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Snowflake DSA-C03 Exam Syllabus Topics:

Snowflake SnowPro Advanced: Data Scientist Certification Sample Questions:

1. 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) Add '@F.sproc' decorator before the function definition.
B) Redefine the function to accept string arguments and cast them to the correct data types within the function.
C) Wrap the Python function inside a stored procedure using @F.sproc' and call that stored procedure instead of the plain python function.
D) Remove argument from 'session.udf.register' call. Snowpark can infer the input types automatically.
E) 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'),

2. You're developing a Python UDTF in Snowflake to perform sentiment analysis on customer reviews. The UDTF uses a pre-trained transformer model from Hugging Face. The code is as follows:

When deploying this UDTF, you encounter a 'ModuleNotFoundError: No module named 'transformers" error. Considering best practices for managing dependencies in Snowflake UDTFs, what is the most effective way to resolve this issue?

A) Create a Conda environment containing the 'transformers' library, package it into a zip file, upload it to a Snowflake stage, and specify the stage path in the 'imports' parameter when registering the UDTF.
B) Install the 'transformers' library directly on the Snowflake compute nodes using Snowpark's 'add_packageS method at the session level:
C) Include the 'transformers' library in the same Python file as the UDTF definition. This is acceptable for smaller libraries.
D) Upload all the dependencies of Transformers (manually downloaded libraries) to the internal stage.
E) Use the 'snowflake-ml-python' library and its dependency management features to automatically resolve and deploy the 'transformers' dependency.

3. 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) 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.
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) 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.
D) Focus solely on rules with very high support (e.g., > 0.1) to ensure statistical significance and avoid overfitting to rare fraudulent events.
E) 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.

4. A data science team is developing a churn prediction model using Snowpark Python. They have a feature engineering pipeline defined as a series of User Defined Functions (UDFs) that transform raw customer data stored in a Snowflake table named 'CUSTOMER DATA'. Due to the volume of data (billions of rows), they need to optimize UDF execution for performance. Which of the following strategies, when applied individually or in combination, will MOST effectively improve the performance of these UDFs within Snowpark?

A) Utilizing vectorized UDFs with NumPy data types wherever possible and carefully tuning batch sizes. Ensure that the input data is already sorted before passing to the UDF.
B) Repartitioning the DataFrame by a key that distributes data evenly across nodes before applying the UDFs, using the method and minimizing data shuffling.
C) Leveraging external functions that call an API endpoint hosted on a cloud provider to perform data transformation. The API endpoint should utilize a serverless architecture.
D) Using temporary tables to store intermediate results calculated by the UDFs instead of directly writing to the target table.
E) Converting Python UDFs to Java UDFs, compiling the Java code, and deploying as a JAR file in Snowflake. Using a larger warehouse size is always the best first option.

5. A data science team is using Snowpark ML to train a classification model. They want to log model metadata (e.g., training parameters, evaluation metrics) and artifacts (e.g., the serialized model file) for reproducibility and model governance purposes. Which of the following approaches is the most appropriate for integrating model logging and artifact management within the Snowpark ML workflow, minimizing operational overhead?

A) Use a custom Python function to manually write model metadata to a Snowflake table and store the model file in a Snowflake stage.
B) Serialize the model object to a string and store it as a VARIANT column in a Snowflake table, alongside the model metadata.
C) Employ a separate, external model management platform (e.g., Databricks MLflow, SageMaker Model Registry) and configure Snowpark to interact with it via API calls during model training and deployment.
D) Leverage the MLflow integration within Snowpark, utilizing its ability to track experiments, log parameters and metrics, and store model artifacts directly within Snowflake stages or external storage.
E) Only track basic model performance metrics in a Snowflake table and rely on code versioning (e.g., Git) for model artifact management.

Solutions: