Relevant Snowflake DSA-C03 Answers - Training DSA-C03 For Exam

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Snowflake SnowPro Advanced: Data Scientist Certification Exam Sample Questions (Q26-Q31):

NEW QUESTION # 26
You are working with a large dataset in Snowflake and need to build a machine learning model using scikit-learn in Python. You want to leverage Snowflake's compute resources for feature engineering to speed up the process. Which of the following approaches correctly combines Snowflake's SQL capabilities with scikit-learn for feature engineering and model training, while minimizing data transfer between Snowflake and the Python environment?

A. Implement the feature engineering steps directly in Python using Pandas and scikit-learn, then load the raw data into a Pandas DataFrame and apply the transformations. Finally, train the scikit-learn model.
B. Write a complex SQL query in Snowmake to perform all feature engineering, then load the resulting features into a Pandas DataFrame and train the scikit-learn model.
C. Use the Snowflake Python Connector to execute individual SQL queries for each feature engineering step. Load the resulting features step-by-step into a Pandas DataFrame and train the scikit-learn model.
D. Use Snowflake external functions to invoke a remote service (e.g., AWS Lambda) for feature engineering. Pass data from Snowflake to the remote service, receive the engineered features back, and load them into a Pandas DataFrame for model training.
E. Create Snowflake User-Defined Functions (UDFs) in Python for complex feature engineering calculations. Call these UDFs within a SQL query to apply the feature engineering to the Snowflake data. Load the resulting features into a Pandas DataFrame and train the scikit-learn model.

Answer: E

Explanation:
Option D is the most efficient approach. Using Snowflake UDFs in Python allows you to perform complex feature engineering directly within Snowflake's compute environment, minimizing the amount of data that needs to be transferred to the Python environment. This reduces network latency and improves performance. Option A may be workable but it would need writing complex SQL queries. Option B will involve a lot of individual interactions between Snowflake and python making this a slower and more complex process. Option C would bring the data out to python before processing it with Pandas and scikit-learn, meaning you'd lose out on the compute of Snowflake. Option E is a viable solution to offload compute to a different compute environment than the python environment and load into a Pandas DataFrame.

NEW QUESTION # 27
A retail company is using Snowflake to store transaction data'. They want to create a derived feature called 'customer _ recency' to represent the number of days since a customer's last purchase. The transactions table 'TRANSACTIONS has columns 'customer_id' (INT) and 'transaction_date' (DATE). Which of the following SQL queries is the MOST efficient and scalable way to derive this feature as a materialized view in Snowflake?

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

Answer: A

Explanation:
Option C is the most efficient because it correctly calculates the number of days since the last transaction using and 'DATEDIFF. The 'OR REPLACE clause ensures that the materialized view can be updated if it already exists. Options A and B are syntactically identical but A is slightly more correct since it considers the MAX. Option D calculates recency from the first transaction, which is incorrect. Option E is similar to option C but less performant since we want datediff on max(transaction_date) and not calculate and take the max over it.

NEW QUESTION # 28
You're a data scientist analyzing sensor data from industrial equipment stored in a Snowflake table named 'SENSOR READINGS' The table includes 'TIMESTAMP' , 'SENSOR ID', 'TEMPERATURE', 'PRESSURE', and 'VIBRATION'. You need to identify malfunctioning sensors based on outlier readings in 'TEMPERATURE' , 'PRESSURE' , and 'VIBRATION'. You want to create a dashboard to visualize these outliers and present a business case to invest in predictive maintenance. Select ALL of the actions that are essential for both effectively identifying sensor outliers within Snowflake and visualizing the data for a business presentation. (Multiple Correct Answers)

A. Create a Snowflake stored procedure to automatically flag outlier readings in a new column 'IS OUTLIER based on a predefined rule set (e.g., IQR method or Z-score threshold), and then use this column to filter data for visualization in a dashboard.
B. Directly connect the 'SENSOR_READINGS' table to a visualization tool and create a 3D scatter plot with 'TEMPERATURE, 'PRESSURE, and 'VIBRATION' on the axes, without any pre-processing or outlier detection in Snowflake.
C. Calculate basic statistical summaries (mean, standard deviation, min, max) for each sensor and each variable C TEMPERATURE, 'PRESSURE, and 'VIBRATION') and use that information to filter down to the most important sensor, prior to using the other techniques.
D. Implement a clustering algorithm (e.g., DBSCAN) within Snowflake using Snowpark Python to group similar sensor readings, identifying outliers as points that do not belong to any cluster or belong to very small clusters.
E. Calculate Z-scores for 'TEMPERATURE, 'PRESSURE, and 'VIBRATION' for each 'SENSOR_ID within a rolling window of the last 24 hours using Snowflake's window functions. Define outliers as readings with Z-scores exceeding a threshold (e.g., 3).

Answer: A,C,D,E

Explanation:
Options A, C, D, and E are essential. A (Z-score calculation with rolling window) provides a dynamic measure of how unusual a reading is relative to recent history for each sensor. C (DBSCAN clustering) helps identify outliers based on density; points far from any cluster are likely outliers. D (Stored procedure with outlier flagging) automates the outlier detection process and makes it easy to filter and visualize outliers in a dashboard, with a business ready explanation. Option E allows you to focus on the right data, allowing you to have a more useful visualisation. Option B (direct 3D scatter plot without pre-processing) is not effective because it will be difficult to identify outliers visually in a high- density scatter plot without any outlier detection or data reduction. The direct scatter plot becomes overwhelming very quickly with sensor data.

NEW QUESTION # 29
You are building a model to predict loan defaults using a dataset stored in Snowflake. After training your model and calculating residuals, you create a scatter plot of the residuals against the predicted values. The plot shows a cone-shaped pattern, with residuals spreading out more as the predicted values increase. Which of the following SQL queries, run within a Snowpark Python session, could be used to address the underlying issue indicated by this residual pattern, assuming the predicted values are stored in a column named and the residuals in a column named 'loan_default_residuar in a Snowflake table named 'loan_predictionds'?

Answer: D

Explanation:
A cone-shaped pattern in the residuals plot (heteroscedasticity) indicates that the variance of the errors is not constant. Applying a transformation like Box-Cox to the target variable before retraining the model (Option D) is the most appropriate way to address this. Option A attempts to filter outliers based on the residuals, but does not address the heteroscedasticity itself and requires statistical functions unavailable within standard SQL. Option B attempts to take the natural log of the residuals, which is nonsensical as residuals can be negative. Option C attempts to filter based on the rank of residuals, which is similarly unhelpful, does not fix the problem, and uses inappropriate outlier removal with SQL QUALIFY clause. Option E scaling the features might sometimes improve model performance, but it does not directly address heteroscedasticity.

NEW QUESTION # 30
You are using Snowflake Cortex to perform sentiment analysis on customer reviews stored in a table called 'CUSTOMER REVIEWS' The table has a column containing the text of each review. You want to create a user-defined function (UDF) to extract sentiment score between the range of -1 to 1 using the 'snowflake_cortex.sentiment' function in Snowflake Cortex. Which of the following UDF definitions would correctly implement this, allowing it to be called directly on the column?

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

Answer: D

Explanation:
The 'snowflake_cortex.sentiment' function returns a VARIANT containing the sentiment score and sentiment label. To extract the sentiment score as a float, you need to access the 'sentiment_score' field and cast it to FLOAT or NUMBER data type using Option D does this correctly, using the Snowflake's preferred casting syntax.Option A return type is incorrect, where it returns the Variant instead of FLOAT. Option B return type is correct but it doesnt cast the result to Float, which is not correct syntax as result is VARIANT. Option C is incorrect because of TO_NUMBER function. Option E result is the SENTIMENT Label instead sentiment score.

NEW QUESTION # 31
......

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Relevant Snowflake DSA-C03 Answers - Training DSA-C03 For Exam

Training Snowflake DSA-C03 For Exam - DSA-C03 Test Objectives Pdf

Snowflake SnowPro Advanced: Data Scientist Certification Exam Sample Questions (Q26-Q31):

Snowflake - DSA-C03 - Professional Relevant SnowPro Advanced: Data Scientist Certification Exam Answers

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