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Snowflake SnowPro Advanced: Data Scientist Certification Exam Sample Questions (Q209-Q214):

NEW QUESTION # 209
You are working with a Snowflake table named 'CUSTOMER DATA' containing customer information, including a 'PHONE NUMBER' column. Due to data entry errors, some phone numbers are stored as NULL, while others are present but in various inconsistent formats (e.g., with or without hyphens, parentheses, or country codes). You want to standardize the 'PHONE NUMBER column and replace missing values using Snowpark for Python. You have already created a Snowpark DataFrame called 'customer df representing the 'CUSTOMER DATA' table. Which of the following approaches, used in combination, would be MOST efficient and reliable for both cleaning the existing data and handling future data ingestion, given the need for scalability?

A. Leverage Snowflake's data masking policies to mask any invalid phone number and create a view that replaces NULL values with 'UNKNOWN'. This approach doesn't correct existing data but hides the issue.
B. Create a Snowflake Stored Procedure in SQL that uses regular expressions and 'CASE statements to format the "PHONE_NUMBER column and replace NULL values. Call this stored procedure from a Snowpark Python script.
C. Use a series of and methods on the Snowpark DataFrame to handle NULL values and different phone number formats directly within the DataFrame operations.
D. Use a UDF (User-Defined Function) written in Python that formats the phone numbers based on a regular expression and applies it to the DataFrame using For NULL values, replace them with a default value of 'UNKNOWN'.
E. Create a Snowflake Pipe with a COPY INTO statement and a transformation that uses a SQL function within the COPY INTO statement to format the phone numbers and replace NULL values during data loading. Also, implement a Python UDF for correcting already existing data.

Answer: D,E

Explanation:
Options A and E provide the most robust and scalable solutions. A UDF offers flexibility and reusability for data cleaning within Snowpark (Option A). Option E leverages Snowflake's data loading capabilities to clean data during ingestion and adds a UDF for cleaning existing data providing a comprehensive approach. Using a UDF written in Python and used within Snowpark leverages the power of Python's regular expression capabilities and the distributed processing of Snowpark. Handling data transformations during ingestion with Snowflake's built- in COPY INTO with transformation is highly efficient. Option B is less scalable and maintainable for complex formatting. Option C is viable but executing SQL stored procedures from Snowpark Python loses some of the advantages of Snowpark. Option D addresses data masking not data transformation.

NEW QUESTION # 210
You are deploying a machine learning model to Snowflake using a Python UDF. The model predicts customer churn based on a set of features. You need to handle missing values in the input data'. Which of the following methods is the MOST efficient and robust way to handle missing values within the UDF, assuming performance is critical and you don't want to modify the underlying data tables?

A. Pre-process the data in Snowflake using SQL queries to replace missing values with the mean for numerical features and the mode for categorical features before calling the UDF.
B. Use within the UDF to forward fill missing values. This assumes the data is ordered in a meaningful way, allowing for reasonable imputation.
C. Use within the UDF, replacing missing values with a global constant (e.g., 0) defined outside the UDF. This constant is pre-calculated based on the training dataset's missing value distribution.
D. Raise an exception within the UDF when a missing value is encountered, forcing the calling application to handle the missing values.
E. Implement a custom imputation strategy using 'numpy.where' within the UDF, basing the imputation value on a weighted average of other features in the row.

Answer: A

Explanation:
Pre-processing data in Snowflake with SQL for imputation offers several advantages. It allows leveraging Snowflake's compute resources for data preparation, rather than the UDF's limited resources. Handling missing values before the UDF call also simplifies the UDF code, making it more efficient and less prone to errors. Using 'fillna' within the UDF (options A, B, and C) can lead to performance bottlenecks and potential data leakage issues if not carefully managed. Raising an exception (option E) is not practical for production deployments where missing values are expected.

NEW QUESTION # 211
You are building a data science pipeline in Snowflake to perform time series forecasting. You've decided to use a Python UDTF to encapsulate the forecasting logic using a library like 'Prophet'. The UDTF needs to access historical data to train the model and generate forecasts. The data is stored in a Snowflake table named 'SALES DATA with columns 'DATE' and 'SALES'. Which of the following approaches is/are most efficient and secure for accessing the 'SALES DATA table from within the UDTF during model training?

A. Pass the entire 'SALES DATA' table as a Pandas DataFrame to the UDTF as an argument. This approach is suitable for smaller datasets. Do not partition the data frame.
B. Create a view on top of 'SALES DATA' and grant access to the UDTF's owner role to the view. Then, query the view using Snowpark within the UDTF.
C. Use the 'snowflake.connector' to connect to Snowflake using a dedicated service account with read-only access to the 'SALES DATA' table. Store the service account credentials securely in Snowflake secrets and retrieve them within the UDTF.
D. Use the Snowpark API within the UDTF to query the 'SALES DATA' table directly, leveraging the existing Snowflake session context. This requires no additional credentials management.
E. Bypass Snowflake entirely and load data from S3 stage into a Pandas dataframe.

Answer: B,D

Explanation:
Options C and D provide the most efficient and secure ways to access data within a Snowflake UDTF. C leverages the Snowpark API, which allows you to query Snowflake tables directly using the existing session context, eliminating the need for managing separate credentials. This is the recommended approach for accessing Snowflake data from within UDTFs. Option D is also viable, as creating a view provides an abstraction layer and allows you to control access to specific columns or rows of the underlying table. Option A, using 'snowflake.connector' and managing credentials, is less desirable due to the increased complexity and security risks associated with credential management. Option B can be suitable for smaller data sets, but its very inefficient approach. Option E is not acceptable as it is not secure.

NEW QUESTION # 212
You are building a product recommendation system using Snowflake Cortex. You have a table 'PRODUCT DESCRIPTIONS' containing product IDs and textual descriptions. You want to generate vector embeddings for these descriptions to perform similarity searches. However, you need to control the cost and latency of the embedding generation process. Which of the following strategies and considerations are MOST important for optimizing performance and cost when generating vector embeddings in Snowflake Cortex using a UDF?

A. Use a larger Snowflake warehouse size. Increasing the warehouse size always linearly reduces embedding generation time and cost.
B. Use the smallest available Cortex embedding model. Smaller models are always faster and cheaper, regardless of the dataset size.
C. Optimize the batch size passed to the embedding UDF. Experiment with different batch sizes to find the optimal trade-off between throughput and latency. Too large batches might cause memory issues, while too small batches increase overhead. Consider using a batch size of 64 or 128 as a starting point, adjusting based on your dataset and resource constraints.
D. Cache the results of the embedding LJDF. Implement a caching mechanism (e.g., using a Snowflake table) to store the embeddings for frequently accessed product descriptions, avoiding redundant embedding calculations. use a materialized view.
E. Partition the 'PRODUCT DESCRIPTIONS' table by product category and generate embeddings for each partition separately. This helps to distribute the workload and reduce the size of the data processed by each UOF call. This makes more sense and is faster to re-create the table.

Answer: C,D,E

Explanation:
Optimizing batch size is crucial for throughput and latency (B). Caching embeddings avoids redundant computations (C), and partitioning data helps distribute the workload (D). Using the smallest model may sacrifice accuracy (A), and simply increasing warehouse size isn't always cost-effective (E).

NEW QUESTION # 213
You are performing exploratory data analysis on a dataset containing customer transaction data in Snowflake. The dataset has a column named 'transaction_amount' and a column named 'customer_segment'. You want to analyze the distribution of transaction amounts for each customer segment using Snowflake's statistical functions. Which of the following approaches would BEST achieve this, providing insights into the central tendency and spread of the data?

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

Answer: B

Explanation:
Option E is the best approach. It uses to calculate the mean, to calculate the median (robust to outliers), to calculate the standard deviation (measure of spread), and 'QUANTILE(transaction_amount, 0.25, 0.5, 0.75)' to calculate the quartiles (25th, 50th, and 75th percentiles), all grouped by 'customer_segment'. This provides a comprehensive view of the distribution. Option A only provides an approximate count of distinct transaction amounts and the average. Option B provides standard deviation, variance, and median but lacks the mean and quartiles. Option C provides the range and count, which are useful but not as comprehensive. Option D calculates correlation and covariance, which are useful for understanding the relationship between transaction amount and customer segment (assuming customer segment is appropriately encoded numerically), but not for analyzing the distribution within each segment. It is important to note that 'QUANTILE' can also be accomplished using 'APPROX_PERCENTILE'

NEW QUESTION # 214
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