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Snowflake SnowPro Advanced: Data Scientist Certification Exam Sample Questions (Q227-Q232):

NEW QUESTION # 227
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. 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.
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. 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.
D. Employing a SQL-based approach using string manipulation functions and keyword extraction techniques to identify important sentences and concatenate them to form a summary.
E. 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.

Answer: A,B

Explanation:
Options A and D are the most appropriate approaches. Snowflake Cortex provides summarization task-specific models that are optimized for performance and cost-effectiveness within the Snowflake environment, Option A utilizes the task-specific model using Snowflake's SNOWFLAKE.ML.PREDICT function. Option D utilizes the 'COMPLETE endpoint. Option B is more complex and resource-intensive, as it requires training a custom model. Option C is less effective because it is hard to implement accurate summarization logic only with SQL. Option E introduces external dependencies and management complexities.

NEW QUESTION # 228
You've built a machine learning model in scikit-learn and want to deploy it to Snowflake for real-time inference. You have the following options for deploying the model. Select all that apply and are considered a best practice for cost and time optimization:

A. Implement a custom microservice that reads data from Snowflake, performs inference using the scikit-learn model, and writes the predictions back to Snowflake.
B. Package the scikit-learn model using 'joblib' or 'pickle' , store it in a Snowflake stage, and create a Snowflake UDF (User-Defined Function) in Python to load the model from the stage and perform inference.
C. Migrate your entire Snowflake data warehouse to a different platform which better supports real-time ML inference.
D. Create a Snowflake external function that calls a cloud-based (AWS SageMaker, Azure Machine Learning, GCP Vertex A1) endpoint for inference, passing the input data to the endpoint and receiving the prediction back.
E. Use Snowflake's Snowpark Python API to directly load the model from a stage and execute inference using Snowpark DataFrames, which will implicitly handle the distributed processing of the data.

Answer: B,E

Explanation:
Options A and B are the recommended approaches. Option A leverages Snowflake UDFs for inference, which minimizes data transfer and leverages Snowflake's compute. Option B, using Snowpark, provides a more seamless integration with Snowflake's distributed processing capabilities. Option C introduces external dependencies and latency. Option D requires managing and maintaining a separate microservice and data transfer and Option E is not viable.

NEW QUESTION # 229
You are performing exploratory data analysis on a large sales dataset in Snowflake using Snowpark. The dataset contains columns such as 'order_id', , and 'profit'. You want to identify the top 5 most profitable products for each month. You have already created a Snowpark DataFrame named 'sales_df. Which of the following Snowpark operations, when combined correctly, will efficiently achieve this?

A. Group by and 'product_id' , aggregate 'sum(profit)' , then use partitioned by ordered by 'sum(profit) DESC'.
B. Use 'ntile(5)' partitioned by ordered by 'sum(profit) DESC' after grouping by and 'product_id', and aggregating 'sum(profit)'.
C. Use 'rank()' partitioned by ordered by 'sum(profit) DESC' , after grouping by and 'product_id' , and aggregating 'sum(profity.
D. First, create a temporary table with aggregated monthly profit for each product using SQL. Then, use Snowpark to read the temporary table and apply a window function partitioned by ordered by 'sum(profit) DESC'.
E. Group by 'product_id', aggregate 'sum(profity, then use partitioned by ordered by 'sum(profit) DESC' within a UDF.

Answer: A

Explanation:
Option A correctly describes the process. First group by month and product to calculate total profit, then use with correct partitioning and ordering to assign a rank within each month based on profit. Options B and C use less efficient ranking functions. Option D groups by product globally, missing the monthly granularity. Option E 'ntile' divides products into 5 buckets which is not what we are looking for.

NEW QUESTION # 230
You are building a real-time fraud detection system using Snowpark ML and Dynamic Tables. The raw transaction data arrives continuously in a Snowflake stream. You need to create a data science pipeline that continuously transforms the data, trains a model, and scores new transactions in near real-time. Which combination of Snowflake features provides the BEST solution for achieving low latency and high throughput for this fraud detection system? Select all that apply:

A. Dynamic Tables to continuously transform the raw transaction data into features required by the model, with 'WAREHOUSE SIZE set to 'X-LARGE to ensure sufficient compute resources.
B. Snowpark ML User-Defined Functions (UDFs) to apply the fraud detection model to incoming transactions, executed using Snowflake's vectorized engine for optimal performance.
C. Scheduled Snowflake tasks to retrain the model every hour based on the most recent transaction data.
D. Snowpipe with Auto-Ingest to load the raw transaction data into a staging table before processing it with Dynamic Tables.
E. Snowflake Tasks with a 'WHEN SYSTEM$STREAM HAS clause to incrementally process new transactions from the stream and update feature tables.

Answer: A,B,E

Explanation:
Options A, B, and C are the best choices for low-latency, high-throughput fraud detection. Option A: Using Snowflake Tasks with the 'WHEN SYSTEM$STREAM HAS DATA()' clause ensures that tasks only run when there is new data in the stream, enabling incremental processing and reducing unnecessary computations. Option B: Dynamic Tables automatically update as the underlying data changes, providing continuously transformed features. Increasing the 'WAREHOUSE SIZE ensures enough compute resources are available. Option C: Snowpark ML UDFs allow you to score incoming transactions in near real-time, leveraging Snowflake's vectorized engine for fast performance. Option D introduces a delay of one hour for model retraining, which is not ideal for a real-time system. Furthermore, regularly scheduling retrain tasks and rerunning them when data is available from stream is not most efficient processing paradigm. While option E is relevant, the question focuses on the transformation, model scoring and data processing parts of a real time data sciecne pipeline, for which A, B, and C are more directly connected.

NEW QUESTION # 231
You are developing a fraud detection model in Snowflake. You've identified that transaction amounts and transaction frequency are key features. You observe that the transaction amounts are heavily right-skewed and the transaction frequencies have outliers. Furthermore, the model needs to be robust against seasonal variations in transaction frequency. Which of the following feature engineering steps, when applied in sequence, would be MOST appropriate to handle these data characteristics effectively?

A. 1. Apply a logarithmic transformation to the transaction amounts. 2. Replace outliers in transaction frequency with the mean value. 3. Create lag features of transaction frequency for the previous 7 days.
B. 1. Apply a logarithmic transformation to the transaction amounts. 2. Apply a Winsorization technique to the transaction frequencies to handle outliers. 3. Calculate a rolling average of transaction frequency over a 7-day window.
C. 1. Apply min-max scaling to the transaction amounts. 2. Remove outliers in transaction frequency using the Interquartile Range (IQR) method. 3. Calculate the cumulative sum of transaction frequencies.
D. 1. Apply a Box-Cox transformation to the transaction amounts. 2. Apply a quantile-based transformation (e.g., using NTILE) to the transaction frequencies to map them to a uniform distribution. 3. Calculate the difference between the current transaction frequency and the average transaction frequency for that day of the week over the past year.
E. 1. Apply a square root transformation to the transaction amounts. 2. Standardize the transaction frequencies using Z-score normalization. 3. Create dummy variables for the day of the week.

Answer: D

Explanation:
Option C is the most comprehensive solution. Box-Cox transformation is effective for skewed data and can handle negative values (if applicable after shifting). Quantile-based transformation maps the transaction frequencies to a uniform distribution, mitigating the impact of outliers. Calculating the difference between the current transaction frequency and the historical average for that day of the week effectively removes seasonality. Logarithmic transformation (A) is a good alternative to Box-Cox but might not be optimal for all skewness types. Winsorization (A) reduces the impact of outliers but doesn't necessarily normalize the data distribution. Standardization (B) is suitable if the data follows a normal distribution, but may not be effective with heavy outliers. Min-max scaling (D) preserves the data distribution, so it is not a remedy for skewed data. Removing outliers (D) can lead to information loss. Replacing outliers with the mean (E) can distort the data distribution.

NEW QUESTION # 232
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