File formats (CSV, JSON, Parquet, Avro) in Snowflake - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ service_behavior

intermediate

Understanding CSV File Loading Behavior in Snowflake

You load a CSV file into a Snowflake table using the COPY INTO command with default options. The CSV file contains some rows with missing values at the end of the line.

What will happen to those missing values during the load?

Snowflake

COPY INTO my_table FROM @my_stage/file.csv FILE_FORMAT = (TYPE = 'CSV');

A Missing trailing columns are ignored and the row is truncated to available columns.

B The load fails with an error due to missing columns in some rows.

C Missing trailing columns are loaded as NULL values in the table columns.

D Snowflake fills missing columns with empty strings instead of NULL.

💡 Hint

Think about how Snowflake handles incomplete rows in CSV files by default.

Snowflake treats missing trailing columns in CSV files as NULL values by default when loading data. This allows partial rows to be loaded without errors.

❓ Architecture

intermediate

Choosing the Best File Format for Semi-Structured Data in Snowflake

You need to store and query semi-structured JSON data efficiently in Snowflake. Which file format should you choose to optimize query performance and storage?

A Parquet

B JSON

C CSV

D Avro

💡 Hint

Consider which format supports columnar storage and efficient compression.

Parquet is a columnar storage format that compresses data efficiently and improves query performance, especially for semi-structured data like JSON.

❓ security

advanced

Handling Schema Evolution with Avro Files in Snowflake

You have Avro files with evolving schemas being loaded into a Snowflake table. What is the best practice to handle schema changes without causing load failures?

A Convert Avro files to CSV before loading to avoid schema issues.

B Use a fixed schema in Snowflake and reject files with schema differences.

C Manually update the Snowflake table schema before loading new Avro files.

D Use Snowflake's VARIANT column type to store Avro data and parse schema dynamically.

💡 Hint

Think about how Snowflake handles semi-structured data and schema flexibility.

Using VARIANT columns allows Snowflake to store semi-structured data like Avro with evolving schemas without requiring strict schema enforcement, avoiding load failures.

🧠 Conceptual

advanced

Comparing Compression and Query Efficiency of File Formats in Snowflake

Which file format among CSV, JSON, Parquet, and Avro generally provides the best compression and fastest query performance in Snowflake?

A CSV

B Parquet

C Avro

D JSON

💡 Hint

Consider columnar vs row-based formats and compression capabilities.

Parquet is a columnar format that compresses data well and enables faster queries by reading only needed columns, outperforming row-based formats like CSV, JSON, and Avro.

✅ Best Practice

expert

Optimizing Snowflake Data Loading with Mixed File Formats

You have a Snowflake pipeline that loads data from multiple file formats: CSV, JSON, Parquet, and Avro. To optimize loading speed and minimize errors, which approach is best?

A Create separate tables optimized for each file format and load accordingly.

B Load all files into one table with columns matching the CSV schema.

C Load all files into a single VARIANT column table regardless of format.

D Convert all files to CSV before loading to simplify the process.

💡 Hint

Think about how different file formats have different strengths and schema requirements.

Creating separate tables optimized for each file format allows Snowflake to leverage format-specific features and avoid errors, improving load speed and data quality.