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Snowflake DSA-C03 Exam Syllabus Topics:

Snowflake SnowPro Advanced: Data Scientist Certification Sample Questions:

1. You are working with a dataset in Snowflake containing customer reviews stored in a 'REVIEWS' table. The 'SENTIMENT SCORE column contains continuous values ranging from -1 (negative) to 1 (positive). You need to create a new column, 'SENTIMENT CATEGORY, based on the following rules: 'Negative': 'SENTIMENT SCORE < -0.5 'Neutral': -0.5 'SENTIMENT SCORE 0.5 'Positive': 'SENTIMENT SCORE > 0.5 You also want to binarize this 'SENTIMENT CATEGORY column into three separate columns: 'IS NEGATIVE, 'IS NEUTRAL', and 'IS POSITIVE. Which of the following SQL statements correctly implements both the categorization and subsequent binarization?

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

2. You are working with a large dataset of sensor readings stored in a Snowflake table. You need to perform several complex feature engineering steps, including calculating rolling statistics (e.g., moving average) over a time window for each sensor. You want to use Snowpark Pandas for this task. However, the dataset is too large to fit into the memory of a single Snowpark Pandas worker. How can you efficiently perform the rolling statistics calculation without exceeding memory limits? Select all options that apply.

A) Utilize the 'window' function in Snowpark SQL to define a window specification for each sensor and calculate the rolling statistics using SQL aggregate functions within Snowflake. Leverage Snowpark to consume the results of the SQL transformation.
B) Increase the memory allocation for the Snowpark Pandas worker nodes to accommodate the entire dataset.
C) Explore using Snowpark's Pandas user-defined functions (UDFs) with vectorization to apply custom rolling statistics logic directly within Snowflake. UDFs allow you to use Pandas within Snowflake without needing to bring the entire dataset client-side.
D) Use the 'grouped' method in Snowpark DataFrame to group the data by sensor ID, then download each group as a Pandas DataFrame to the client and perform the rolling statistics calculation locally. Then upload back to Snowflake.
E) Break the Snowpark DataFrame into smaller chunks using 'sample' and 'unionAll', process each chunk with Snowpark Pandas, and then combine the results.

3. You have successfully deployed a real-time prediction service using Snowpark Container Services, consuming events from a Kafka topic. The service leverages a large language model (LLM) stored in the Snowflake Model Registry. You observe that inference latency is high and the service is struggling to keep up with the incoming event rate. You need to optimize the service for higher throughput and lower latency. Which of the following actions, when implemented together, would most effectively improve the performance of your Snowpark Container Services deployment?

A) Enable autoscaling for the service based on CPU utilization. Remove all logging statements from the containerized application to reduce 1/0 overhead.
B) Switch to a smaller, less accurate LLM. Increase the 'container.resources.cpu' allocation for the service. Ensure data is pre-processed before sending to kafka.
C) Increase the 'container.resources.memory' allocation for the service. Implement caching of frequently accessed data within the containerized application.
D) Implement custom monitoring solution outside of snowflake and determine bottleneck of your application. Increase the container.resources.gpu allocation for the service.
E) Increase the number of replicas for the service. Implement batching within the containerized application to process multiple events in a single inference call.

4. A data scientist is tasked with predicting house prices using Snowflake. They have a dataset stored in a Snowflake table called 'HOUSE PRICES' with columns such as 'SQUARE FOOTAGE, 'NUM BEDROOMS, 'LOCATION_ID, and 'PRICE. They choose a Random Forest Regressor model. Which of the following steps is MOST important to prevent overfitting and ensure good generalization performance on unseen data, and how can this be effectively implemented within a Snowflake-centric workflow?

A) Increase the number of estimators (trees) in the Random Forest to the maximum possible value to capture all potential patterns, without cross validation.
B) Eliminate outliers without understanding the data properly to reduce noise.
C) Tune the hyperparameters of the Random Forest model (e.g., 'max_deptm, 'n_estimators') using cross-validation. You can achieve this by splitting the 'HOUSE PRICES table into training and validation sets using Snowflake's 'QUALIFY clause or temporary tables, then train and evaluate the model within a loop or stored procedure.
D) Randomly select a small subset of the features (e.g., only use 'SQUARE FOOTAGE and 'NUM BEDROOMS) to simplify the model and prevent overfitting.
E) Train the Random Forest model on the entire 'HOUSE PRICES table without splitting into training and validation sets, as this will provide the model with the most data.

5. A data scientist is preparing customer churn data for a machine learning model in Snowflake. The dataset contains a 'Contract_Type' column with values 'Month-to-Month', 'One Year', and 'Two Year'. They want to use label encoding to transform this categorical feature into numerical values. Which of the following SQL statements correctly implements label encoding for the 'Contract_Type' column, assigning 'Month-to-Month' to 0, 'One Year' to 1, and 'Two Year' to 2, and creates a new column named 'Contract_Type_Encoded'? Additionally, the data scientist wants to handle potential NULL values in 'Contract_Type' by assigning them the value of -1.

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

Solutions: