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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Data Transformations and DataFrame Operations | 35% | - Complex data pipelines - Filtering, Aggregating, and Joining DataFrames - Window functions - Using built-in functions - Persisting transformed data |
| Topic 2: Snowpark API for Python | 30% | - User-Defined Functions (UDFs) and Stored Procedures - Establishing connections and session management - Reading and writing data - DataFrame creation and manipulation - Working with Semi-structured data |
| Topic 3: Performance Optimization and Best Practices | 20% | - Caching strategies - Minimizing data transfer - Warehouse sizing for Snowpark - Vectorized UDFs - Query pushdown and optimization - Debugging and explain plans |
| Topic 4: Snowpark Concepts | 15% | - Stored procedures and conditional logic - Transformations vs. Actions - Snowpark Sessions and connection management - Client-side vs. Server-side execution - Snowpark architecture and core concepts - Snowpark DataFrames and query plans |
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NEW QUESTION # 136
You have a Snowpark DataFrame 'customer data df containing customer information, including 'customer id', 'email', and phone number'. You need to anonymize the 'email' and 'phone number" columns for customers residing in specific countries (e.g., 'USA', 'Canada') before persisting the changes back to the 'customers' table. Anonymization should replace sensitive data with 'XXXXX'. You want to leverage UDF for obfuscation. What is correct and optimal approach considering performance and security?
Answer: B,E
Explanation:
Options B and E provide the most efficient and secure solutions. Option B correctly utilizes the 'when' function along with a registered UDF ('anonymize_udf) to conditionally anonymize the 'email' and 'phone_number' columns only for customers in 'USA' or 'Canada'. This ensures that only the necessary data is modified. Option E creates and uses a temporary UDF with SQL, achieving the same result in a different (yet valid) way. Option A is less efficient because it filters the DataFrame, updates it, and then joins it back with the original, which can be computationally expensive. Additionally, join can produce cartesian results if not done properly. Option C, while straightforward, hardcodes the anonymized value directly, which might not be the best practice in all scenarios and doesn't use a UDF. Option D, although similar to B, attempts to create a permanent UDF without specifying the stage location, which may lead to errors and is not best practice unless you manage it. Also creating a UDF in Python using SQL can be faster and safer.
NEW QUESTION # 137
You are developing a Snowpark application to process large datasets. You want to leverage asynchronous jobs to improve performance and prevent blocking the main thread. You have the following code snippet:
Answer: A
Explanation:
Option D is the most robust and recommended approach for handling asynchronous Snowpark jobs. Using callback functions allows for non-blocking execution and proper error handling. ensures that the main thread doesn't proceed until the asynchronous job has completed. Option B, while functionally correct, introduces inefficient polling. Options A and C lack comprehensive error handling or assume error handling behavior that Snowflake doesn't provide directly. Option E bypasses the Snowpark API and introduces unnecessary complexity.
NEW QUESTION # 138
You are developing a Snowpark application that requires secure access to Snowflake. You need to establish a session using Key Pair authentication. You have stored your private key in an encrypted file and have the passphrase available. Assume you've correctly configured your Snowflake user with the public key. Which of the following methods can be used to load the private key, assuming that 'PRIVATE KEY PATH' stores path to encrypted private key and 'PRIVATE KEY PASSPHRASE stores passphrase?
Answer: C,E
Explanation:
The correct answers are B and E. B: This option correctly loads an encrypted private key from a file using the 'cryptography' library, specifying the passphrase and backend. E: Option E directly utilizes snowflake.connector.read_private_key functionality simplifying the process if using snowflake-connector. Option A fails to specify the backend for the serialization, which is necessary for certain key types and may lead to errors. Option C simply reads the key as a string without decryption. Option D assumes the private key is stored in an environment variable, which does not include any decryption.
NEW QUESTION # 139
You are tasked with creating a Snowpark DataFrame from a Python list of tuples. Each tuple represents a customer record with the following structure: '(customer_id, signup_date, The 'customer _ id' should be an integer, 'signup_date' should be a date, and should be a decimal. You want to define the schema explicitly for type safety and performance. Which of the following code snippets correctly defines the schema and creates the Snowpark DataFrame?
Answer: A
Explanation:
Option A correctly defines the schema using 'StructType', 'StructField', 'Integer Type', 'DateType' , and 'DecimalType' . It also specifies the precision and scale for the 'DecimalType' which is important for accurately representing monetary values. The date values in the data are also compatible with DateType. The other options use incorrect data types for the last_purchase_amount (FloatType, DoubleType, StringType) or don't specify precision and scale for the DecimalType. Note that Snowflake DateType only accepts values formatted as YYYY-MM-DD'.
NEW QUESTION # 140
You have a Snowpark DataFrame named 'products_df' with columns 'product_id' (INT), 'product_name' (VARCHAR), and 'price' (FLOAT). You want to create a new DataFrame called 'discounted_products df that includes all columns from 'products_df' plus a new column named 'discounted_price', which is calculated as the original price minus a discount percentage specified by the variable 'discount_rate' (e.g., 0.1 for 10%). The 'discount_rate' is stored in the database table named 'discount_table'. You want to load the rate to variable. Choose the correct ways to achieve this. (Select all that apply)
Answer: B,D,E
Explanation:
Options A, B and D provide valid ways to fetch 'discount_rate' as a single numerical value. And fetch the data and gets the first value from the first row. Similarly, gets the data and return the first row. However, Option C does not have LIMIT 1 and will not work. Option E fetches one row as one array, thus requires rate[0] to compute discounted_price.
NEW QUESTION # 141
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