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Snowflake SnowPro Advanced: Data Scientist Certification Exam Sample Questions (Q132-Q137):

NEW QUESTION # 132
You've trained a model using Snowflake ML and want to deploy it for real-time predictions using a Snowflake UDF. To ensure minimal latency, you need to optimize the UDF's performance. Which of the following strategies and considerations are most important when creating and deploying a UDF for model inference in Snowflake to minimize latency, especially when the model is large (e.g., > 100MB)?
Select all that apply.

A. Ensure the UDF code is written in Python and utilizes vectorized operations with libraries like NumPy to process data in batches efficiently.
B. Store the trained model as a BLOB within the UDF code itself to avoid external dependencies.
C. Utilize a Snowflake external function instead of a UDF if the model requires access to resources outside of Snowflake's environment.
D. Use smaller warehouse size for UDF evaluation in order to reduce latency and compute costs.
E. Use a Snowflake Stage to store the model file and load the model within the UDF using 'snowflake.snowpark.files.SnowflakeFile' to minimize memory footprint.

Answer: A,E

Explanation:
Options A and C are the most important strategies. Option A: Vectorized operations in Python using libraries like NumPy can significantly improve the performance of UDFs, especially for large datasets. Option C: Storing the model in a Snowflake Stage and loading it within the UDF helps manage memory usage efficiently, especially when dealing with large models. Option B is not recommended as embedding large BLOB data within UDF code increases UDF size. Option D: External functions introduce additional latency due to the need to communicate with external resources. Option E is incorrect because smaller warehouses may lead to longer processing times.

NEW QUESTION # 133
You've trained a machine learning model using Scikit-learn and saved it as 'model.joblib'. You need to deploy this model to Snowflake. Which sequence of commands will correctly stage the model and create a Snowflake external function to use it for inference, assuming you already have a Snowflake stage named 'model_stage'?

A. Option D
B. Option E
C. Option B
D. Option C
E. Option A

Answer: B

Explanation:

NEW QUESTION # 134
You have a Snowflake table 'PRODUCT_PRICES' with columns 'PRODUCT_ID' (INTEGER) and 'PRICE' (VARCHAR). The 'PRICE' column sometimes contains values like '10.50 USD', '20.00 EUR', or 'Invalid Price'. You need to convert the 'PRICE column to a NUMERIC(10,2) data type, removing currency symbols and handling invalid price strings by replacing them with NULL. Considering both data preparation and feature engineering, which combination of Snowpark SQL and Python code snippets achieves this accurately and efficiently, preparing the data for further analysis?

A. Option D
B. Option E
C. Option B
D. Option C
E. Option A

Answer: B

Explanation:
Option E is the most efficient and accurate approach. It uses F.try_to_decimar directly in Snowpark to convert the cleaned string (after removing currency symbols using to a NUMERIC(10,2) data type. handles invalid price strings by automatically returning NULL. It avoids the overhead of UDFs and complex conditional logic, streamlining the data preparation process. Option A uses an UDF, which is less efficient than using Snowflake's built-in functions. Option B tries to cast to FloatType instead of Numeric(10,2), not meeting the requirements. Option C is similar to Option B but uses 'to_double' , which doesn't directly address the numeric precision requirement. Option D extracts all the digits and tries to do the if the length is greater than zero.

NEW QUESTION # 135
You are a data scientist working with a Snowflake table named 'CUSTOMER TRANSACTIONS' that contains sensitive PII data, including customer names and email addresses. You need to create a representative sample of 1% of the data for model development, ensuring that the sample is anonymized and protects customer privacy. The sample must be reproducible for future model iterations.
Which of the following steps are most appropriate using Snowpark for Python and SQL?

A. Use Snowpark DataFrame's 'sample' function with a fraction of 0.01 and a fixed random seed. Before sampling, create a view that masks 'customer_name' and 'email_address' columns, and then sample from the view.
B. Use the 'SAMPLE clause in a SQL query to extract 1% of the rows, then apply SHA256 hashing to the 'customer_name' and 'email_addresS columns within Snowpark using a UDF. Seed the sampling for reproducibility.
C. Use the 'QUALIFY OVER (ORDER BY RANDOM()) (SELECT COUNT( ) 0.01 FROM CUSTOMER_TRANSACTIONS)' clause with SHA256 on sensitive columns directly within a CREATE TABLE AS statement to generate an anonymized sample. The function should return only 1 percentage of row.
D. Create a new table using 'CREATE TABLE AS SELECT statement combined with 'SAMPLE clause and SHA256 hashing functions in SQL to create the sample and anonymize data. Manually seed the random number generator in Python before executing the SQL statement via Snowpark.
E. Employ stratified sampling based on a customer segment column, then anonymize data. Use the TABLESAMPLE BERNOULLI function in SQL with a 1 percent sample rate. Apply SHA256 hashing to the 'customer_name' and 'email_addresS columns using SQL functions.

Answer: B,E

Explanation:
Options A and D are correct because they address both sampling and anonymization requirements while leveraging Snowflake's capabilities. Option A utilizes SAMPLE clause within a SQL query in Snowflake and then leverages UDF for SHA256 hashing of sensitive information. This is a practical and common data sampling/anonymization pattern. Option D employs stratified sampling based on a customer segment, TABLESAMPLE BERNOULLI and SHA256 hashing in SQL, which provides a solid anonymization and sampling strategy.Option B: Creating a view is a good practice, but it doesn't automatically anonymize the data, and directly sampling from the view without anonymization doesn't meet the security requirements. Option C: Manually seeding in Python doesn't guarantee reproducibility when SQL is executed separately, as Snowflake has its own random number generator. Option E does not guarantee reproduciblity, and the query complexity might introduce performance issues and is less readable compared to the other options.

NEW QUESTION # 136
You are training a fraud detection model on a dataset containing millions of transactions. To ensure robust generalization, you've decided to implement a train-validation-holdout split using Snowflake's capabilities. Given the following requirements: Temporal Split: The dataset contains a 'transaction date' column. You want to ensure that the validation and holdout sets contain transactions after the training data'. This is crucial because fraud patterns evolve over time. Stratified Sampling (Within Training): The training set should maintain the original proportion of fraudulent vs. non-fraudulent transactions. The column indicates if a transaction is fraudulent (1) or not (0). Deterministic Splits: You need a repeatable process to ensure consistency across model iterations. Which of the following SQL code snippets best achieves these requirements, considering performance and best practices within Snowflake?

A. Option D
B. Option E
C. Option B
D. Option C
E. Option A

Answer: B

Explanation:
Option E is the most efficient and accurate. It correctly splits data based on 'transaction_date' into train, validation, and holdout sets. Critically, it uses 'SAMPLE ROW(... USING HASH_AGG(is_fraud))' to perform stratified sampling on the training set. The REPEATABLE(123)' clause guarantees deterministic and reproducible splits. Other Options are not correct because Option A does not address stratified sampling.Option B uses SAMPLE without hash aggregation for stratification, which is incorrect for maintaining proportions.Option C does not use ' REPEATABLE for deterministic splitting. Option DOS attempt at stratification is overly complex and inefficient, and the count calculation will likely be incorrect.

NEW QUESTION # 137
......

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