and 'number of_transactions'. To ensure that all features contribute equally to the distance calculations in K-means, which of the following scaling approaches should you consider, and why? Select all that apply:

A. Do not scale the data, as K-means is robust to differences in feature scales.
B. Apply StandardScaler to all three features ('age', 'income', 'number_of_transactions') to center the data around zero and scale it to unit variance.
C. Apply PowerTransformer to transform income and StandardScaler to other features to handle skewness.
D. Apply MinMaxScaler to all three features to scale them to a range between O and 1 .
E. Apply RobustScaler to handle outliers and then StandardScaler or MinMaxScaler to further scale the features.

Answer: B,D,E

Explanation:
K-means clustering is sensitive to the scale of the features because it relies on distance calculations. Features with larger values will have a disproportionate influence on the clustering results. StandardScaler centers the data around zero and scales it to unit variance, which ensures that all features have a similar range and variance. MinMaxScaler scales the features to a range between O and 1, which also addresses the issue of different scales. RobustScaler handles outliers which will then use the other two scaling techniques. Therefore A, B and D are the appropriate scaling techniques. C is not correct as K-means relies on distance calculations and not scaling the data could give some feature a larger weight which isn't the desired outcome. Option E: Using PowerTransformer on 'income' to reduce skewness and StandardScaler on the other features can be a valid approach, but it depends on the distribution of 'income' and the presence of outliers. If 'income' is highly skewed and/or contains outliers, this combination might be more effective than using StandardScaler or MinMaxScaler alone.

NEW QUESTION # 269
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 B
E. Option E

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 # 270
......

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Reliable DSA-C03 Test Pattern: SnowPro Advanced: Data Scientist Certification Exam - Trustable Snowflake Pdf DSA-C03 Exam Dump

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Snowflake SnowPro Advanced: Data Scientist Certification Exam Sample Questions (Q265-Q270):

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