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Snowflake SPS-C01 Exam Syllabus Topics:

Snowflake Certified SnowPro Specialty - Snowpark Sample Questions:

1. You are tasked with operationalizing a Snowpark Python UDF for batch scoring of a large dataset. The UDF takes a set of feature columns and returns a prediction. You want to optimize performance and resource utilization. Select all the strategies that would effectively improve the operational efficiency and scalability of your UDF execution.

A) Always use a warehouse size of 'X-Large' or larger regardless of the data volume to guarantee sufficient resources for UDF execution.
B) Ensure that the Snowpark DataFrame being passed to the UDF is appropriately partitioned based on a relevant column (e.g., a geographical region) before invoking the UDF.
C) Utilize the 'vectorized' argument during UDF registration to enable batch processing of input data within the UDF.
D) Adjust the 'MAX BATCH SIZE parameter for the warehouse executing the UDF to the largest possible value to minimize overhead.
E) If the UDF performs external API calls, implement retry logic with exponential backoff to handle transient network errors gracefully.

2. You are using Snowflake Notebooks to develop a Snowpark application and want to leverage a custom Python library that is not available in the default environment. What steps are necessary to make this library available within your Snowflake Notebook?

A) Install the library directly within the Snowflake Notebook using '!pip install
B) Upload the Python library's ' .py' file directly to the Snowflake stage and import it using 'import sys; sys.path.append("); import
C) Create a conda environment specification file ('environment.yml') that includes the custom library, upload it to a Snowflake stage, and then create a new environment based on that file when creating or modifying the Snowflake Notebook.
D) Install the library using pip in the Snowflake Notebook's terminal and then restart the Snowflake Notebook.
E) Create a deployment file using setup.py, upload deployment file to stage, and create function

3. You're working with Snowpark and have a DataFrame 'df containing a column 'json_data' with JSON strings. Some of these JSON strings are invalid. You need to parse the valid JSON strings and extract a field named 'product_id' from them. Invalid JSON strings should result in a 'NULL' value for the extracted 'product_id'. Which of the following approaches is the MOST robust and efficient way to achieve this?

A)

B)

C)

D)

E)

4. A data engineering team is using Snowpark Python to build a complex ETL pipeline. They notice that certain transformations are not being executed despite being defined in the code. Which of the following are potential reasons why transformations in Snowpark might not be executed immediately, reflecting the principle of lazy evaluation? Select TWO correct answers.

A) Snowpark employs lazy evaluation to optimize query execution by delaying the execution of transformations until the results are actually required.
B) Snowpark operations are only executed when an action (e.g., 'collect()', 'show()', is called on the DataFrame or when the DataFrame is materialized.
C) Snowpark automatically executes all transformations as soon as they are defined, regardless of whether the results are needed.
D) The 'eager_execution' session parameter is set to 'True'.
E) The size of the data being processed exceeds Snowflake's memory limits, causing transformations to be skipped.

5. You are developing a Snowpark application to process customer reviews. You need to use a third-party sentiment analysis library, 'SentimentAnalyzer', which is NOT available in the Anaconda repository. You have the library JAR file stored in an internal artifact repository accessible via HTTP. Which of the following steps are necessary to make this library available to your Snowpark session?

A) Configure the Snowflake account-level parameter to point to the HTTP location of the JAR file. Then use session.add_import' to use it.
B) Upload the JAR file toa Snowflake stage and register it as a Java UDF using CREATE FUNCTION.
C) Upload the JAR file to a Snowflake stage. Then use 'session.add_import' to make the file available in your Snowpark session.
D) Use 'session.add_dependency('/path/to/SentimentAnalyzer.jar')' in your Snowpark Python code after uploading the JAR to an internal stage.
E) Create a conda environment that includes the JAR, upload it to a stage, and use the environment in Snowpark.

Solutions: