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Exam4Labs Snowflake DSA-C03 desktop practice exam software is usable on Windows computers without an active internet connection. It creates the complete scenario of the SnowPro Advanced: Data Scientist Certification Exam (DSA-C03) real test through its multiple mock tests. Our practice software contains all the questions which you will encounter in the Snowflake final test.

Snowflake SnowPro Advanced: Data Scientist Certification Exam Sample Questions (Q35-Q40):

NEW QUESTION # 35
Which of the following statements are TRUE regarding the 'Data Understanding' and 'Data Preparation' steps within the Machine Learning lifecycle, specifically concerning handling data directly within Snowflake for a large, complex dataset?

A. The 'Data Understanding' step is unnecessary when working with data stored in Snowflake because Snowflake automatically validates and cleans the data during ingestion.
B. Data Understanding primarily involves identifying potential data quality issues like missing values, outliers, and inconsistencies, and Snowflake features like 'QUALIFY and 'APPROX TOP can aid in this process.
C. Data Preparation in Snowflake can involve feature engineering using SQL functions, creating aggregated features with window functions, and handling missing values using 'NVL' or 'COALESCE. Furthermore, Snowpark Python provides richer data manipulation using DataFrame APIs directly on Snowflake data.
D. Data Preparation should always be performed outside of Snowflake using external tools to avoid impacting Snowflake performance.
E. During Data Preparation, you should always prioritize creating a single, wide table containing all possible features to simplify the modeling process.

Answer: B,C

Explanation:
Data Understanding is crucial for identifying data quality issues using tools such as 'QUALIFY' and 'APPROX TOP Data Preparation within Snowflake using SQL and Snowpark Python enables efficient feature engineering and data cleaning. Option C is incorrect because Snowflake doesn't automatically validate and clean your data. Option D is incorrect as leveraging Snowflake's compute for data preparation alongside Snowpark can drastically increase speed. Option E is not desirable, feature selection is important, and feature stores help in organization.

NEW QUESTION # 36
You are developing a data transformation pipeline in Python that reads data from Snowflake, performs complex operations using Pandas DataFrames, and writes the transformed data back to Snowflake. You've implemented a function, 'transform data(df)', which processes a Pandas DataFrame. You want to leverage Snowflake's compute resources for the DataFrame operations as much as possible, even for intermediate transformations before loading the final result. Which of the following strategies could you employ to optimize this process, assuming you have a configured Snowflake connection "conn"?

A. Use Snowpark Python DataFrame API to perform the transformation directly on Snowflake's compute and then load results into the same table. Call 'df_snowpark = session.create_dataframe(df)'.
B. Read the entire Snowflake table into a single Pandas DataFrame, apply , and then write the entire transformed DataFrame back to Snowflake.
C. Chunk the Snowflake table into smaller DataFrames using 'fetchmany()' , apply to each chunk, and then append each transformed chunk to a Snowflake table using multiple INSERT statements. Call columns=[col[0] for col in cur.description]))'
D. Use 'snowflake.connector.pandas_tools.write_pandas(conn, df, table_name, auto_create_table=Truey to write the transformed DataFrame to Snowflake and let Snowflake handle the transformations using SQL.
E. Create a series of Snowflake UDFs that perform the individual transformations within Snowflake, load the data into Pandas DataFrames, apply UDFs on these DataFrames, and use to upload to Snowflake.

Answer: A

Explanation:
Snowpark for Python is specifically designed to push down DataFrame operations to the Snowflake engine for execution. Option C directly leverages Snowflake's compute resources for DataFrame transformations by creating a Snowpark DataFrame. Option A is inefficient as it loads the entire dataset into memory and performs transformations locally. Option B directly only handles write function . Option D involves manual chunking and multiple INSERT statements, which is slow and inefficient. Option E is overly complex and doesn't fully utilize Snowflake's capabilities; Snowpark provides a more seamless and efficient way to express DataFrame transformations within Snowflake. Using Snowpark eliminates the need for data transfer between Python environment and Snowflake for intermediate transformations which is more efficient and scalable for Data Scientist (DSA-C03) Certification Exam Model Development.

NEW QUESTION # 37
You've built a regression model in Snowflake using Snowpark Python to predict customer churn. After evaluating the model on a holdout dataset, you generate a residuals plot. The plot shows a distinct 'U' shape. Which of the following interpretations and subsequent actions are most appropriate?

A. The 'U' shape indicates homoscedasticity. No changes to the model are necessary.
B. The 'U' shape implies multicollinearity is present. Use techniques like Variance Inflation Factor (VIF) to identify and remove highly correlated features.
C. The 'U' shape suggests the model is missing important non-linear relationships. Consider adding polynomial features or using a non-linear model like a Random Forest or Gradient Boosting Machine.
D. The 'U' shape indicates that the residuals are normally distributed. This is a positive sign and no changes are required.
E. The 'U' shape suggests that the learning rate is too high. Reduce the learning rate of the model.

Answer: C

Explanation:
A 'U' shaped residuals plot indicates heteroscedasticity and a non-linear relationship is not being captured by the model. Options B is correct because adding polynomial features or using a non-linear model can help capture this relationship. The other options are incorrect because they misinterpret the meaning of the 'U' shape in the residuals plot or propose inappropriate remedies.

NEW QUESTION # 38
You are building an automated model retraining pipeline for a sales forecasting model in Snowflake using Snowflake Tasks and Stored Procedures. After retraining, you want to validate the new model against a champion model already deployed. You need to define a validation strategy using the following models: champion model deployed as UDF "FORECAST UDF , and contender model deployed as UDF 'FORECAST UDF NEW'. Given the following objectives: (1) Minimal impact on production latency, (2) Ability to compare predictions on a large volume of real-time data, (3) A statistically sound comparison metric. Which of the following SQL statements best represents how to efficiently compare the forecasts of the two models on a sample dataset and calculate the Root Mean Squared Error (RMSE) to validate the new model?

Answer: B

Explanation:
Option E is the best approach. It samples the data using 'SAMPLE BERNOULLI(IO)' for minimal impact on production. Then, it calculates both the challenger RMSE (new model) and the champion RMSE on this sample data. This provides a direct comparison of the model performance against actual sales and also allows to minimise runtime to compute this metric compared to option C which computes a difference without evaluating if the new model has a better score. Sampling helps with minimal impact while comparison metric in this case needs the actual_sales column. This provides a statistically relevant comparison within Snowflake, minimizing external processing. Option A does not compare the model to the ground truth (actual sales). Option B only compares the challenger and champion models' predictions against each other on a small, limited dataset (1000 records), which may not be representative. Options C calculates the RMSE difference directly and has a SAMPLE size of 1, which is unlikely to reflect the reality and Option D filters based on RMSE, which makes the approach bias and makes it harder to evalute if the RMSE is statistically significant.

NEW QUESTION # 39
A data scientist is developing a model within a Snowpark Python environment to predict customer churn. They have established a Snowflake session and loaded data into a Snowpark DataFrame named 'customer data'. The feature engineering pipeline requires a custom Python function, 'calculate engagement_score', to be applied to each row. This function takes several columns as input and returns a single score representing customer engagement. The data scientist wants to apply this function in parallel across the entire DataFrame using Snowpark's UDF capabilities. The following code snippet is used to define and register the UDF:

When the UDF is called the above error is observed. What change needs to be applied to make the UDF work as expected?

A. Redefine the function to accept string arguments and cast them to the correct data types within the function.
B. Change the function call to use the Snowpark DataFrame's 'select' function with column objects: 'customer_data.select(engagement_score_udf(F.col('num_transactions'), F.col('avg_transaction_value'),
C. Wrap the Python function inside a stored procedure using @F.sproc' and call that stored procedure instead of the plain python function.
D. Add '@F.sproc' decorator before the function definition.
E. Remove argument from 'session.udf.register' call. Snowpark can infer the input types automatically.

Answer: B

Explanation:
The error message 'UDFArgumentException: Invalid argument types for function 'calculate_engagement_score_udf. Expected arguments: ONT, FLOAT, INT], actual arguments: [COLUMN_NAME, COLUMN_NAME, COLUMN_NAME]' indicates that the UDF is receiving column objects instead of the actual data values. This is because when calling the UDF on a Snowpark DataFrame, you need to explicitly reference the columns using The correct way to apply the UDF to the DataFrame is to use the 'select' function with ' F.col()' to pass the column objects as arguments to the UDF.

NEW QUESTION # 40
......

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