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| Section | Weight | Objectives |
|---|---|---|
| Data Transformations and DataFrame Operations | 35% | - Filtering, Aggregating, and Joining DataFrames - Window functions - Using built-in functions - Complex data pipelines - Persisting transformed data |
| Snowpark API for Python | 30% | - Establishing connections and session management - DataFrame creation and manipulation - Working with Semi-structured data - User-Defined Functions (UDFs) and Stored Procedures - Reading and writing data |
| Performance Optimization and Best Practices | 20% | - Warehouse sizing for Snowpark - Caching strategies - Vectorized UDFs - Debugging and explain plans - Minimizing data transfer - Query pushdown and optimization |
| Snowpark Concepts | 15% | - Snowpark Sessions and connection management - Snowpark DataFrames and query plans - Stored procedures and conditional logic - Client-side vs. Server-side execution - Transformations vs. Actions - Snowpark architecture and core concepts |
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NEW QUESTION # 19
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: E
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 # 20
You are profiling a Snowpark application that uses a combination of SQL queries and Python UDFs. You observe that a particular stage involving a UDF is taking significantly longer than expected. You suspect that the UDF's performance is the bottleneck. Which of the following steps would be the MOST comprehensive approach to diagnose and address the performance issue?
Answer: B
Explanation:
Option B offers the most structured and informed approach. The query profile provides detailed insights into execution times for each stage, including UDF execution. Analyzing the UDF code then allows for targeted optimization. While A, C, and D are potentially helpful, they are less systematic. E is premature without proper diagnosis. The query profile in Snowflake is the most comprehensive and targeted approach to the performance troubleshooting. Also it is important to understand the code inside UDF.
NEW QUESTION # 21
You have a Snowpark DataFrame containing customer data'. You need to create a stored procedure that accepts the DataFrame and a list of column names as input and returns a new DataFrame containing only the specified columns. Which of the following approaches correctly implement this functionality and handles data types effectively (Select all that apply)?
Answer: C,E
Explanation:
Options B and E are correct. Option B correctly registers the function 'select_columnS as a stored procedure using "session.sproc.register'. Option E properly constructs the DataFrame by dynamically selecting columns by using 'df[col]'. Option A although syntactically correct may not perform as expected. Option C is incorrect because it attempts to use 'ArrayType' for a standard Python List, which is incompatible. Option D uses columns: str' which makes column as Tuple object instead of List object.
NEW QUESTION # 22
Consider a Snowflake table named 'raw events' containing web event data in a VARIANT column called 'event data'. Each 'event_data' JSON object may contain an array of product IDs under the key 'product_ids', and another array of user IDs who interacted with those products under the key 'user_ids'. These arrays are not guaranteed to be the same length, and a missing array should be handled gracefully (return null/empty result for that row rather than throwing error). Your objective is to create a Snowpark DataFrame that flattens both arrays into rows, pairing each product ID with each user ID for the specific event, generating as many rows as possible, and including event timestamp. You can use a zero-based index for the array elements using 'ARRAY CONSTRUCT()' and then using that array for using the EXPLODE function. Which of the following are necessary steps to fulfill this requirement and which are not?
Answer: A,B,D
Explanation:
Options A, C, and E are correct. A: Using and along with ensures dynamic index array generation and graceful handling of missing or non-numeric dat a). C: Using 'LEFT OUTER JOIN LATERAL FLATTEN' allows both 'product_ids' and 'user_ids' arrays to be flattened, even if one or both are missing. The LEFT JOIN ensures all rows from the original data are kept. The use of handles cases where either array is null or empty, preventing errors and providing default values. E:Using or 'case when' handles exception and different length of arrays.
NEW QUESTION # 23
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: A,E
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 # 24
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