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Snowflake SPS-C01 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Data Transformations and Operations	35%	- DataFrame manipulation 1. Selection, projection, renaming, casting 2. Joins, unions, set operations 3. Filtering, sorting, grouping, aggregation - Advanced operations 1. Semi-structured data processing 2. Pivot and unpivot transformations 3. Window functions and analytics - User-defined logic 1. Stored procedures with Snowpark 2. UDFs, UDAFs, UDTFs
Topic 2: Snowpark Concepts and Architecture	25%	- Session management and connection 1. Authentication and connection settings 2. Create and configure Snowpark sessions - Snowpark architecture and execution model 1. Client-side vs server-side processing 2. Lazy evaluation and DAG execution 3. Transformations vs actions
Topic 3: Performance and Best Practices	10%	- Security and governance 1. Access control and permissions 2. Data protection and compliance - Optimization techniques 1. Minimizing data movement 2. Caching and warehouse sizing 3. Query pushdown and execution plans
Topic 4: Snowpark API and Development	30%	- Multi-language support 1. Environment setup and dependencies 2. Java and Scala API basics - Python API fundamentals 1. DataFrame creation from tables, views, SQL 2. Column operations and functions 3. Data persistence and writing results

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Snowflake Certified SnowPro Specialty - Snowpark Sample Questions (Q19-Q24):

NEW QUESTION # 19
You have a Snowpark DataFrame containing customer order data with columns , and 'order_amount' . You need to identify customers who placed orders exceeding $1000 on more than 3 separate days. Which Snowpark code snippet correctly achieves this? Assume SparkSession 'spark' and DataFrame are already defined.

Answer: C

Explanation:
Option A correctly filters for orders exceeding $1000, groups by customer ID, counts the distinct order dates, aliases the count as 'distinct_order_days', filters for customers with more than 3 distinct order days meeting the criteria, and then displays the result. Option B counts total number of orders instead of distinct dates. Option C counts all orders exceeding $1000. Option D uses distinct which will remove some of the dates and produce innacurate number of days. Option E is an incorrect syntax.

NEW QUESTION # 20
A data engineering team is building a Snowpark pipeline to process IoT sensor data'. They want to create a UDF that uses a 3rd-party Python library (not available in Snowflake's Anaconda channel) to analyze the sensor readings. The UDF needs to be efficiently deployed and managed within Snowflake. Which of the following approaches represents the MOST robust and scalable way to register and deploy this UDF using Snowpark?

A. Use 'session.add_packages' to add the specific Python package directly from the Snowflake Anaconda channel (even if the required version isn't available) and then use 'session.udf.register' for the UDF definition.
B. Create a virtual environment with the necessary Python library, zip it, upload the zip file to a Snowflake stage, and use to register the UDF. Reference the stage location and virtual environment in the register call.
C. Use 'functions.udf and directly embed the package code within the UDF definition. This approach handles package management automatically.
D. Use 'session.udf.register' and directly include the library code as a string within the UDF definition. This avoids external dependencies.
E. Create a Docker container with the Python library, push it to Snowflake Container Services, and call this container from the UDF.

Answer: B

Explanation:
Option B is the correct answer. It describes the best practice for deploying UDFs with external Python libraries in Snowflake. Creating a virtual environment, zipping it, uploading it to a stage, and referencing it during UDF registration ensures proper dependency management and avoids conflicts. Option A is problematic because embedding the library directly makes the UDF definition very large and unmanageable. Option C will not work if the required version isn't available. Option D is incorrect because functions.udf relies on packages available in the Snowflake Anaconda channel and doesn't manage custom packages. While Option E could work, its overly complex for this specific scenario compared to utilizing Snowpark virtual enviornment and stage management. Option B is more efficient and streamlined.

NEW QUESTION # 21
A data science team wants to operationalize a Snowpark Python UDF that performs sentiment analysis on customer reviews. The UDF, 'analyze sentiment(review_text)', is currently defined within a Snowpark session. Which of the following approaches is the MOST efficient and scalable way to deploy this UDF for real-time scoring of incoming review data in a Snowflake table named 'CUSTOMER REVIEWS'?

A. Package the 'analyze_sentiment' function as a stored procedure and execute it using Snowpark session.execute.
B. Persist the Snowpark session with UDF definition using pickle and call it from another Snowpark session.
C. Register the 'analyze_sentiment' UDF as a persistent UDF in Snowflake and then call it from a SQL query that reads from 'CUSTOMER REVIEWS.
D. Create a Snowpark Dataframe that reads from 'CUSTOMER_REVIEWS , convert it to pandas dataframe and call analyze_sentiment function on pandas dataframe.
E. Call the 'analyze_sentiment' UDF directly within a Snowpark DataFrame transformation that reads from 'CUSTOMER_REVIEWS'.

Answer: C

Explanation:
Registering the UDF as a persistent UDF in Snowflake allows it to be called directly from SQL, leveraging Snowflake's query engine for optimal performance and scalability. Option A keeps the transformation entirely within Snowpark, which is a valid approach, but less scalable. Option C involves converting to a Pandas DataFrame, which moves data outside of Snowflake and negates its benefits. Option D creates a stored procedure that is correct but the question ask to perform in real time so it's not ideal. Option E is not the way to persist Snowpark with UDF.

NEW QUESTION # 22
You have a Snowpark application that reads data from a large Snowflake table and performs several transformations. During testing, you observe that the application's performance is inconsistent, with some runs taking significantly longer than others, even with the same input data'. You suspect that data locality might be a contributing factor. What steps can you take within your Snowpark application to investigate and potentially improve data locality and performance consistency?

A. Disable Snowflake's result cache. This ensures that the application always reads the most recent data from disk, regardless of performance impact.
B. Use to redistribute the data across the cluster based on a relevant key. This can improve data locality for subsequent operations.
C. Ensure the Snowpark session is configured with a large enough warehouse size to minimize data spilling to disk.
D. Implement caching using , combined with a targeted 'repartition()' to ensure that frequently accessed data is readily available in memory close to the processing nodes.
E. Enable Snowflake's automatic clustering on the underlying table if it's not already enabled. This will physically organize the data on disk based on the clustering key.

Answer: B,D,E

Explanation:
'DataFrame.repartition(V allows you to redistribute data based on a chosen key, improving locality for operations that depend on that key. Snowflake's automatic clustering physically organizes data on disk, improving read performance. Combining with repartition()' ensures that frequently accessed, well-partitioned data is readily available in memory close to the processing nodes, boosting performance. Disabling the cache makes the application's performance worse. The warehouse size, if not large enough, can also lead to data spilling to disk, decreasing the performance.

NEW QUESTION # 23
You have a SQL query stored in a file named 'query.sqr which contains several complex analytical calculations. The query depends on a Snowpark 'session' object already established. You want to create a Snowpark DataFrame from the result of this query. Which of the following code snippets achieves this with optimal performance and readability, assuming correct file access permissions?

Answer: B

Explanation:
Option A provides the most straightfomard and efficient approach. It reads the SQL query from the file and directly creates a Snowpark DataFrame using 'session.sql(sql_query)'. Option B introduces Pandas, which is unnecessary and less efficient. Option C uses the Snowflake Connector outside of Snowpark's API, which is generally not the preferred approach. Option D has a non-existent function create_dataframe' , and Option E reads lines separately requiring a join which might be erroneous.

NEW QUESTION # 24
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