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[General] Pass-Sure DEA-C02 Study Test Help You to Get Acquainted with Real DEA-C02 Exam S

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【General】 Pass-Sure DEA-C02 Study Test Help You to Get Acquainted with Real DEA-C02 Exam S

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Snowflake SnowPro Advanced: Data Engineer (DEA-C02) Sample Questions (Q191-Q196):NEW QUESTION # 191
You are tasked with designing a solution to load semi-structured data (JSON) from an AWS S3 bucket into a Snowflake table using Snowpipe and the REST API. The data in S3 is constantly being updated, and you need to ensure that only new or modified files are loaded into Snowflake. Which of the following steps are essential for implementing an efficient and cost-effective solution?
  • A. Create a Snowflake external function that polls the S3 bucket every minute, checks for new files using the LIST command, and then calls the Snowpipe REST API endpoint for each new file.
  • B. Configure Snowpipe to automatically detect new files in the S3 bucket using event notifications, but manually refresh the pipe using SYSTEM $PIPE STATUS periodically to ensure that all files are processed.
  • C. Configure an S3 event notification to trigger a REST API call to the Snowpipe endpoint whenever a new or modified file is added to the S3 bucket. The API call should include the file name in the request.
  • D. Use the 'VALIDATION MODES copy option with 'RETURN_ALL RESULTS = TRUE to validate all data being loaded into the Snowflake table.
  • E. Configure auto-ingest using SQS queue and SNOWPIPE object. No need to manually call the REST API endpoint for data loading.
Answer: C,E
Explanation:
Options A and E are the most efficient and cost-effective solutions. Option A utilizes S3 event notifications to trigger Snowpipe, loading only new files using REST API, and avoids unnecessary polling. Option E uses Snowflake's Auto Ingest feature. Auto ingest eliminates the need for manual Snowpipe calls through REST API and reduces latency. Option B involves inefficient polling. Option C involves unnecessary manual refreshing of the pipe. Option D focuses on data validation during the copy process but doesn't address the core requirement of efficient file detection and triggering.

NEW QUESTION # 192
Consider the following Snowflake UDTF definition written in Python:

Which of the following statements are TRUE regarding the deployment and usage of this UDTF?
  • A. The UDTF will automatically be available in all schemas across all databases in the Snowflake account.
  • B. The UDTF can be called directly in SQL using 'SELECT FROM TABLE(process_json(VARlANT COLUMN));' without any prior registration.
  • C. The library needs to be explicitly installed and configured within the UDTF's environment using a Snowpark session.
  • D. The UDTF needs to be registered using 'session.udtf.register' or 'create or replace function' with the 'imports' clause referencing the Python file, and the handler' specifying the function name.
  • E. The return type of the generator 'yield' must strictly adhere to the declared output schema , or errors will occur during execution.
Answer: D,E
Explanation:
UDTFs in Snowflake require explicit registration using 'session.udtf.register' or 'create or replace function' command, defining the location of the source code (Python file) and the function to be executed. Also, data types of values which is 'yield'ed in the body of UDTF must strictly adhere with Output schema. Libraries from 'snowflake.snowpark' are usually available and does not needs explicit configuration. UDTFs are schema-bound, not automatically available everywhere. Direct call to UDTF without creation isn't possible.

NEW QUESTION # 193
Consider a scenario where you need to transform data in a Snowflake table using a complex custom transformation logic best implemented in Java'. You decide to use a Snowpark Java UDF. You've packaged your Java code into a JAR file and uploaded it to an internal stage named Which of the following steps are necessary and correctly ordered to deploy and use this Java UDF within Snowflake?

  • A. Option A
  • B. Option B
  • C. Option D
  • D. Option E
  • E. Option C
Answer: B
Explanation:
Option B is the most accurate and complete. It explicitly states that the JAR needs to be uploaded first, and then correctly shows the 'CREATE FUNCTION' syntax including 'IMPORTS', 'HANDLER, and specifying the language as 'JAVA". The handler specifies the method to be invoked inside the class. The execution follows the correct order. Option D is incorrect because HANDLER needs full qualified method name instead of the class name. And option E just ensures stage is present and jar file is there, rather explicitly mention upload.

NEW QUESTION # 194
Snowpark DataFrame 'employee_df' contains employee data, including 'employee_id', 'department', and 'salary'. You need to calculate the average salary for each department and also retrieve all the employee details along with the department average salary.
Which of the following approaches is the MOST efficient way to achieve this?
  • A. Use a correlated subquery within the SELECT statement to calculate the average salary for each department for each employee.
  • B. Create a separate DataFrame with average salaries per department, then join it back to the original DataFrame.
  • C. Use 'groupBV to get a dataframe containing average salary by department and then use a Python UDF to iterate through the 'employee_df and add the value to each row
  • D. Create a temporary table with average salaries per department, then join it back to the original DataFrame.
  • E. Use the 'window' function with 'avg' to compute the average salary per department and include it as a new column in the original DataFrame.
Answer: E
Explanation:
Using the 'window' function (Option C) is the most efficient. Window functions are specifically designed for this type of calculation, allowing you to perform aggregations over a subset of rows related to the current row (in this case, employees in the same department) without the overhead of joins or subqueries. Option A, B and E are less efficient due to join and subquery overhead. UDFs are also typically slower than built-in functions.

NEW QUESTION # 195
You have a Snowpark DataFrame 'df_products' with columns 'product id', 'category', and 'price'. You need to perform the following transformations in a single, optimized query using Snowpark Python: 1. Filter for products in the 'Electronics' or 'Clothing' categories. 2. Group the filtered data by category. 3. Calculate the average price for each category. 4. Rename the aggregated column to 'average_price'. Which of the following code snippets demonstrates the most efficient way to achieve this?

  • A. Option A
  • B. Option B
  • C. Option D
  • D. Option E
  • E. Option C
Answer: B
Explanation:
Option B is the most efficient and correct. It uses 'col()' from 'snowflake.snowpark.functions' to properly reference the 'category' and 'price' columns, uses 'isin()' for a more concise and efficient filtering of multiple category values, groups by the category using and calculates the average price with 'avg(col('price')).as_('average_price')'. Option A, C, and D are syntactically incorrect or less efficient ways to accomplish the same task within Snowpark. Option E is incorrect because it utilizes 'to_pandas()' which returns the result as a Pandas DataFrame rather than a Snowpark DataFrame, failing to adhere to the Snowpark environment. While Option D is very similar, it lacks the proper syntax for specifying column references with 'col('category')' in the groupBy and 'col('price')' in the avg function.

NEW QUESTION # 196
......
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