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

1. A data pipeline ingests clickstream data from various sources into a raw Snowflake table CRAW CLICKS). A transformation job then processes this data and loads it into a more structured 'CLICK EVENTS table, performing filtering, cleaning, and data enrichment. The data engineering team notices significant performance bottlenecks during this transformation process, leading to data freshness issues.
The team wants to optimize this process, considering the following:

A) Create a materialized view on top of 'RAW CLICKS' that pre-computes the necessary transformations and aggregations, allowing the 'CLICK EVENTS' table to be populated directly from the materialized view.
B) Use a larger virtual warehouse for the transformation job and partition the 'RAW CLICKS table on the ingestion timestamp to improve data pruning and reduce the amount of data processed during the transformation.
C) Optimize the transformation queries by identifying and rewriting inefficient SQL patterns, ensuring appropriate use of joins, filtering conditions, and data type conversions.
D) Replace the transformation job with a series of smaller, more specialized jobs, each running on a separate virtual warehouse optimized for the specific task, and orchestrate these jobs using a data pipeline tool.
E) Implement a change data capture (CDC) mechanism on the source systems to only ingest changed data into 'RAW CLICKS, reducing the overall data volume and the amount of data processed by the transformation job.

2. A data engineer wants to use Snowpark to read a large CSV file from an external stage and infer the schema automatically. However, some columns in the CSV contain data that Snowflake cannot automatically infer the type for. Which of the following code snippets demonstrates the CORRECT way to read the CSV file with schema inference and handle potentially problematic columns by explicitly specifying their data types?

A)

B)

C)

D)

E)

3. You are tasked with designing a data pipeline to load data from an Azure Blob Storage container into Snowflake using an external stage. The data is in CSV format, compressed using GZIP. The container contains millions of small CSV files. To optimize the data loading process and minimize cost, which of the following strategies would you implement, considering both stage configuration and COPY INTO options? Choose TWO that apply.

A) Use the 'MATCH BY COLUMN NAME = CASE INSENSITIVE option with a copy transformation in the 'COPY INTO' statement to ensure that the column order in the CSV files doesn't affect the data load.
B) Leverage Snowflake's Snowpipe with a REST API endpoint to trigger data loads whenever new files are available in the Azure Blob Storage container.
C) Consolidate the small CSV files in the Azure Blob Storage container into larger files before loading them into Snowflake. This reduces the overhead of processing numerous small files.
D) Create a pipe object with 'AUTO INGEST = TRUE to automatically ingest new files as they are added to the Azure Blob Storage container. This ensures near real-time data ingestion.
E) Use the 'VALIDATION MODE = RETURN ERRORS option in the 'COPY INTO' statement to identify and correct any data quality issues during the load. This ensures that only clean data is loaded into Snowflake.

4. 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 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$PIPE STATUS and address any errors by manually retrying failed loads with 'ALTER PIPE REFRESH;'
B) Create a Stream on the target table and a Snowflake Task. The task executes a COPY INTO command into a staging table when the Stream has data and then a MERGE statement. Schedule the task to run continuously with 'WHEN SYSTEM$STREAM HAS but limit the 'WAREHOUSE SIZE' to
C) Create a 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.
D) 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.
E) 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.

5. You need to create a development environment from a production schema called 'PRODUCTION SCHEMA. You decide to clone the schema'. Which of the following statements are correct regarding the impact of cloning a schema in Snowflake? (Select all that apply)

A) External tables are also cloned when cloning a schema, but the underlying data files in cloud storage are not duplicated.
B) All tables, views, and user-defined functions (UDFs) within the 'PRODUCTION_SCHEMX will be cloned to the new development schema.
C) Cloning a schema automatically clones all tasks and streams associated with tables in the schema but only if the clone is executed at the Database Level.
D) Sequences in the cloned schema will continue from where they left off in the original 'PRODUCTION SCHEMA' if no operations are performed on sequence object, if the sequence is updated after cloning then these sequences are fully independent.
E) Cloned schemas consume twice the storage as the source schema immediately after cloning as the underlying data is duplicated.

Solutions: