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Snowflake DEA-C02 Exam Syllabus Topics:

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

1. You are tasked with loading data from a set of highly nested JSON files into Snowflake. Some files contain an inconsistent structure where a particular field might be a string in some records and an object in others. You want to avoid data loss and ensure that you capture both string and object representations of the field. What is the most efficient approach to achieve this, minimizing data transformation outside of Snowflake?

A) Create two separate external tables, one with the field defined as VARCHAR and another with the field defined as VARIANT. Load data into both, then UNION the results in a view.
B) Define the field as a VARCHAR in an internal stage and use a COPY INTO statement with the VALIDATE function to identify records with object representations. Load the valid VARCHAR values. Create a separate table for the invalid object representations identified during validation.
C) Use a single external table with the field defined as VARIANT. During data loading, use the TRY CAST function within a SELECT statement to convert the field to VARCHAR when possible,V otherwise retain the VARIANT representation. Handle further processing in subsequent views or queries.
D) Pre-process the JSON files using a scripting language (e.g., Python) to transform object representations to string representations before loading them into Snowflake. This ensures consistent data type for the field.
E) Define the field in the external table as VARCHAR. During data loading, use a UDF written in Python or Java to handle the different data types, transforming objects to strings. This approach requires deploying the UDF to Snowflake.

2. You are designing a Snowflake alert system for a data pipeline that loads data into a table named 'ORDERS'. You want to trigger an alert if the number of rows loaded per hour falls below a threshold, indicating a potential issue with the data source. You need to create an alert that is triggered based on the count of rows. Consider the code snippet below and the additional requirements. Assume that the table exists and the connection is successful.

A) Create a Snowflake Stream on the 'ORDERS' table. Then create an Alert that triggers based on the metadata column, comparing it to the threshold value. This allows for real-time monitoring of data changes.
B) Create a Snowflake task that runs every hour, executes a query to count the rows loaded in the past hour and triggers an alert using 'SYSTEM$SEND_EMAIC if the count is below the threshold. No need to create a Snowflake alert.
C) Create a Snowflake Alert that executes a SQL query to count the number of rows loaded into the 'ORDERS table within the last hour. Configure the alert to trigger when the count is below the defined threshold. Use a Notification Integration to send alerts to a monitoring system.
D) Use Snowflake's Resource Monitor feature and adjust the credit quota to trigger an alert if the credit usage exceeds the threshold for the virtual warehouse processing data pipeline, indirectly indicating that performance is degraded or data volume has changed significantly.
E) You cannot create alerts based on a rolling hourly window within Snowflake. Alerts can only be based on fixed time intervals.

3. You are designing a Snowpark Python application to process streaming data from a Kafka topic and land it into a Snowflake table 'STREAMED DATA. Due to the nature of streaming data, you want to achieve the following: 1. Minimize latency between data arrival and data availability in Snowflake. 2. Ensure exactly-once processing semantics to prevent data duplication. 3. Handle potential schema evolution in the Kafka topic without breaking the pipeline. Which combination of Snowpark and Snowflake features, applied with the correct configuration, would BEST satisfy these requirements? Select all that apply.

A) Use Snowflake's native Kafka connector to load data into a staging table. Then, use a Task and Stream combination, using a Snowpark Python UDF, to transform and load the data into 'STREAMED DATA' within a single transaction, handling schema evolution by casting columns to their new types or dropping missing column data.
B) Use Snowflake Connector for Kafka to load data into a staging table. Then, use Snowpark Python to transform and load the data into 'STREAMED_DATR within a single transaction. Implement schema evolution logic in the Snowpark code to handle changes in the Kafka topic schema.
C) Use Snowpipe with auto-ingest and configure it to trigger on Kafka topic events. Define a VARIANT column in 'STREAMED_DATX to handle schema evolution.
D) Utilize Snowflake Streams on in conjunction with Snowpark to transform and cleanse the data after it has been ingested by Snowpipe. Apply a merge statement to update an external table of parquet files.
E) Implement a Snowpark Python UDF that consumes data directly from the Kafka topic using a Kafka client library. Write data into 'STREAMED_DATX within a single transaction. Use a structured data type for the 'STREAMED DATA'.

4. A data engineer is using Snowpark Scala to create a UDF that calculates the distance between two geographical coordinates (latitude and longitude) using the Haversine formula'. The function should accept four 'Double' values (latl, lonl , lat2, lon2) and return the distance in kilometers as a 'Double'. The UDF must be named 'haversine distance'. What is the correct Scala code to define and register this UDF with Snowflake, including the import statements required for using Snowpark functions?

A) Option C
B) Option E
C) Option B
D) Option D
E) Option A

5. You have a table named 'TRANSACTIONS with the following definition: CREATE TABLE TRANSACTIONS ( TRANSACTION ID NUMBER, TRANSACTION DATE DATE, CUSTOMER_ID NUMBER, AMOUNT PRODUCT_CATEGORY VARCHAR(50) Users frequently query this table using filters on both 'TRANSACTION_DATE and 'PRODUCT CATEGORY. You want to optimize query performance. What is the MOST effective approach?

A) Create separate indexes on 'TRANSACTION DATE' and 'PRODUCT CATEGORY.
B) Cluster the table on ' TRANSACTION_DATE and then create a materialized view filtered by PRODUCT_CATEGORY&.
C) Cluster the table using a composite key of '(TRANSACTION_DATE, PRODUCT CATEGORY)'.
D) Create a materialized view joining 'TRANSACTIONS' with a dimension table containing product category information.
E) Partition the table by 'TRANSACTION DATE

Solutions: