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Snowflake DSA-C03 Exam Syllabus Topics:

Snowflake SnowPro Advanced: Data Scientist Certification Sample Questions:

1. 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) 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.
B) 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.
C) 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.
D) Using temporary tables to store intermediate results calculated by the UDFs instead of directly writing to the target table.
E) Repartitioning the DataFrame by a key that distributes data evenly across nodes before applying the UDFs, using the method and minimizing data shuffling.

2. You are developing a machine learning model using scikit-learn within Visual Studio Code (VS Code) and connecting directly to Snowflake to access a large dataset. You need to authenticate to Snowflake using Key Pair Authentication, but want to avoid storing the private key directly within your VS Code project or environment variables for security reasons. Which of the following approaches offers the MOST secure way to manage and access the private key for Snowflake authentication from VS Code?

A) Use the Snowflake CLI to generate a temporary access token and hardcode it into your VS Code script for authentication.
B) Store the private key in a password-protected ZIP archive and extract it during the Snowflake connection process.
C) Store the private key in a secure database table within Snowflake and query it dynamically.
D) Store the private key in a secure vault (e.g., HashiCorp Vault, AWS Secrets Manager, Azure Key Vault) and retrieve it dynamically within your VS Code script using the appropriate API or SDK.
E) Store the encrypted private key in a configuration file within your VS Code project and decrypt it at runtime using a password-based encryption algorithm.

3. You are tasked with deploying a time series forecasting model within Snowflake using Snowpark Python. The model requires significant pre-processing and feature engineering steps that are computationally intensive. These steps include calculating rolling statistics, handling missing values with imputation, and applying various transformations. You aim to optimize the execution time of these pre- processing steps within the Snowpark environment. Which of the following techniques can significantly improve the performance of your data preparation pipeline?

A) Convert the Snowpark DataFrame to a Pandas DataFrame using and perform all pre-processing operations using Pandas functions before loading the processed data back to Snowflake.
B) Ensure that all data used is small enough to fit within the memory of the client machine running the Snowpark Python script, thus removing the need for distributed computing.
C) Utilize Snowpark's vectorized UDFs and DataFrame operations to leverage Snowflake's distributed computing capabilities.
D) Write the feature engineering logic directly in SQL and create a view. Use the Snowpark DataFrame API to query the view, avoiding Python code execution within Snowpark.
E) Force single-threaded execution by setting to avoid overhead associated with parallel processing.

4. You are training a regression model to predict house prices using a Snowflake dataset. The dataset contains various features, including 'number of_bedrooms', , and You want to use time-based partitioning for your training, validation, and holdout sets. However, you also need to ensure that the dataset is properly shuffled within each time partition to mitigate potential bias introduced by the order of data entry. Which of the following strategies is MOST EFFECTIVE and EFFICIENT for partitioning your data into train, validation, and holdout sets in Snowflake, while also ensuring random shuffling within each partition, and addressing potential data leakage issues?

A) Create separate views for train, validation, and holdout sets, filtering by 'sale_date' . Shuffle the entire dataset using 'ORDER BY RANDOM()' before creating the views to ensure randomness across all sets. This does not address shuffling within parition.
B) Use Snowflake's SAMPLE clause with a 'REPEATABLE seed for each split (train, validation, holdout), filtering by 'sale_date'. Add an 'ORDER BY RANDOM()' clause within each 'SAMPLE query to shuffle the data within each split. This approach does not guarantee non-overlapping sets and can introduce sampling bias.
C) Create a new column 'split_group' using a CASE statement based on 'sale_date' to assign each row to 'train', 'validation', or 'holdout'. Calculate a random number within each 'split_group' by using OVER (PARTITION BY split_group ORDER BY RANDOM())'. Then create temporary tables for each split using 'CREATE TABLE AS SELECT FROM WHERE split_group = QUALIFY ROW NUMBER() OVER (ORDER BY RANDOM()) (SELECT COUNT( ) FROM transactions WHERE split_group -- ...) (respective split percentage);'
D) Create a new column 'split_group' using a CASE statement based on 'sale_date' to assign each row to 'train', 'validation', or 'holdout'. Then, create temporary tables for each split using 'CREATE TABLE AS SELECT FROM WHERE split_group = ORDER BY RANDOM()'. This can be very slow because of global RANDOM sort and leakage issues with using full dataset for randomness.
E) Create a user-defined function (UDF) in Python that takes a 'sale_date' as input and returns either 'train', 'validation', or 'holdout' based on pre-defined date ranges. Apply this UDF to each row, creating a 'split_group' column. Then, create temporary tables for each split using 'CREATE TABLE AS SELECT ... FROM . WHERE split_group = ... ORDER BY RANDOM()'. UDF overhead and global RANDOM sort make it very slow.

5. You are building a machine learning model using Snowpark Python to predict house prices. The dataset contains a feature column named 'location' which contains free-form text descriptions of house locations. You want to leverage a pre-trained Large Language Model (LLM) hosted externally to extract structured location features like city, state, and zip code from the free-form text within Snowpark. You want to minimize the data transferred out of Snowflake. Which approach is most efficient and secure?

A) Create a Snowflake External Function that calls the external LLM API. Pass the 'location' column data to the External Function and retrieve the structured location features. Then apply the External Function directly on the Snowpark DataFrame.
B) Use to load the 'location' column data into a Pandas DataFrame, call the external LLM API in your Python script to enrich the location data and then use to store the enriched data back into a Snowflake table.
C) Use Snowpark's 'createOrReplaceStage' to create an external stage pointing to the LLM API endpoint. Load the 'location' data into this stage and call the LLM API directly from the Snowflake stage using SQL.
D) Create a Snowpark User-Defined Function (UDF) that calls the external LLM API. Pass the 'location' column data to the UDF and retrieve the structured location features. Then apply the UDF directly on the Snowpark DataFrame.
E) Use the Snowflake Connector for Python to directly query the 'location' column and call the external LLM API from the connector. Then write the updated data into a new table.

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