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Snowflake DSA-C03 Exam Syllabus Topics:

Snowflake SnowPro Advanced: Data Scientist Certification Sample Questions:

1. You are building a machine learning model using Snowflake data to predict customer churn. Your dataset includes a 'CUSTOMER TYPE column with the following possible values: 'New', 'Returning', and 'VIP'. You need to perform one-hot encoding on this column. Which of the following Snowflake SQL queries correctly implements one-hot encoding for the 'CUSTOMER TYPE column, creating separate binary columns for each customer type ('IS NEW', 'IS RETURNING', 'IS VIP')?

A) Option E
B) Option D
C) Option B
D) Option A
E) Option C

2. You are tasked with building a Python stored procedure in Snowflake to train a Gradient Boosting Machine (GBM) model using XGBoost.
The procedure takes a sample of data from a large table, trains the model, and stores the model in a Snowflake stage. During testing, you notice that the procedure sometimes exceeds the memory limits imposed by Snowflake, causing it to fail. Which of the following techniques can you implement within the Python stored procedure to minimize memory consumption during model training?

A) Reduce the sample size of the training data and increase the number of boosting rounds to compensate for the smaller sample. Use the 'predict_proba' method to avoid storing probabilities for all classes.
B) Use the 'hist' tree method in XGBoost, enable gradient-based sampling ('gosS), and carefully tune the 'max_depth' and parameters to reduce memory usage during tree construction. Convert all features to numerical if possible.
C) Implement XGBoost's 'early stopping' functionality with a validation set to prevent overfitting. If the stored procedure exceeds the memory limits, the model cannot be saved. Always use larger virtual warehouse.
D) Write the training data to a temporary table in Snowflake, then use Snowflake's external functions to train the XGBoost model on a separate compute cluster outside of Snowflake. Then upload the model to snowflake stage.
E) Convert the Pandas DataFrame used for training to a Dask DataFrame and utilize Dask's distributed processing capabilities to train the XGBoost model in parallel across multiple Snowflake virtual warehouses.

3. A data science team is evaluating different methods for summarizing lengthy customer support tickets using Snowflake Cortex. The goal is to generate concise summaries that capture the key issues and resolutions. Which of the following approaches is/are appropriate for achieving this goal within Snowflake, considering the need for efficiency, cost-effectiveness, and scalability? (Select all that apply)

A) Employing a SQL-based approach using string manipulation functions and keyword extraction techniques to identify important sentences and concatenate them to form a summary.
B) Using the 'SNOWFLAKE.ML.PREDICT' function with a summarization task-specific model provided by Snowflake Cortex, passing the full ticket text as input to generate a summary.
C) Calling the Snowflake Cortex 'COMPLETE' endpoint with a detailed prompt that instructs the model to summarize the support ticket, explicitly specifying the desired summary length and format.
D) Creating a custom summarization model using a transformer-based architecture like BART or T5, training it on a large dataset of support tickets and summaries within Snowflake using Snowpark ML, and then deploying this custom model for generating summaries via a UDF.
E) Developing a Python UDF that leverages a pre-trained summarization model from a library like 'transformers' and deploying it in Snowflake. Managing the model loading and inference within the UDF.

4. You are developing a Snowflake Native App that leverages Snowflake Cortex for text summarization. The app needs to process user-provided text input in real-time and return a summarized version. You want to expose this functionality as a secure and scalable REST API endpoint within the Snowflake environment. Which of the following strategies are MOST suitable for achieving this, considering best practices for security and performance?

A) Develop a Snowflake Native App containing a Python UDF that calls 'SNOWFLAKCORTEX.SUMMARIZE function, and expose it as a REST API endpoint using Snowflake's API Integration feature within the app package.
B) Create a Snowflake External Function using Python that directly calls the 'SNOWFLAKE.CORTEX.SUMMARIZE' function and expose this function via a REST API gateway outside of Snowflake.
C) Write a Snowflake Stored Procedure using Javascript to invoke the 'SNOWFLAKE.CORTEX.SUMMARIZE function, deploy the procedure to a Snowflake stage, and then trigger it via an AWS Lambda function integrated with Snowflake.
D) Utilize a Snowflake Stored Procedure written in SQL that invokes the 'SNOWFLAKE.CORTEX.SUMMARIZE' function, and then create a Snowflake API Integration to expose the stored procedure as a REST endpoint.
E) Develop a Snowflake Native App that includes a Java UDF that calls 'SNOWFLAKE.CORTEX.SUMMARIZE and expose a REST API using Snowflake's built-in REST API capabilities within the Native App framework.

5. You are working with a dataset of customer transaction logs stored in Snowflake. Due to legal restrictions, you are unable to directly access or analyze the entire dataset. However, you can query aggregate statistics. You need to estimate the standard error of the mean transaction amount using bootstrapping. Knowing that you cannot retrieve the individual transaction amounts directly, which of the following approaches, while technically feasible within Snowflake and its stored procedure capabilities, is the least appropriate and potentially misleading application of bootstrapping?

A) Even without individual transaction data, bootstrapping is fundamentally impossible in this scenario, as bootstrapping requires resampling from the original data . All given options are therefore equally inappropriate.
B) Attempt to apply the central limit theorem rather than bootstrapping.
C) Construct a stored procedure that uses the available aggregated statistics (e.g., mean, standard deviation, and sample size) to generate bootstrap samples based on an assumed parametric distribution (e.g., gamma or log-normal) fitted to the data, and then estimate the standard error from these resamples.
D) Use the available aggregate statistics to create many synthetic datasets, all adhering to the same mean, variance, and total sample size. Then, compute the statistic of interest (mean transaction amount) for each of these synthetic datasets, and use this collection to estimate the standard error. This is a valid approach.
E) Develop a stored procedure that generates random samples from a normal distribution with the same mean and standard deviation as the aggregated transaction data available to you, then calculates the standard error of the mean from these synthetic resamples.

Solutions: