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Snowflake SnowPro Advanced: Data Scientist Certification Exam Sample Questions (Q142-Q147):

NEW QUESTION # 142
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. Increase the memory allocation for the Snowpark Pandas worker nodes to accommodate the entire dataset.
B. Break the Snowpark DataFrame into smaller chunks using 'sample' and 'unionAll', process each chunk with Snowpark Pandas, and then combine the results.
C. 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.
D. 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.
E. 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.

Answer: C,D

Explanation:
Explanation:Options B and D are the most appropriate and efficient solutions for handling large datasets when calculating rolling statistics with Snowpark Pandas. Option B uses the 'window' function in Snowpark SQL. Leverage 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. Option D uses Snowpark's Pandas UDFs. Snowpark's Pandas UDFs with vectorization allow you to bring the processing logic to the data within Snowflake, avoiding the need to move the entire dataset to the client-side and bypassing memory limitations. This approach is generally more scalable and performant for large datasets. Option A is inefficient as it retrieves groups of data from Snowflake to client side before creating the calculations before sending back to snowflake. Option C is correct but complex and not optimal. Option E is possible, but it's not a scalable solution and can be costly.

NEW QUESTION # 143
You have a Snowflake Model Registry set up and are managing multiple versions of a machine learning model. You want to programmatically retrieve a specific version of the model and load it for inference within a Snowflake Snowpark Python UDE Assume your registry name is 'my_registry', the model name is 'credit risk_model', and you want to retrieve version 'v2'. How would you achieve this using Snowpark Python?

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

Answer: D

Explanation:
Option A correctly uses the method to directly load the model into memory for inference. This is the intended method for retrieving and using models managed by the Snowflake Model Registry. Option B uses 'joblib.load' which bypasses the Model Registry completely after getting the path. Option C is suitable if the model was trained using MLFlow, not generic scikit learn. Option D is an imaginary command not present in Model Registry and Option E involves calling udf to load and that is not right way to programatically load the model from registry and do inference with it.

NEW QUESTION # 144
You're developing a model to predict equipment failure using sensor data stored in Snowflake. The dataset is highly imbalanced, with failure events (positive class) being rare compared to normal operation (negative class). To improve model performance, you're considering both up-sampling the minority class and down-sampling the majority class. Which of the following statements regarding the potential benefits and drawbacks of combining up-sampling and down-sampling techniques in this scenario are TRUE? (Select TWO)

A. The optimal sampling ratio for both up-sampling and down-sampling must always be 1:1, regardless of the initial class distribution.
B. Combining up-sampling and down-sampling can lead to a more balanced dataset, potentially improving the model's ability to learn patterns from both classes without introducing excessive bias from solely up-sampling.
C. Down-sampling, when combined with up-sampling, can exacerbate the risk of losing important information from the majority class, leading to underfitting, especially if the majority class is already relatively small.
D. Over-sampling, combined with downsampling, makes the model more prone to overfitting since this causes the model to train on a large dataset.
E. Using both up-sampling and down-sampling always guarantees improved model performance compared to using only one of these techniques, regardless of the dataset characteristics.

Answer: B,C

Explanation:
Option A is correct: Combining both techniques can lead to a more balanced dataset, potentially improving the model's ability to learn patterns from both classes, if done correctly. Option C is correct: Down-sampling can exacerbate the risk of losing important information from the majority class, potentially leading to underfitting, especially if the majority class is already relatively small. Option B is incorrect because the effect depends on the data. Option D is incorrect because oversampling helps the model, even combined with downsampling, not to be prone to overfitting. Option E is incorrect because the right up/down-sampling ratio is very specific to the dataset.

NEW QUESTION # 145
You are tasked with developing a Snowpark Python function to identify and remove near-duplicate text entries from a table named 'PRODUCT DESCRIPTIONS. The table contains a 'PRODUCT ONT) and 'DESCRIPTION' (STRING) column. Near duplicates are defined as descriptions with a Jaccard similarity score greater than 0.9. You need to implement this using Snowpark and UDFs. Which of the following approaches is most efficient, secure, and correct to implement?

A. Define a Python UDF that calculates the Jaccard similarity. Create a new table, 'PRODUCT DESCRIPTIONS NO DUPES , and insert the distinct descriptions based on the similarity score. Rows in the original table with similar product description must be inserted with lowest product id into new table.
B. Define a Python UDF that calculates the Jaccard similarity. Use 'GROUP BY to group descriptions by the 'PRODUCT ID. Apply the UDF on this grouped data to remove duplicates with similarity score greater than threshold.
C. Define a Python UDF that calculates the Jaccard similarity between all pairs of descriptions in the table. Use a cross join to compare all rows, then filter based on the Jaccard similarity threshold. Finally, delete the near-duplicate rows based on a chosen tie-breaker (e.g., smallest PRODUCT_ID).
D. Use the function directly in a SQL query without a UDF. Partition the data by 'PRODUCT_ID' and remove near duplicates where the approximate Jaccard index is above 0.9.
E. Define a Python UDF to calculate Jaccard similarity. Create a temporary table with a ROW NUMBER() column partitioned by a hash of the DESCRIPTION column. Calculate the Jaccard similarity between descriptions within each partition. Filter and remove near duplicates based on a tie-breaker (smallest PRODUCT_ID).

Answer: E

Explanation:
Option D is the most efficient, secure, and correct approach for removing near-duplicate text entries using Snowpark and UDFs. It correctly addresses both the computational complexity and the security implications of the task. - It create a temporary table because we are doing operations of delete and create a table which is best done via temporary table. - It uses bucketing (hashing descriptions) to reduce the number of comparisons. This significantly improves performance compared to comparing all possible pairs of descriptions which is what options A and B do. - Use ROW_NUMBER() to flag duplicate for deletion with threshold. Option A is not optimal due to the complexity of cross join. Option B is incorrect because there is data and functionality that is lost with the insertion of distinct entries based on score. Also, it would be inefficient as it required re-evaluation of score on insertion. Option C is incorrect because Grouping by Product ID will not allow for similarity calculation across different product IDs. Option E is not applicable because Snowflake does not have a built-in 'APPROX JACCARD INDEX' function to apply directly in a SQL query.

NEW QUESTION # 146
You're working with a Snowflake stage named that contains several versions of your machine learning model, named 'model_vl .pkl' , 'model_v2.pkl' , and You want to programmatically list all files in the stage and retrieve the creation time of the latest version (i.e., using SnowSQL. Which of the following approaches is most efficient and correct?

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

Answer: C

Explanation:
Option C is the most efficient and correct solution. Snowflake's 'DIRECTORY table function provides metadata about files in a stage, including 'relative_path', 'size', and By querying this table and ordering by in descending order with 'LIMIT 1 , you can directly retrieve the information for the latest file without resorting to external tools like 'awk' or 'grep'. The '-csv' flag ensures that the output is easily parsable. Options A and D rely on parsing the output of the command, which is less reliable and efficient. Option B downloads file and uses system 'IS command. Option E gives information about stage, not files.

NEW QUESTION # 147
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