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

1. You have a requirement to continuously load data from a cloud storage location into a Snowflake table. The source data is in Avro format and is being appended to the cloud storage location frequently. You want to automate this process using Snowpipe. You've already created the Snowpipe and the associated stage and file format. However, you notice that some files are being skipped during the ingestion process, and data is missing in your Snowflake table. What is the MOST likely reason for this issue, assuming all necessary permissions and configurations (stage, file format, pipe definition) are correctly set up?

A) Snowflake does not support Avro format for Snowpipe.
B) The file format definition in Snowflake is incompatible with the Avro schema.
C) The cloud storage event notifications are not properly configured to trigger Snowpipe.
D) The data files in cloud storage are not being automatically detected by Snowpipe.
E) The Snowpipe is paused due to exceeding the daily quota.

2. You have a directory table 'my_directory_table' pointing to a stage containing CSV files with headers. You need to query the directory table to find all files modified in the last 24 hours and load those CSV files using COPY INTO into a target table Assume the target table exists and has appropriate schema'. Which of the following SQL statements, or set of statements, will accomplish this efficiently? Note: Consider efficient file loading.

A)

B)

C)

D)

E)

3. You are tasked with creating a resilient data ingestion pipeline using Snowpipe and external tables on AWS S3. The data consists of JSON files, some of which may occasionally contain invalid JSON structures (e.g., missing closing brackets, incorrect data types). You want to ensure that even if some files are corrupted, the valid data is still ingested into your target Snowflake table, and the corrupted files are logged for later investigation. Which of the following steps would BEST achieve this?

A) Set the 'ON ERROR option to 'ABORT STATEMENT in the Snowpipe definition. This will stop the entire Snowpipe process when a JSON error is detected, allowing you to manually investigate and fix the corrupted files before restarting the pipeline.
B) Configure Snowpipe to use the 'ON ERROR = 'SKIP FILE" copy option and then create a separate task to query the 'VALIDATION MODE metadata column in the external table to identify and log the corrupted files.
C) Use Snowflake's => 'JSON', job_id => function against the external stage before ingesting data with Snowpipe to pre-validate files. Then ingest only validated files to your target table
D) Create a custom error handler using a Snowflake stored procedure that catches the 'JSON PARSER ERROR exception and logs the filename to a separate error table. Use the ERROR = 'CONTINUE" copy option in the Snowpipe definition.
E) Configure the external table definition with 'VALIDATION MODE = 'RETURN ERRORS" and then create a view on top of the external table that filters out rows where the 'METADATA$FILE ROW NUMBER column contains errors.

4. You are tasked with building a data pipeline that ingests customer interaction data from multiple microservices using Snowpipe Streaming. Each microservice writes data in JSON format to its own Kafka topic. You need to design an efficient and scalable solution to ingest this data into a single Snowflake table, while ensuring data integrity and minimizing latency. Consider these constraints: 1. High data volume with variable ingestion rates. 2. The need to correlate data from different microservices based on a common 'customer id'. 3. Potential for schema evolution in the microservices. Given these requirements and constraints, which of the following architectural approaches, leveraging Snowpipe Streaming features and Snowflake capabilities, would be the MOST appropriate and robust?

A) Use a single Snowpipe Streaming client to ingest data from all Kafka topics into a single VARIANT column in the Snowflake table. Then, use Snowflake's external functions to transform and load the data into the final target table based on the 'customer_id'
B) Create a separate Snowpipe Streaming client for each Kafka topic, ingesting data into separate staging tables. Then, use a scheduled task to merge the data into the final target table based on 'customer id'.
C) Develop a Spark Streaming application that reads data from Kafka, transforms it, and then uses the Snowflake Connector for Spark to write the data to Snowflake in micro-batches.
D) Implement a custom Kafka Connect connector that directly writes data to Snowflake using Snowpipe Streaming. The connector should handle schema evolution and routing based on topic name. Define a clustering key on the Snowflake table on the 'customer id'
E) Develop a single Snowpipe Streaming client that consumes data from all Kafka topics, using a transformation function to route the data to the correct table based on the topic name. Use Snowflake's clustering key on 'customer _ id' for efficient querying.

5. You are tasked with creating a resilient data pipeline using Snowpark Python. The pipeline transforms data from a raw stage to a processed stage. A key transformation involves joining two DataFrames, 'dfl' and 'df2 , based on a common column, 'id'. You want to ensure that even if 'df2 is temporarily unavailable or contains unexpected data, the pipeline continues to process 'dfl' using a default value for missing data from 'df2. Which of the following approaches provides the best balance of resilience and data integrity? Assume you have defined a default dataframe 'df default' already.

A) Use a 'try-except' block to catch any exceptions during the join operation. If an exception occurs, use the 'fillna()' method to replace missing values with the default data value.
B) Perform a 'left_outer' join of 'dfl' with 'df2. If 'df2 is unavailable or returns no data, replace 'df2' with a default DataFrame Cdf_default) and proceed with the join.
C) Perform a 'left_outer' join of 'dfl' with 'df2. If the join fails, catch the exception and proceed without the join.
D) Use 'broadcast hint on 'df2 before performing the join to reduce the chances of join failure, assuming 'df2 is a small dataframe.
E) Write a custom Python UDF that attempts to retrieve the corresponding data from 'df2 based on the 'id' column. If the retrieval fails for a particular ID, return a default value.

Solutions: