Snowflake SnowPro Advanced: Data Scientist Certification : DSA-C03

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Snowflake DSA-C03 Exam Syllabus Topics:

Snowflake SnowPro Advanced: Data Scientist Certification Sample Questions:

1. You are deploying a time series forecasting model in Snowflake. You need to log the performance metrics (e.g., MAE, RMSE) of the model after each prediction run to the Snowflake Model Registry. Which of the following steps are necessary to achieve this?

A) Use the method with the 'metrics' parameter to log the metrics directly during model registration.
B) Use the method to log individual metrics to the Model Registry associated with a specific model version after the prediction run.
C) Leverage Snowflake's Event Tables to capture and store metrics data generated during model evaluation and prediction workflows and then access via stored procedures that log to the Model Registry.
D) You must create a custom logging solution outside of Snowflake using external services and then integrate those logs back into Snowflake via external functions and Model Registry APIs
E) Create a separate table in Snowflake to store the performance metrics and use SQL "INSERT statements to log the metrics after each prediction run.

2. You are working on a fraud detection model and need to prepare transaction data'. You have two tables: 'transactions' (transaction_id, customer_id, transaction_date, amount, merchant_id) and (merchant_id, city, state). You need to perform the following data cleaning and feature engineering steps using Snowpark: 1. Remove duplicate transactions based on 'transaction_id'. 2.
Join the 'transactions' table with the 'merchant_locations table to add city and state information to each transaction. 3. Create a new feature called 'amount_category' based on the transaction amount, categorized as 'Low', 'Medium', or 'High'. 4. The categorization thresholds are defined as follows: 'LoW: amount < 50 'Medium': 50 amount < 200 'High': amount >= 200 Which of the following statements about performing these operations using Snowpark are accurate?

A) Removing duplicate transactions can be efficiently done using the method on the Snowpark DataFrame, specifying 'transaction_id' as the subset. Creating the amount categories can be completed using the 'when' clause with multiple 'otherwise' clauses.
B) A LEFT JOIN should be used to join the 'transactions' and 'merchant_location' tables to ensure that all transactions are included, even if some merchant IDs are not present in the 'merchant_location' table.
C) The construct in Snowpark can be used to create the 'amount_category' feature directly within the DataFrame transformation without needing a UDF
D) Removing duplicate transactions can be efficiently done using the method on the Snowpark DataFrame, specifying 'transaction_id' as the subset. Creating the amount categories requires use of a User-Defined Function (UDF) as the logic can't be efficiently embedded in a single 'when' clause.
E) You can register SQL UDF to calculate the 'amount_category' using 'CASE WHEN' statement

3. You've built a complex machine learning model using scikit-learn and deployed it as a Python UDF in Snowflake. The UDF takes a JSON string as input, containing several numerical features, and returns a predicted probability However, you observe significant performance issues, particularly when processing large batches of data'. Which of the following approaches would be MOST effective in optimizing the performance of this UDF in Snowflake?

A) Pre-process the input data outside of the UDF using SQL transformations, reducing the amount of data passed to the UDF and simplifying the Python code.
B) Rewrite the UDF in Java or Scala to leverage the JVM's performance advantages over Python in Snowflake.
C) Use Snowflake's vectorized UDF feature to process data in micro-batches, minimizing the overhead of repeated Python interpreter initialization.
D) Serialize the scikit-learn model using 'joblib' instead of 'pickle' for potentially faster deserialization within the UDF.
E) Increase the warehouse size to improve the overall compute resources available for UDF execution.

4. 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) 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.
B) 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.
C) Employing a SQL-based approach using string manipulation functions and keyword extraction techniques to identify important sentences and concatenate them to form a summary.
D) 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.
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.

5. You're developing a model to predict customer churn using Snowflake. Your dataset is large and continuously growing. You need to implement partitioning strategies to optimize model training and inference performance. You consider the following partitioning strategies: 1. Partitioning by 'customer segment (e.g., 'High-Value', 'Medium-Value', 'Low-Value'). 2. Partitioning by 'signup_date' (e.g., monthly partitions). 3. Partitioning by 'region' (e.g., 'North America', 'Europe', 'Asia'). Which of the following statements accurately describe the potential benefits and drawbacks of these partitioning strategies within a Snowflake environment, specifically in the context of model training and inference?

A) Partitioning by 'region' is useful if churn is heavily influenced by geographic factors (e.g., local market conditions). It can improve query performance during both training and inference when filtering by region. However, it can create data silos, making it difficult to build a global churn model that considers interactions across regions. Furthermore, the 'region' column must have low cardinality.
B) Using clustering in Snowflake on top of partitioning will always improve query performance significantly and reduce compute costs irrespective of query patterns.
C) Partitioning by 'customer_segment' is beneficial if churn patterns are significantly different across segments, allowing for training separate models for each segment. However, if any segment has very few churned customers, it may lead to overfitting or unreliable models for that segment.
D) Partitioning by 'signup_date' is ideal for capturing temporal dependencies in churn behavior and allows for easy retraining of models with the latest data. It also naturally aligns with a walk-forward validation approach. However, it might not be effective if churn drivers are independent of signup date.
E) Implementing partitioning requires modifying existing data loading pipelines and may introduce additional overhead in data management. If the cost of partitioning outweighs the performance gains, it's better to rely on Snowflake's built-in micro-partitioning alone. Also, data skew in partition keys is a major concern.

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