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Snowflake DEA-C02 Exam Syllabus Topics:

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

1. You are tasked with creating a Snowpark Java stored procedure to calculate a complex, custom rolling average for a time series dataset. This rolling average requires access to external libraries for statistical calculations. Which of the following steps are necessary to successfully deploy and execute this stored procedure?

A) Grant the necessary privileges on the stage and the database to the role executing the stored procedure.
B) Package the Java code and all necessary external libraries into a single JAR file.
C) Upload the JAR file to a Snowflake stage.
D) Create a stored procedure in Snowflake, specifying the fully qualified path to the JAR file in the stage, the handler class, and the return type.
E) All of the above.

2. A data engineer is tasked with optimizing a Snowflake data pipeline that ingests data from multiple external sources, transforms it, and loads it into a reporting table. The pipeline uses a series of Snowflake tasks orchestrated with a root task and child tasks. Performance monitoring shows inconsistent execution times for the transformation tasks. Which of the following strategies would provide the MOST granular insights into the performance bottlenecks within the pipeline and allow for targeted optimization?

A) Use Snowflake's Resource Monitors to track overall warehouse consumption and assume that high consumption during transformation tasks indicates a bottleneck within those tasks.
B) Enable query profiling for all queries executed within the transformation tasks using 'ALTER SESSION SET QUERY PROFILE = 'ON" , then analyze the query profiles for performance bottlenecks after each task run.
C) Rely solely on the Snowflake web UI's Task History view to identify slow-running tasks.
D) Implement a custom logging mechanism within the transformation tasks to record execution times for each stage of the transformation process, and store these logs in a Snowflake table for analysis.
E) Leverage Snowflake's event tables like QUERY HISTORY and TASK HISTORY in the ACCOUNT USAGE schema joined with custom metadata tags to correlate specific transformation steps to execution times and resource usage. Also set up alerting based on defined performance thresholds.

3. You are tasked with designing a data pipeline that ingests JSON data from an external stage (AWS S3). The JSON files contain records for various product types, each having a different set of attributes. Some product types might have attributes that are not present in other types. You want to create a single Snowflake table that can accommodate all product types without defining a rigid schema upfront and also be queryable efficiently. Which of the following approaches, combining external tables, schema evolution and querying, would be MOST effective? (Choose two)

A) Create a stored procedure that dynamically infers the schema from the JSON files and creates a new Snowflake table based on the inferred schema.
B) Create a single external table with a VARIANT column to store the entire JSON record for each product. Use LATERAL FLATTEN to extract specific attributes during querying.
C) Load all the data into a raw Snowflake internal table. Use dynamic SQL to infer distinct product types and create views on top of the raw table for each product type.
D) Create a single external table with a VARIANT column and use the 'VALIDATE function to identify and handle schema inconsistencies during data loading.
E) Create a separate external table for each product type, defining the schema for each table based on the attributes present in the corresponding JSON files.

4. You are tasked with designing a solution to ingest a continuous stream of unstructured log data from various sources into Snowflake. The log data includes text, JSON, and XML formats. The goal is to efficiently store the data, allow for flexible querying, and minimize storage costs. Which of the following approaches would BEST address these requirements? (Select TWO)

A) Ingest all data 'as is' into a raw staging table. Then create a Task that use Python UDF to parse data and save it to different tables as required
B) Pre-process the log data to convert all formats into a standardized JSON format before ingestion.
C) Create separate tables for each log data format (text, JSON, XML).
D) Ingest all log data into a single VARIANT column in a Snowflake table.
E) Use Snowflake's external functions to parse the log data during query execution.

5. You have a Snowflake table called 'RAW ORDERS that contains semi-structured JSON data in a column named 'ORDER DETAILS. You need to extract specific fields from the JSON data, perform some data type conversions, and then load the transformed data into a relational table named 'CLEAN ORDERS'. Your requirements are as follows: 1. Extract the (STRING) from the JSON and store it as 'ORDER ID (NUMBER). 2. Extract the (STRING) from the JSON and store it as 'CUSTOMER ID (NUMBER). 3. Extract the 'order_date' (STRING) from the JSON and store it as 'ORDER DATE' (DATE). 4. Extract (STRING) from the JSON and store it as 'TOTAL AMOUNT' (FLOAT). Which of the following Snowpark Python code snippets correctly transforms the data and loads it into the 'CLEAN ORDERS table using a combination of Snowpark DataFrame operations and SQL? Assume that session 'sp' is already initialized.

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

Solutions: