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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Data Transformations and Operations | 35% | - Advanced operations
|
| Topic 2: Performance and Best Practices | 10% | - Security and governance
|
| Topic 3: Snowpark Concepts and Architecture | 25% | - Snowpark architecture and execution model
|
| Topic 4: Snowpark API and Development | 30% | - Python API fundamentals
|
>> New SPS-C01 Test Questions <<
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NEW QUESTION # 211
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: C
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 # 212
You are working with semi-structured data in Snowflake stored in a VARIANT column named 'payload'. You want to extract specific fields from this VARIANT column within a SQL query used to create a Snowpark DataFrame. Which of the following approaches allows you to access nested fields within the 'payload' column directly in the SQL query and create a corresponding column in your Snowpark DataFrame? Select all that apply.
Answer: A,B,C
Explanation:
Options A, B, and D are correct. Option A utilizes the Snowflake's native dot notation (e.g., 'payload:fieldl :field2) for direct access of nested fields. Option B provides the 'GET_PATH' function, also allowing access to nested fields. Option D leverages 'LATERAL FLATTEN' to unnest the VARIANT data, enabling subsequent field access. Option C is less efficient, adding unnecessary steps, and Option E involves Pandas, which is typically not the optimal path for leveraging Snowpark's capabilities directly. Remember that 'LATERAL FLATTEN' is best when you need to process the data in a relational format after extracting it from the VARIANT.
NEW QUESTION # 213
When using key pair authentication with Snowpark, what security best practices should you implement to protect your private key?
(Select all that apply)
Answer: A,B,C
Explanation:
Storing the private key directly in the code repository (A) is a major security risk. Encrypting the private key at rest (B) provides an additional layer of security. Storing the private key in an environment variable or secure secret management system (C) is the recommended approach. Granting broad access (D) increases the risk of compromise. Regularly rotating the key pair (E) limits the impact if a key is compromised. Options B,C and E are the most secure ones.
NEW QUESTION # 214
You have a complex data pipeline implemented using Snowpark Tasks in a Directed Acyclic Graph (DAG). One of the tasks, , depends on the successful completion of two parent tasks, and 'task B'. You need to implement error handling such that if 'task_R fails, 'task_C' should not be executed, but should still complete its execution regardless of status. If 'task B' fails, 'task_C' should not be executed. How do you configure the task dependencies and error handling in Snowflake to achieve this behavior?
Answer: B
Explanation:
Setting to execute 'AFTER task_A, task_B' establishes the dependency. Configuring on both 'task_R and 'task_B' ensures that if either task fails, it will suspend itself and prevent 'task_C' from running. This achieves the required behavior. Option A is incorrect because setting the parameter on 'task_C' will not prevent the execution of 'task_C' if either 'task_R or failed, but only suspend it after it tries to execute and fails. Option B is not correct because Snowflake's default behavior does not inherently skip 'task_C' unless specifically configured through dependencies. Option C is overly complex and doesn't accurately reflect intended behaviour.Option E is overly complex and doesn't accurately reflect intended behaviour. Option D will ensure only if both 'task_A' and runs successfully then runs else it will suspend the further execution.
NEW QUESTION # 215
A data engineering team has developed a Snowpark Python application to process customer orders, enrich them with external data (e.g., geo location, weather) and update the Customer360 table. The application is deployed to a production environment. The application's latency has significantly increased over the last week. Your investigation reveals that the Snowflake warehouse used by the application is constantly switching between the 'Scaling Up' and 'Scaling Down' states. The team has set the Auto Suspend time to 5 minutes and Auto Resume to True. Assuming that the team hasn't changed the code, the external API or any parameter related to data ingestion, which combination of the following actions would MOST likely fix the warehouse instability issue and improve the performance of this Snowpark application in production without substantial cost increases?
Answer: B,C
Explanation:
The 'Scaling Up' and 'Scaling Down' thrashing is likely caused by the warehouse suspending too quickly, leading to constant restarts as new requests arrive. Increasing the Auto Suspend time (Option A) prevents this frequent cycling. Increasing the MIN CLUSTER COUNT (Option B) makes more resources readily available, helping the warehouse respond faster to spikes in demand and reducing the need for scaling up. Workload management (Option C) is a good practice but may not directly address the root cause of the instability. Reducing MAX_CLUSTER_COUNT (Option D) could worsen the problem by limiting the warehouse's ability to handle peak loads. Changing to 'ECONOMY' scaling policy (Option E) would prioritize cost over performance, which is counter to improving performance.
NEW QUESTION # 216
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