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Snowflake DSA-C03 Exam Syllabus Topics:

Snowflake SnowPro Advanced: Data Scientist Certification Sample Questions:

1. A data scientist is analyzing website traffic data stored in Snowflake. The data includes daily page views for different pages. The data scientist suspects that the variance of page views for a particular page, 'home', has significantly increased recently. Which of the following steps and Snowflake SQL queries could be used to identify a potential change in the variance of 'home' page views over time (e.g., comparing variance before and after a specific date)? Select all that apply.

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

2. You are building a time-series forecasting model in Snowflake to predict the hourly energy consumption of a building. You have historical data with timestamps and corresponding energy consumption values. You've noticed significant daily seasonality and a weaker weekly seasonality. Which of the following techniques or approaches would be most appropriate for capturing both seasonality patterns within a supervised learning framework using Snowflake?

A) Decomposing the time series using STL (Seasonal-Trend decomposition using Loess) and building separate models for the trend and seasonal components, then combining the predictions.
B) Using a simple moving average to smooth the data before applying a linear regression model.
C) Using Fourier terms (sine and cosine waves) with frequencies corresponding to daily and weekly cycles as features in a regression model.
D) Applying exponential smoothing directly to the original time series without feature engineering.
E) Creating lagged features (e.g., energy consumption from the previous hour, the same hour yesterday, and the same hour last week) and using these features as input to a regression model (e.g., Random Forest or Gradient Boosting).

3. You are tasked with feature engineering a dataset containing customer transaction data stored in a Snowflake table named 'CUSTOMER TRANSACTIONS'. This table includes columns like 'CUSTOMER ID', 'TRANSACTION DATE, and 'TRANSACTION AMOUNT. You need to create a new feature representing the 'Recency' of the customer, which is the number of days since their last transaction. Using Snowpark Pandas, which of the following code snippets will correctly calculate the Recency feature as a new column in a Snowpark DataFrame?

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

4. A data scientist at 'Polaris Analytics' wants to estimate the average transaction value of all online purchases made during the Black Friday sale. Due to the enormous volume of data in Snowflake, they decide to use the Central Limit Theorem (CLT). They randomly sample 1000 transactions daily for 30 days and calculate the sample mean for each day. The sample mean values are stored in a Snowflake table named Which of the following SQL queries, assuming the table has a column of 'FLOAT' type, will provide the best estimate of the population mean and its confidence interval using the CLT?

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

5. You are deploying a fraud detection model using Snowpark Container Services. The model requires a substantial amount of GPU memory. After deploying your service, you notice that it frequently crashes due to Out-Of-Memory (OOM) errors. You have verified that the container image itself is not the source of the problem. Which of the following strategies are most appropriate to mitigate these OOM errors when using Snowpark Container Services, assuming you want to minimize costs and complexity?

A) Utilize CPU-based inference instead of GPU-based inference, as CPU inference is generally less memory-intensive. Convert the model to a format optimized for CPU inference (e.g., using ONNX). Reduce the 'container.resources.cpu' count.
B) Implement a mechanism within your model's inference code to explicitly free up unused memory after each prediction. Use Python's 'gc.collect()' and ensure proper cleanup of large data structures. Configure a smaller 'container.resources.memory' allocation.
C) Implement model parallelism across multiple containers, splitting the model's workload and data across them. Configure each container with a smaller 'container.resources.memory' allocation.
D) Increase the 'container.resources.memory' configuration setting in the service definition to a value significantly larger than the model's memory footprint. Monitor memory utilization and adjust as needed.
E) Ignore OOM errors and rely on the container service to automatically restart the container. The model will eventually process all requests.

Solutions: