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DSA-C03 real exam dumps: SnowPro Advanced: Data Scientist Certification Exam & DSA-C03 free practice exam

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Snowflake SnowPro Advanced: Data Scientist Certification Exam Sample Questions (Q111-Q116):

NEW QUESTION # 111
A financial institution suspects fraudulent activity based on unusual transaction patterns. They want to use association rule mining to identify relationships between different transaction attributes (e.g., transaction amount, location, time of day, merchant category code) that are indicative of fraud. The data is stored in a Snowflake table called 'TRANSACTIONS'. Which of the following considerations are CRITICAL when applying association rule mining in this fraud detection scenario?

A. Carefully discretize continuous variables like 'transaction amount' and 'time of day' into meaningful categories to enable association rule mining, and consider the impact of different discretization strategies on the resulting rules.
B. Ensure that the Apriori algorithm is run directly within Snowflake using SQL to maximize performance and scalability, rather than extracting the data and processing it in an external Python environment.
C. Focus solely on rules with very high support (e.g., > 0.1) to ensure statistical significance and avoid overfitting to rare fraudulent events.
D. Ignore transaction attributes that have a large number of distinct values (e.g., specific location coordinates) as they will likely lead to an explosion of rules and make interpretation difficult.
E. Prioritize rules with high confidence and lift, even if support is relatively low, as rare but highly predictive combinations of attributes can be strong indicators of fraudulent activity.

Answer: A,E

Explanation:
Option B is critical because discretization is essential for handling continuous variables in association rule mining. The way these variables are binned can significantly influence the rules discovered. Option C is also critical because in fraud detection, identifying rare but highly predictive rules is crucial. Low support rules, if they have high confidence and lift, can point to specific patterns indicative of fraud. Option A is incorrect because requiring high support would miss rare fraud patterns. Option D is incorrect because some high cardinality attributes might be important indicators.Option E is incorrect as Apriori algorith cannot be directly run using SQL, Snowpark and python is a good option.

NEW QUESTION # 112
You are training a fraud detection model on a dataset containing millions of transactions. To ensure robust generalization, you've decided to implement a train-validation-holdout split using Snowflake's capabilities. Given the following requirements: Temporal Split: The dataset contains a 'transaction date' column. You want to ensure that the validation and holdout sets contain transactions after the training data'. This is crucial because fraud patterns evolve over time. Stratified Sampling (Within Training): The training set should maintain the original proportion of fraudulent vs. non-fraudulent transactions. The column indicates if a transaction is fraudulent (1) or not (0). Deterministic Splits: You need a repeatable process to ensure consistency across model iterations. Which of the following SQL code snippets best achieves these requirements, considering performance and best practices within Snowflake?

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

Answer: B

Explanation:
Option E is the most efficient and accurate. It correctly splits data based on 'transaction_date' into train, validation, and holdout sets. Critically, it uses 'SAMPLE ROW(... USING HASH_AGG(is_fraud))' to perform stratified sampling on the training set. The REPEATABLE(123)' clause guarantees deterministic and reproducible splits. Other Options are not correct because Option A does not address stratified sampling.Option B uses SAMPLE without hash aggregation for stratification, which is incorrect for maintaining proportions.Option C does not use ' REPEATABLE for deterministic splitting. Option DOS attempt at stratification is overly complex and inefficient, and the count calculation will likely be incorrect.

NEW QUESTION # 113
You are performing exploratory data analysis on a dataset containing customer transaction data in Snowflake. The dataset has a column named 'transaction_amount' and a column named 'customer_segment'. You want to analyze the distribution of transaction amounts for each customer segment using Snowflake's statistical functions. Which of the following approaches would BEST achieve this, providing insights into the central tendency and spread of the data?

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

Answer: B

Explanation:
Option E is the best approach. It uses to calculate the mean, to calculate the median (robust to outliers), to calculate the standard deviation (measure of spread), and 'QUANTILE(transaction_amount, 0.25, 0.5, 0.75)' to calculate the quartiles (25th, 50th, and 75th percentiles), all grouped by 'customer_segment'. This provides a comprehensive view of the distribution. Option A only provides an approximate count of distinct transaction amounts and the average. Option B provides standard deviation, variance, and median but lacks the mean and quartiles. Option C provides the range and count, which are useful but not as comprehensive. Option D calculates correlation and covariance, which are useful for understanding the relationship between transaction amount and customer segment (assuming customer segment is appropriately encoded numerically), but not for analyzing the distribution within each segment. It is important to note that 'QUANTILE' can also be accomplished using 'APPROX_PERCENTILE'

NEW QUESTION # 114
You are using a Snowflake Notebook to build a churn prediction model. You have engineered several features, and now you want to visualize the relationship between two key features: and , segmented by the target variable 'churned' (boolean). Your goal is to create an interactive scatter plot that allows you to explore the data points and identify any potential patterns.
Which of the following approaches is most appropriate and efficient for creating this visualization within a Snowflake Notebook?

A. Leverage Snowflake's native support for Streamlit within the notebook to create an interactive application. Query the data directly from Snowflake within the Streamlit app and use Streamlit's plotting capabilities for visualization.
B. Write a stored procedure in Snowflake that generates the visualization data in a specific format (e.g., JSON) and then use a JavaScript library within the notebook to render the visualization.
C. Create a static scatter plot using Matplotlib directly within the Snowflake Notebook by converting the data to a Pandas DataFrame. This involves pulling all relevant data into the notebook's environment before plotting.
D. Use the Snowflake Connector for Python to fetch the data, then leverage a Python visualization library like Plotly or Bokeh to generate an interactive plot within the notebook.
E. Use the 'snowflake-connector-python' to pull the data and use 'seaborn' to create static plots.

Answer: A

Explanation:
Option D, leveraging Snowflake's native support for Streamlit, is the most appropriate and efficient approach. Streamlit allows you to build interactive web applications directly within the notebook, querying data directly from Snowflake and using Streamlit's built-in plotting capabilities (or integrating with other Python visualization libraries). This avoids pulling large amounts of data into the notebook's environment, which is crucial for large datasets. Option A is inefficient due to the data transfer overhead and limited interactivity. Option B can work but is not as streamlined as using Streamlit within the Snowflake environment. Option C will create static plots only. Option E is overly complex and less efficient than using Streamlit.

NEW QUESTION # 115
You are tasked with training a complex machine learning model using scikit-learn and need to leverage Snowflake's data for training outside of Snowflake using an external function. The training data resides in a Snowflake table named 'CUSTOMER DATA'. Due to data governance policies, you must ensure minimal data movement and secure communication. You choose to implement the external function using AWS Lambda'. Which of the following steps are crucial to achieve secure and efficient model training outside of Snowflake?

A. Create an API integration object in Snowflake that points to your AWS API Gateway endpoint, configured to invoke the Lambda function. This API integration must use a service principal and access roles for secure authentication.
B. Grant usage privilege on the API integration object to the role that will be calling the external function, ensuring only authorized users can trigger the model training.
C. Create an external function in Snowflake that accepts a JSON payload containing the necessary parameters for model training, such as features to use and model hyperparameters. This function will call the API integration to invoke the Lambda function.
D. In the Lambda function, establish a direct connection to the Snowflake database using the Snowflake JDBC driver and Snowflake user credentials stored in the Lambda environment variables. This allows the Lambda function to directly query the 'CUSTOMER DATA' table.
E. Utilize Snowflake's data masking policies on the table to anonymize sensitive information before sending it to the external function for training. This ensures data privacy and compliance with regulations.

Answer: A,B,C

Explanation:
Options A, B, and D are correct. An API integration is essential for securely communicating with the external function via API Gateway, offering authentication and authorization. Granting usage privileges ensures only authorized roles can execute the external function. The external function serves as the bridge between Snowflake and the external service. Option C is incorrect because storing Snowflake credentials directly in Lambda environment variables is a security risk. Option E, while good practice, is not strictly related to the external function configuration for training model.

NEW QUESTION # 116
......

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