Study SPS-C01 Test & SPS-C01 Relevant Exam Dumps

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Snowflake Certified SnowPro Specialty - Snowpark Sample Questions (Q324-Q329):

NEW QUESTION # 324
You're building a Snowpark Python application that processes sensor data from various devices. The data arrives as a stream of JSON objects, each containing the device ID, timestamp, and sensor readings. You want to use a Streamlit application to visualize near real- time aggregates on the data'. You're aiming to create a Snowpark DataFrame from this data, perform transformations, and then serve this DataFrame to Streamlit. Which of the following approaches concerning creating the initial DataFrame from JSON data is generally the MOST efficient and scalable for handling such a stream of data?

Answer: B

Explanation:
Using Snowflake's Kafka connector (or a similar streaming ingestion service) is the most efficient and scalable way to handle streaming data. It allows for near real-time ingestion and avoids intermediate steps like writing to temporary files or using Pandas DataFrames. Using Snowpipe with auto-ingest is also a valid approach, however Kafka connector is slightly better suited for streaming data because of its real time data processing. Kafka is also a common real time streaming platform. Therefore, option C is the best answer. Other options may encounter scalability and performance issues with high-volume, continuous data streams.


NEW QUESTION # 325
You have a Snowpark DataFrame with columns 'sale_date', 'product_id', and 'revenue'. You need to calculate the cumulative revenue for each product over time. Which of the following approaches will accomplish this in Snowpark using window functions?

Answer: C,E

Explanation:
Options A and E both achieve the desired result of calculating cumulative revenue for each product. Option A utilizes 'rowsBetween' specifying that the window frame should include all rows from the beginning ('Window.unboundedPreceding') up to the current row ('Window.currentRow'). This calculates a running sum of revenue for each product over time. Option E uses 'rangeBetween' , which is equivalent to when the order-by expression is of a numeric or date type. Option B does not partition by product_id, so the cumulative revenue is calculated over the entire dataset. Option C does not include frame specification 'rowsBetween()' or 'rangeBetween(Y , therefore defaults to 'RANGE BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW, which is valid for this question. While it's functionally correct, it's implicit, so 'A' is preferrable if one option is to be selected. Option D partitions incorrectly by sale_date.


NEW QUESTION # 326
You are tasked with optimizing the performance of a Snowpark Python application that performs complex data transformations on a large dataset of IoT sensor readings. The application uses a Snowpark-optimized warehouse. You notice that the application is consistently slow, with CPU utilization on the warehouse fluctuating significantly. Which of the following actions would be MOST effective in addressing this performance issue? Assume the dataset is partitioned on the 'sensor_id' column within Snowflake.

Answer: C,D,E

Explanation:
Repartitioning allows for improved parallelism and reduces data skew, especially when the initial data distribution is uneven. Avoiding Python UDFs improves performance because they execute outside of Snowflake's optimized engine. Pushing down transformations and leveraging stored procedures minimizes data transfer between Snowpark and Snowflake, and leverages Snowflake's processing capabilities. Increasing warehouse size or enabling auto-scaling might help, but addressing data skew and UDF overhead will likely provide more significant performance gains.


NEW QUESTION # 327
You have a Snowpark Python application that performs several data transformations on a DataFrame representing customer transactions. The application is experiencing performance issues, and you suspect that some transformations are unnecessarily expensive. Which of the following techniques can MOST effectively optimize the performance of your Snowpark application, specifically focusing on minimizing data movement and leveraging Snowflake's query optimization capabilities?

Answer: D

Explanation:
Snowpark is designed to push down computations to Snowflake, allowing Snowflake's query optimizer to handle the execution. Using Snowpark's built-in DataFrame transformations allows Snowflake to understand the intent and optimize the query accordingly. Materializing intermediate results using .cache()' (A) can lead to unnecessary data movement. Python UDFs (B) can be useful for complex logic but should be avoided for simple transformations as they bypass Snowflake's optimization capabilities and are generally slower than native SQL functions. Warehouse size (E) is a factor, but optimizing the query logic is more crucial. Using Pandas dataframe is also costly and performance heavy.


NEW QUESTION # 328
A data scientist has developed a complex machine learning model in Python that needs to be operationalized within a Snowpark pipeline. This model depends on several custom Python packages not available in Snowflake's default environment. The data scientist wants to define a UDTF to apply this model to incoming data'. Which of the following steps are NECESSARY to successfully deploy and execute this UDTF in Snowflake? (Select three)

Answer: A,B,C

Explanation:
To deploy a UDTF with custom Python packages, you need to: 1 . Isolate the required packages using a virtual environment. 2. Upload the entire virtual environment (or a selection) as a ZIP file to a Snowflake stage, to make it available to Snowflake. 3. Reference the stage location of the ZIP file in the 'imports clause of the 'CREATE FUNCTION' statement. Options A, C and E are necessary for the UDTF to access the packages. Option B is not required and can cause issue, if entire virtual environment is not packaged appropriately. Option D is not recommended, if entire vitual environment is packaged. It is possible, packages are dependant on some python internal modules.


NEW QUESTION # 329
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