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Snowflake SPS-C01 Exam Syllabus Topics:

Section	Objectives
Topic 1: Testing, Debugging, and Deployment	- Production readiness 1. Debugging Snowpark applications 2. Deployment strategies
Topic 2: User Defined Functions and Stored Procedures	- Extending Snowpark with custom logic 1. Python UDFs 2. Stored procedures in Snowpark
Topic 3: DataFrame Operations and Data Processing	- Data transformation workflows 1. Filtering, selecting, and aggregations 2. Joins and window functions
Topic 4: Snowpark Fundamentals	- Snowpark architecture and concepts 1. Snowflake execution model overview 2. Snowpark APIs and supported languages
Topic 5: Performance Optimization and Best Practices	- Efficient Snowpark execution 1. Resource utilization tuning 2. Pushdown optimization concepts
Topic 6: Data Engineering with Snowpark	- Pipeline development 1. Batch processing workflows 2. Integration with Snowflake data pipelines

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Snowflake Certified SnowPro Specialty - Snowpark Sample Questions (Q25-Q30):

NEW QUESTION # 25
You are working with a Snowpark DataFrame containing product review data'. The DataFrame has a 'review_text' column containing unstructured text reviews. Your task is to perform sentiment analysis on these reviews using Snowpark for Python. However, you are restricted to using only Snowpark built-in functions and UDFs; you cannot use external libraries like NLTK or TextBlob directly within your Snowpark code. Given this contraint, what is a valid approach to enrich your dataframe?

A. Use Snowpark's 'transform' function with a custom Python transformer that leverages an internal vocabulary of positive and negative words to assign a sentiment score to each review based on word frequency. Then store the result in the column.
B. Develop a sentiment analysis microservice deployed outside Snowflake, then use Snowpark's 'call_udf function to invoke this service, passing the 'review_text' as input and receiving the sentiment score as output.
C. Leverage Snowflake's external function capabilities to call a pre-trained sentiment analysis model hosted on a cloud ML platform (e.g., AWS SageMaker, Azure Machine Learning), passing the 'review_text' as input and receiving the sentiment score as output.
D. Create a Java UDF within Snowflake that utilizes a Java-based sentiment analysis library (e.g., Stanford CoreNLP) to process the 'review_text' and return the sentiment score. Then, call this Java UDF from your Snowpark Python code.
E. Build a Snowpark Python UDF that uses regular expressions to identify keywords and phrases indicative of positive, negative, or neutral sentiment. Assign a score based on the presence and frequency of these keywords. You can then apply 'when' statements to get .

Answer: B,C,D,E

Explanation:
Options A, B, D and E are valid approaches given the constraints, since the task says enrich your dataframe, so we need some final column containing a score. Option A: The microservice approach, using 'call_udf and external function is a robust and scalable solution. It enables to leverage complex sentiment analysis models without directly integrating them into Snowflake. Option B: The Java UDF allows leveraging existing Java-based NLP libraries, extending the capabilities of Snowpark for sentiment analysis while adhering to the constraint of not using external Python libraries directly. Option D: Snowflake's External Functions are designed for calling external services. Passing 'review_text as an argument to pre-trained sentiment analysis model, and receive a result. Option E: This solution utilizes regular expression and UDFs. To search for sentiment indicative keywords and phrases and assigning the score using 'when' statements. Option C: This approach is the least efficient because it requires maintaining an internal vocabulary, which can be less accurate and harder to update compared to using pre-trained models or external services and it also does not generate a final new column to enrich the dataframe.

NEW QUESTION # 26
You have a requirement to process a large number of JSON files stored in a Snowflake stage 'json_stage'. These JSON files contain complex nested structures. You need to extract specific fields from these files using Snowpark Python and load them into a Snowflake table. You want to use 'SnowflakeFile' to read the JSON files and minimize the amount of data loaded into memory. Select all that apply from the following options to efficiently accomplish this task:

A. Create a UDTF that accepts a "SnowflakeFile' object, uses 'json.loadS to parse the JSON content incrementally, extracts the desired fields, and yields rows for insertion into the target table. Use 'session.read.option('PATTERN', ' to generate the initial DataFrame.
B. Write a Python script that downloads all JSON files from the stage using 'SnowflakeFile.get' , iterates through the downloaded files, parses each file, extracts the fields, and inserts the data into the Snowflake table using the Snowflake Python connector.
C. Create a UDF that takes a file path as input, constructs a 'SnowflakeFile' object within the UDF, reads the JSON data using 'json.loadS, extracts the required fields, and returns a Row object or a dictionary. Use 'session.sql('SELECT relative_path FROM to get file paths, create Snowpark Dataframe and then call the UDF on each file path.
D. Use to directly load all JSON files into a Snowpark DataFrame and then use 'select' with path expressions (e.g., 'col('fieldl .nested_field')) to extract the required fields.
E. Create a UDF that accepts a 'SnowflakeFile' object, opens the file, reads the JSON data using 'json.load', extracts the required fields, and returns a JSON string. Create a Snowpark DataFrame using 'session.read.option('PATTERN', ' then call the UDF with the file path, and finally parse the returned JSON string to load data.

Answer: A

Explanation:
Option C is the most efficient approach. - A UDTF allows for parallel processing of the JSON files within the Snowflake environment. - By directly using 'SnowflakeFile' objects, you avoid unnecessary data transfer outside of Snowflake. - The function incrementally parses the JSON data, minimizing memory usage compared to loading the entire file at once. Option A loads all JSON files into a DataFrame which can lead to memory issues with large JSON files. Option B reads all files using snowflake connector in python, so it is not optimal. Option D downloads all files which would be inefficient. Option E returns a JSON string from UDF then parses it again which is redundant and less efficient.

NEW QUESTION # 27
You have a Snowpark Python application that performs complex data transformations and machine learning model training. The data is stored in Snowflake tables. You notice that model training jobs, specifically those involving large feature sets and iterative algorithms, are consistently slow. The warehouse is already scaled to a LARGE size. Which of the following techniques, when applied individually or in combination, would MOST likely improve the performance of model training in Snowpark?

A. Cache intermediate Snowpark DataFrames using to avoid recomputation of common data transformations across multiple training iterations.
B. Leverage external functions (IJDFs) to offload computationally intensive operations to specialized hardware outside of Snowflake.
C. Implement data skipping and filtering strategies to reduce the amount of data read during feature extraction and model training. Pre-aggregate when possible.
D. Scale the virtual warehouse UP to an XLARGE or larger. This provides more computational resources.
E. Use the 'sproc' decorator to define user-defined functions (UDFs) directly within Snowflake, leveraging the platform's optimized execution engine for specific computations.

Answer: A,C

Explanation:
Caching intermediate DataFrames (Option B) avoids redundant computations, significantly speeding up iterative algorithms. Filtering and data skipping (Option E) reduce the amount of data processed, which is especially beneficial with large feature sets. While sprocs (Option A) offer performance benefits for certain IJDFs, the impact on overall model training performance might be less significant compared to caching and data reduction. External functions (Option C) may introduce network latency, outweighing the potential benefits, unless carefully optimized for data transfer. Scaling up (Option D) might help, but is not the most likely to improve performance, given that the warehouse is already at a LARGE size. Focusing on more efficient data handling is better in most cases.

NEW QUESTION # 28
A data engineering team is building a Snowpark application in Python to perform advanced time series analysis on sensor data stored in Snowflake. They need to leverage a specific, but older, version of the 'pandas' library (version 1.1.5) that is not available in the default Snowflake Anaconda channel. Which of the following approaches is the MOST efficient and recommended way to ensure this specific version of 'pandas' is available to their Snowpark application, while minimizing security risks and operational overhead?

A. Create a new Snowflake Anaconda environment and install pandas 1.1.5 into this environment, then configure the Snowpark session to use this custom environment. Ensure the 'snowflake-snowpark-python' package is also included.
B. Build a custom Anaconda channel outside of Snowflake, and configure the Snowpark session to point to that channel.
C. Upload the pandas 1.1.5 wheel file directly into a Snowflake stage and reference it within the session configuration using along with
D. Package the pandas 1.1.5 library within the Snowpark application code itself, by including it in the application deployment package.
E. Use 'conda install pandas==l .1.5' within the Snowpark Python code directly. This will install pandas at runtime.

Answer: A

Explanation:
Creating a new Snowflake Anaconda environment is the most efficient and secure way to manage specific package versions. It leverages Snowflake's built-in dependency management capabilities and avoids external dependencies or manual package management within the application code. Options A and D introduce potential security risks and maintenance overhead. Option B is inefficient for dependency management. Option E will not work, as it tries to install dependencies at runtime, which isn't supported in Snowpark.

NEW QUESTION # 29
You are optimizing a Snowpark application that performs complex data transformations on a large dataset. The transformation involves multiple joins and aggregations. You notice that the query execution time is excessive. Which of the following techniques would be MOST effective in improving the performance of this application, assuming you have the appropriate Snowflake role and privileges?

A. Reduce the overall complexity of the query, but increase the number of queries being executed. This allows for a more incremental approach.
B. Rewrite the Snowpark code to use more UDFs (User-Defined Functions). UDFs are always more efficient than built-in functions.
C. Increase the size of the Snowflake warehouse used for the computation. A larger warehouse provides more resources for parallel processing.
D. Disable result caching for the Snowflake session. This ensures that the most up-to-date data is always used, even if it slows down performance.
E. Use the ' DataFrame.explain(Y method to analyze the query plan and identify potential bottlenecks. Optimize the code based on the query plan analysis, focusing on reducing data shuffling and improving join strategies.

Answer: C,E

Explanation:
Increasing the warehouse size provides more compute resources for parallel processing. Analyzing the query plan using DataFrame.explain(Y helps identify bottlenecks (e.g., excessive data shuffling) so you can optimize the code. UDFs are not always more efficient than built-in functions and can sometimes introduce overhead. Reducing complexity and increasing the number of queries doesn't necessarily improve performance, especially if it leads to more I/O operations. Disabling result caching can negatively impact performance, as it prevents the reuse of previously computed results.

NEW QUESTION # 30
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