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Snowflake DEA-C02 Exam Syllabus Topics:

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

1. You are designing a data pipeline using Snowpipe to ingest data from multiple S3 buckets into a single Snowflake table. Each S3 bucket represents a different data source and contains files in JSON format. You want to use Snowpipe's auto-ingest feature and a single Snowpipe object for all buckets to simplify management and reduce overhead. However, each data source has a different JSON schem a. How can you best achieve this goal while ensuring data is loaded correctly and efficiently into the target table?

A) Create a separate Snowpipe for each S3 bucket. Although this creates more Snowpipe objects, it allows you to specify a different FILE FORMAT and transformation logic for each data source.
B) Use a single Snowpipe and leverage Snowflake's ability to call a user-defined function (UDF) within the 'COPY INTO' statement to transform the data based on the S3 bucket path. The UDF can parse the bucket path and apply the appropriate JSON schema transformation.
C) Since Snowpipe cannot handle multiple schemas with a single pipe, pre-process the data in S3 using an AWS Lambda function to transform all files into a common schema before they are ingested by the Snowpipe.
D) Use a single Snowpipe with a generic FILE FORMAT that can handle all possible JSON schemas. Implement a VIEW on top of the target table to transform and restructure the data based on the source bucket.
E) Use a single Snowpipe and leverage Snowflake's VARIANT data type to store the raw JSON data. Create separate external tables, each pointing to a specific S3 bucket, and use SQL queries to transform and load the data into the target table.

2. You are designing a data ingestion process that involves loading data from an external stage. The data is partitioned into multiple files based on date. The stage is configured to point to the root directory of the partitioned dat a. You want to efficiently load only the data for a specific date (e.g., '2023-01-15') using the 'COPY' command. Assume your stage name is 'my _ stage' , your table is 'my_table', your date column is named 'event_date', and the files in the stage are named in the format 'data YYYY-MM-DD.csv'. Which of the following options allows you to selectively load the data for the specific date? (Select ALL that apply)

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

3. You have a Snowflake table 'orders_raw' with a VARIANT column named 'order detailS that contains an array of order items represented as JSON objects. Each object has 'item id', 'quantity' , and 'price'. You need to calculate the total revenue for each order. Which SQL statement efficiently flattens the array and calculates the total revenue using LATERAL FLATTEN and appropriate casting?

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

4. 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) 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.
B) Package the Java code and all necessary external libraries into a single JAR file.
C) All of the above.
D) Upload the JAR file to a Snowflake stage.
E) Grant the necessary privileges on the stage and the database to the role executing the stored procedure.

5. 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 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.
B) 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$STREAM HAS but limit the 'WAREHOUSE SIZE' to
C) Create a Pipe object in Snowflake using Snowpipe and configure the S3 bucket for event notifications to the Snowflake-provided SQS queue. Monitor the Snowpipe status using 'SYSTEM$PIPE STATUS and address any errors by manually retrying failed loads with 'ALTER PIPE REFRESH;'
D) 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.
E) 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.

Solutions: