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Snowflake DEA-C02 Exam Syllabus Topics:

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

1. Consider a table with columns and 'customer _ region'. You want to implement both a Row Access Policy (RAP) and an Aggregation Policy on this table. The RAP should restrict access to orders based on the user's region, defined in a session variable 'CURRENT REGION'. Users should only see orders from their region. The Aggregation Policy should mask order totals for regions other than the user's region when aggregating data'. In other words if someone attempts to aggregate ALL region's totals, the aggregation will only include their region. Which statements about implementing this scenario are true?

A) The Aggregation Policy is evaluated before the RAP, ensuring that even if users try to bypass the RAP by aggregating across all regions, the results will be masked appropriately according to 'CURRENT REGION'.
B) Using external functions in RAPs can introduce performance overhead, especially if the external function is complex or slow to execute.
C) The RAP should be applied first to filter the data, and then the Aggregation Policy will apply to the filtered data, only masking aggregated values within the user's region.
D) You can use the function within both the RAP and Aggregation Policy to control access based on user roles in addition to region.
E) You cannot use session variables directly in Row Access Policies; you must pass the session variable as an argument to a user-defined function (UDF) called by the policy.

2. 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) Upload the JAR file to a Snowflake stage.
B) Grant the necessary privileges on the stage and the database to the role executing the stored procedure.
C) 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.
D) Package the Java code and all necessary external libraries into a single JAR file.
E) All of the above.

3. You have a VARIANT column named 'raw_data' in a Snowflake table 'eventS , containing nested JSON data'. You need to extract specific fields Cevent_id', 'timestamp' , and 'user.user_id') and load them into a relational table 'structured_events' with columns 'event_id' , 'timestamp' , and 'user_id', respectively. However, some entries may be missing the 'user' object. Which of the following SQL statements will achieve this while handling missing 'user' objects gracefully and ensuring data integrity, and also efficiently handle potentially large JSON payloads?

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

4. You have a large dataset stored in AWS S3 in Parquet format. The data is constantly updated by an external process, but you need to run read-only analytical queries against the most current data in Snowflake without ingesting it. Which approach is the MOST efficient and cost-effective way to achieve this, considering minimal latency for query results?

A) Create a Snowflake external table pointing to the S3 location. Manually refresh the external table metadata periodically using the 'ALTER EXTERNAL TABLE REFRESH' command.
B) Create an external table in Snowflake pointing to the S3 location. Configure automatic data refresh using the 'AUTO REFRESH' parameter and a notification integration.
C) Create a view on top of the S3 files using a custom Java UDF to read Parquet data directly. Then, query the view.
D) Use Snowpipe to continuously ingest the Parquet files from S3 into a Snowflake table.
E) Create a Snowflake internal stage, load the Parquet files into the stage periodically, and create a table from the staged files.

5. 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) 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.
B) Create a single external table with a VARIANT column and use the 'VALIDATE function to identify and handle schema inconsistencies during data loading.
C) 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.
D) 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.
E) Create a stored procedure that dynamically infers the schema from the JSON files and creates a new Snowflake table based on the inferred schema.

Solutions: