Title: Credible Method To Pass Snowflake DEA-C02 Exam On First Try [Print This Page] Author: peterev446 Time: 3 hour before Title: Credible Method To Pass Snowflake DEA-C02 Exam On First Try P.S. Free & New DEA-C02 dumps are available on Google Drive shared by BootcampPDF: https://drive.google.com/open?id=1Bj2WQgQLWBKzvRswFOTJOpTvi8PS32f0
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You are using Snowpark Python to perform a complex data transformation involving multiple tables and several intermediate dataframes. During the transformation, an error occurs within one of the Snowpark functions, causing the entire process to halt. To ensure data consistency, you need to implement transaction management. Which of the following Snowpark DataFrameWriter options or session configurations would be MOST appropriate for rolling back the entire transformation in case of an error during the write operation to the final target table?
A. Use and manually track intermediate dataframes to delete them in case of failure.
B. Set the session parameter to 'TRUE and wrap the entire transformation within a 'try...except block, explicitly calling in the 'excepts block.
C. Use True)' to automatically rollback the write operation if an error occurs during the write process.
D. Set the session parameter to 'TRUE to ensure all DDL operations are atomic and can be rolled back.
E. Wrap the entire transformation in a stored procedure and call 'SYSTEM$QUERY within the stored procedure's exception handler.
Answer: B
Explanation:
Setting 'TRANSACTION_ABORT ON ERROR to 'TRUE ensures that any error will abort the transaction. Wrapping the code in a 'try...except' block allows you to catch the exception and explicitly call 'session.rollback()' to undo any changes made within the transaction. Option A is relevant to DDL operations, not general data transformations. Option B involves manual tracking, which is error-prone. Option D is not a valid Snowpark DataFrameWriter option. Option E, while potentially useful for cancelling queries, does not directly manage transaction rollback from within the Snowpark session.
NEW QUESTION # 245
You need to create a UDF in Snowflake to perform complex data validation. This UDF must access an external API to retrieve validation rules based on the input data'. You want to ensure that sensitive API keys are not exposed within the UDF's code and that the external API call is made securely. Which of the following approaches is the MOST secure and appropriate for this scenario?
A. Store the API key in a Snowflake table with strict access controls, and retrieve it within the UDF using a SELECT statement. Use 'SECURITY INVOKER to ensure the UDF uses the caller's privileges when accessing the table.
B. Pass the API key as an argument to the UDF when it is called. Rely on the caller to provide the correct key and keep it secure.
C. Use a Snowflake Secret to securely store the API key. Retrieve the secret within the UDF using the 'SYSTEM$GET_SECRET function, and use 'SECURITY INVOKER with caution or define the UDF as 'SECURITY DEFINER with appropriate role based access controls .
D. Hardcode the API key directly into the UDF's JavaScript code, obfuscating it with base64 encoding.
E. Store the API key as an environment variable within the UDF's JavaScript code. Snowflake automatically encrypts environment variables for security.
Answer: C
Explanation:
Using Snowflake Secrets is the most secure method for storing sensitive information like API keys. 'SYSTEM$GET_SECRET allows you to retrieve the secret within the UDF without exposing the key in the code. Using 'SECURITY INVOKER should be done with caution to not broaden access unintentionally. Storing API keys in tables, passing them as arguments, environment variables, or hardcoding them are all insecure practices.
NEW QUESTION # 246
You have an external table in Snowflake pointing to data in Azure Blob Storage. The data consists of customer transactions, and new files are added to the Blob Storage daily You want to ensure that Snowflake automatically picks up these new files and reflects them in the external table without manual intervention. However, you are observing delays in Snowflake detecting the new files. What are the potential reasons for this delay and how can you troubleshoot them? (Choose two)
A. The Azure Event Grid notification integration is not properly configured to notify Snowflake about new file arrivals in the Blob Storage.
B. The external table's 'AUTO_REFRESH' parameter is set to 'FALSE', which disables automatic metadata refresh.
C. The storage integration associated with the external table does not have sufficient permissions to access the Blob Storage.
D. Snowflake's internal cache is not properly configured; increasing the cache size will solve the problem.
E. The file format used for the external table is incompatible with the data files in Blob Storage.
Answer: A,B
Explanation:
The two primary reasons for delays in Snowflake detecting new files in an external table are: 1) Incorrect configuration of the cloud provider's notification service (Azure Event Grid in this case). Snowflake relies on these notifications to be informed about new file arrivals. If the integration isn't set up correctly, Snowflake won't know when to refresh the metadata. 2) The parameter must be set to ' TRUE' for automatic metadata refresh to occur. If it's set to FALSE , manual refreshes are required using 'ALTER EXTERNAL TABLE ... REFRESH". Options D and E, although possible issues, won't directly cause a delay in detecting new files, but rather cause issues accessing files after detection. Option C is irrelevant as Snowflake's caching mechanism does not directly impact external table metadata refresh.
NEW QUESTION # 247
You have created a Snowflake Iceberg table that points to data in an AWS S3 bucket. After some initial data ingestion, you realize that the schema in the Iceberg table does not perfectly match the schema of the underlying Parquet files in S3. Specifically, one of the columns in the Iceberg table is defined as 'VARCHAR , while the corresponding column in the Parquet files is stored as 'INT. What will be the most likely behavior when you query this Iceberg table in Snowflake?
A. The query will succeed, but the 'VARCHAR column will contain 'NULL' values for all rows where the underlying Parquet files contain 'INT' values.
B. Snowflake will automatically cast the SINT' data in the Parquet files to 'VARCHAR during query execution, and the query will succeed without any errors or warnings.
C. The query will fail with an error indicating a data type mismatch between the Iceberg table schema and the underlying Parquet file schema.
D. The query will succeed, but the result will be unpredictable and may vary depending on the specific data values in the Parquet files.
E. Snowflake will attempt to cast the data, and if a cast fails (e.g., 'INT' value is too large to fit in 'VARCHAR), the query will return an error only for those specific rows. Other rows will be processed correctly.
Answer: C
Explanation:
Snowflake enforces schema validation for Iceberg tables. If the data types in the Iceberg table schema do not match the data types in the underlying Parquet files, the query will fail with an error. This is because Snowflake relies on the Iceberg metadata to understand the data types and structure of the data in the Parquet files. A mismatch indicates a problem with the Iceberg table definition or the underlying data and should be corrected to ensure data integrity. While Snowflake is often flexible with implicit casting, in the context of Iceberg tables and schema enforcement, a type mismatch will lead to a query failure.
NEW QUESTION # 248
You are tasked with creating a UDTF in Snowflake to perform a complex data transformation that requires external libraries (e.g., for advanced string manipulation or data analysis). The transformation involves cleaning and standardizing addresses from a table containing millions of customer records. Which language and approach would be most appropriate and efficient for this scenario?
A. Python UDTF leveraging Anaconda packages (e.g., 'addressparser' , 'pandas') for advanced address parsing and standardization, utilizing Snowflake's optimized execution environment for Python.
B. SQL UDF with nested CASE statements for address standardization.
C. Scala UDTF leveraging sbt to manage dependencies to achieve address parsing and standardization.
D. JavaScript UDF utilizing regular expressions for simple string replacements.
E. Java UDTF with necessary JAR files uploaded to Snowflake's internal stage, leveraging external libraries for address parsing and standardization.
Answer: A
Explanation:
Python UDTFs with Anaconda packages offer the best balance of flexibility, performance, and ease of use for complex data transformations requiring external libraries. Snowflake's integration with Anaconda allows for the seamless use of popular data science and engineering libraries, making Python UDTFs ideal for tasks like address standardization. Java can be useful, but the overhead of JAR management and potentially less efficient integration with Snowflake's execution engine can be a disadvantage. SQL and JavaScript offer limited expressiveness for complex tasks requiring external libraries. While Scala is powerful, it can present a steeper learning curve and may not be as widely adopted as Python within the Snowflake ecosystem for UDTFs.
NEW QUESTION # 249
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