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Snowflake SnowPro Advanced: Data Engineer (DEA-C02) Sample Questions:

1. You are tasked with implementing a data loading process for a table 'CUSTOMER DATA' in Snowflake. The source data is in Parquet format on Azure Blob Storage and contains personally identifiable information (PII). You must ensure that the data is loaded securely, masked during the loading process, and that only authorized users can access the unmasked data after the load. Assume you have already created a stage pointing to the Azure Blob Storage. Which of the following steps should you take to achieve this?

A) Use a 'COPY command with the 'ENCRYPTION = (TYPE = 'AZURE CSE', KEY = option to encrypt the data during load. Implement role-based access control to restrict access to the table.
B) Load the data without masking. Implement dynamic data masking policies on the table's PII columns using Snowflake's Enterprise edition features. Use a 'COPY' command with ERROR = CONTINUE
C) Load the data directly into a 'VARIANT column. Use a SQL transformation with 'FLATTEN' and masking policies on the extracted columns.
D) Use a 'COPY command with the 'TRANSFORM' clause and JavaScript UDFs to mask the PII data during the load process. Implement masking policies on the 'CUSTOMER DATA' table to restrict access to the unmasked data.
E) Use a 'COPY command with 'ON ERROR = SKIP FILE'. Use a Task to monitor load failures and trigger alerts.

2. Consider a scenario where you have a large dataset of sensor readings stored in a Snowflake table called 'SENSOR DATA'. You need to build an external function to perform complex calculations on these readings using a custom Python library hosted on AWS Lambda'. The calculation requires significant computational resources, and you want to optimize the data transfer between Snowflake and the Lambda function. The following SQL is provided: CREATE OR REPLACE EXTERNAL FUNCTION ARRAY) RETURNS ARRAY VOLATILE MAX BATCH ROWS = 2000 RETURNS NULL ON NULL INPUT API INTEGRATION = aws_lambda_integration AS 'arn:aws:lambda:us-east-1:123456789012:function:sensorProcessor'; Which of the following options would further optimize the performance and reduce data transfer costs, assuming the underlying Lambda function is correctly configured and functional?

A) Compress the data before sending it to the external function and decompress it within the Lambda function. Update the Lambda function to compress the array of results before sending it back to Snowflake and use Snowflake+s functions to decompress it.
B) Increase the 'MAX BATCH ROWS' parameter to the maximum allowed value to send larger batches of data to the external function. Ensure Lambda function memory is increased appropriately.
C) Rewrite the custom Python library in Java and create a Snowflake User-Defined Function (UDF) instead of using an external function.
D) Convert the input data to a binary format (e.g., using 'TO_BINARY and FROM_BINARY' functions in Snowflake) before sending it to the Lambda function, and decode it in Lambda to reduce the size of the data being transmitted.
E) Reduce the number of columns passed to the external function by performing pre-aggregation or filtering on the data within Snowflake before calling the function.

3. You are designing a continuous data pipeline to load data from AWS S3 into Snowflake. The data arrives in near real-time, and you need to ensure low latency and minimal impact on your Snowflake warehouse. You plan to use Snowflake Tasks and Streams. Which of the following approaches would provide the most efficient and cost-effective solution for this scenario, considering data freshness and resource utilization?

A) Create a Stream on the target table and a Snowflake Task. The task executes a COPY INTO command into a staging table when the Stream has data and then a MERGE statement. Schedule the task to run continuously with 'WHEN SYSTEM$STREAM HAS but limit the 'WAREHOUSE SIZE' to
B) Configure an AWS SQS queue to receive S3 event notifications whenever a new file is uploaded. Use a Lambda function triggered by the SQS queue to invoke a Snowflake stored procedure. This stored procedure executes a COPY INTO command to load the specific file into Snowflake. Use 'ON ERROR = CONTINUE' during COPY INTO.
C) Create a single, root Snowflake Task that triggers every 5 minutes, executing a COPY INTO command to load all new data from the S3 bucket into a staging table, followed by a MERGE statement to update the target table. Use 'VALIDATE ( STAGE NAME '0'.////' before COPY INTO.
D) Create a Stream on the target table and a Snowflake Task that runs every minute. The task executes a MERGE statement to apply changes from the Stream to the target table, filtering the Stream data using the 'SYSTEM$STREAM GET TABLE TIMESTAMP function to process only newly arrived data since the last task execution. Use 'WHEN SYSTEM$STREAM HAS to run the Task.
E) Create a Pipe object in Snowflake using Snowpipe and configure the S3 bucket for event notifications to the Snowflake-provided SQS queue. Monitor the Snowpipe status using 'SYSTEM$PIPE STATUS and address any errors by manually retrying failed loads with 'ALTER PIPE REFRESH;'

4. You've created a JavaScript stored procedure using Snowpark to transform data'. The stored procedure is failing, and you suspect an issue with how Snowpark is handling null values during a join operation. Given two Snowpark DataFrames, and 'df2 , what is the expected behavior when performing an inner join on a column containing null values in both DataFrames, and how can you mitigate potential issues?

A) The inner join will treat null values as equal, resulting in rows where the join column is null in both DataFrames being included in the result. To avoid this, you should filter out null values before the join.
B) The inner join will automatically exclude rows where the join column is null in either DataFrame. There is no need for explicit null handling.
C) Inner Join will not throw an error, and will exclude the rows where join column is null. If you need to join records with null values, pre-processing dataframes using to replace null with a valid sentinel value before performing the join is one way to handle this.
D) The behavior of the inner join with null values is undefined and may vary depending on the data types and the specific version of Snowpark. Explicit null handling is always required.
E) The inner join will exclude rows where the join column is null in either DataFrame. To include these rows, you must use a full outer join instead.

5. A Snowflake data warehouse contains a table named 'SALES TRANSACTIONS' with the following columns: 'TRANSACTION ID', 'PRODUCT D', 'CUSTOMER D', 'TRANSACTION DATE, and 'SALES AMOUNT'. You need to optimize a query that calculates the total sales amount per product for a given month. The 'SALES TRANSACTIONS' table is very large (billions of rows), and queries are slow. Given the following initial query: SELECT PRODUCT ID, SUM(SALES AMOUNT) AS TOTAL SALES FROM SALES TRANSACTIONS WHERE TRANSACTION DATE BETWEEN '2023-01-07' AND '2023-01-31' GäOUP BY PRODUCT ID; Which of the following actions, when combined, would MOST effectively improve the performance of this query?

A) Create a clustering key on 'PRODUCT_ID and 'TRANSACTION_DATE columns in the 'SALES_TRANSACTIONS' table.
B) Create a materialized view that pre-aggregates the total sales amount per product and month.
C) Create a temporary table with the results of the query and query that table instead.
D) Convert the column to a VARCHAR data type.
E) Increase the virtual warehouse size to the largest available size.

Solutions: