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

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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) 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.
B) 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.
C) 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.
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) 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.

2. You are using Snowpipe to load data from an AWS S3 bucket into Snowflake. The data files are compressed using GZIP and are being delivered frequently. You have observed that the pipe's backlog is increasing and data latency is becoming unacceptable. Which of the following actions could you take to improve Snowpipe's performance? (Select all that apply)

A) Increase the virtual warehouse size associated with the pipe.
B) Ensure that the S3 event notifications are correctly configured and that there are no errors in the event delivery mechanism.
C) Check if the target table has any active clustering keys defined which could be causing slow down
D) Optimize the file size of the data files in S3. Smaller files are processed faster by Snowpipe.
E) Reduce the number of columns in the target Snowflake table. Fewer columns reduce the overhead of data loading.

3. 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 E
B) Option C
C) Option B
D) Option A
E) Option D

4. You are developing a data transformation pipeline in Snowpark Python to aggregate website traffic data'. The raw data is stored in a Snowflake table named 'website_events' , which includes columns like 'event_timestamp' , 'user_id', 'page_urr , and 'event_type'. Your goal is to calculate the number of unique users visiting each page daily and store the aggregated results in a new table named Considering performance and resource efficiency, select all the statements that are correct:

A) Caching the 'website_eventS DataFrame using 'cache()' before performing the aggregation is always beneficial, especially if the data volume is large.
B) Using is the most efficient method for writing the aggregated results to Snowflake, regardless of data size.
C) Applying a filter early in the pipeline to remove irrelevant 'event_type' values can significantly reduce the amount of data processed in subsequent aggregation steps.
D) Using followed by is an efficient approach to calculate unique users per page per day.
E) Defining the schema for the table before writing the aggregated results is crucial for ensuring data type consistency and optimal storage.

5. 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) 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.
B) The query will fail with an error indicating a data type mismatch between the Iceberg table schema and the underlying Parquet file schema.
C) The query will succeed, but the result will be unpredictable and may vary depending on the specific data values in the Parquet files.
D) The query will succeed, but the 'VARCHAR column will contain 'NULL' values for all rows where the underlying Parquet files contain 'INT' values.
E) 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.

Solutions: