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Snowflake SnowPro Advanced: Data Engineer (DEA-C02) Sample Questions (Q266-Q271):

NEW QUESTION # 266
A large e-commerce company stores clickstream data in an AWS S3 bucket. The data is partitioned by date and consists of Parquet files. They need to analyze this data in Snowflake without physically moving it into Snowflake's internal storage. However, the data frequently changes, and they need to ensure queries reflect the latest updates to the files without significant latency. Which of the following approaches would be MOST suitable, considering cost, performance, and data freshness?

A. Create a standard external table with the 'AUTO REFRESH' parameter set to 'TRUE'. This will automatically refresh the metadata whenever changes are detected in S3.
B. Create an external table using a Snowflake-managed catalog. Configure a Snowpipe to automatically refresh the metadata as new files are added to the S3 bucket.
C. Create an Iceberg table backed by the S3 bucket. Snowflake will automatically manage the metadata and handle incremental updates efficiently.
D. Create a standard external table directly on the S3 bucket. Refresh the external table metadata using SALTER EXTERNAL TABLE ... REFRESH' on a daily schedule.
E. Create a series of views on top of the S3 bucket using 'READ_PARQUET function, updating view definitions whenever underlying files change.

Answer: C

Explanation:
Iceberg tables are the most suitable option in this scenario. They address the limitations of standard external tables regarding metadata management and incremental updates. 'AUTO REFRESH' for external tables isn't ideal for frequent changes as it still relies on scanning the file system. A Snowflake-managed catalog with Snowpipe could work, but Iceberg is more purpose-built for handling the data evolution in place. READ_PARQUET in views is inefficient as it requires parsing the data every time the view is queried. Iceberg provides ACID properties and optimized query performance on data residing in external storage. The key benefit is the automatic metadata management provided by Snowflake for Iceberg tables.

NEW QUESTION # 267
You are tasked with implementing row-level security (RLS) on a 'SALES' table to restrict access based on the 'REGION' column. Users with the 'NORTH REGION ROLE should only see data where 'REGION = 'NORTH". You've created a row access policy named north_region_policy'. After applying the policy to the 'SALES table, users with the 'NORTH REGION ROLE are still seeing all rows.
Which of the following is the MOST likely reason for this and how can it be corrected?

A. The policy needs to be explicitly refreshed. Execute 'REFRESH ROW ACCESS POLICY north_region_policy ON SALES;'
B. The policy function within is not using the correct context function to determine the user's role. It should use 'CURRENT ROLE()' instead of 'CURRENT_USER()'
C. The user has not logged out and back in since the role was granted to them. Force the user to re-authenticate.
D. The is not enabled. Execute 'ALTER ROW ACCESS POLICY ON SALES SET ENABLED = TRUE;'
E. The ' does not have the USAGE privilege on the database and schema containing the 'SALES' table. Grant the USAGE privilege to the role.

Answer: E

Explanation:
Row access policies require the role to have USAGE privilege on the database and schema. Without this privilege, the policy cannot be enforced. The other options, while potentially relevant in other scenarios, are not the most likely cause for the described issue. Row access policies are automatically enabled when applied and the correct context function would be CURRENT_ROLE(). A refresh command is not required.

NEW QUESTION # 268
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. 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.
B. 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.
C. Leverage Snowflake's Snowpipe with a REST API endpoint to trigger data loads whenever new files are available in the Azure Blob Storage container.
D. 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.
E. 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.

Answer: C,E

Explanation:
Consolidating small files into larger files (option E) significantly improves COPY INTO performance by reducing overhead. Using Snowpipe with a REST API endpoint (option C) allows for efficient, triggered data loading. 'VALIDATION MODE (option A) is useful for data quality but doesn't directly address optimization for millions of small files. 'AUTO INGEST (option B) is specific to AWS S3 and Google Cloud Storage, not Azure Blob Storage event notifications. Column matching (option D) addresses schema flexibility, not optimization.

NEW QUESTION # 269
You have a Snowflake stage pointing to an external cloud storage location containing numerous Parquet files. A directory table is created on top of it. Over time, some files are deleted or moved from the external location. You notice discrepancies between the directory table's metadata and the actual files present in the storage location. Choose the option that best describes how Snowflake handles these discrepancies and the actions you should take.

A. Snowflake automatically updates the directory table in real-time, reflecting the changes immediately. No action is needed.
B. Snowflake does not track file deletions. If a file is deleted from cloud storage after being added to a directory table, Snowflake continues to reference the deleted file, potentially causing errors during data loading. Run 'VALIDATE on the directory table.
C. Snowflake automatically detects deleted files and marks them as 'invalid' in the directory table. Queries will automatically exclude these invalid files.
D. Snowflake requires you to drop and recreate the directory table periodically to synchronize the metadata with the external storage. Using 'ALTER DIRECTORY TABLE REFRESH' will not remove deleted files from the directory table's metadata. However, these invalid files wont be shown in select unless explicitly used.
E. Snowflake does not automatically detect these changes. You must manually refresh the directory table using 'ALTER DIRECTORY TABLE ... REFRESH' to synchronize the metadata. Snowflake does not provide an automated cleanup of metadata associated with removed files.

Answer: B

Explanation:
Snowflake's directory tables do not automatically reflect changes in external storage due to file deletions. The 'ALTER DIRECTORY TABLE ... REFRESH' command updates the metadata, but it doesn't automatically remove entries for deleted files. Attempting to load data from a deleted file will likely result in errors. Running 'VALIDATE on the directory table will identify the files that no longer exist in the external stage. Options A, B, C, and D are incorrect because they misrepresent how Snowflake manages changes in the underlying storage.

NEW QUESTION # 270
You have an external table named in Snowflake that points to a set of CSV files in an AWS S3 bucket. The CSV files have a header row, and the data is comma-separated. However, some of the files in the S3 bucket are gzipped. You need to define the external table to correctly read both compressed and uncompressed files. Which of the following SQL statements BEST achieves this?

A. Option E
B. Option B
C. Option A
D. Option C
E. Option D

Answer: B

Explanation:
The 'COMPRESSION = AUTO' parameter in the file format definition allows Snowflake to automatically detect and decompress gzipped files while also reading uncompressed files. The 'SKIP HEADER = 1 ' parameter ensures that the header row in the CSV files is skipped.

NEW QUESTION # 271
......

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