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Snowflake Certified SnowPro Associate - Platform Certification Sample Questions (Q97-Q102):

NEW QUESTION # 97
You are designing a data pipeline in Snowflake that involves frequent updates to a staging table
`STG CUSTOMERS before merging the data into a production table `PROD CUSTOMERS. The
'DATA RETENTION_TIME parameter is set to 7 days at the account level. During a particular data load, a bug in the pipeline causes incorrect data to be loaded into `STG CUSTOMERS. You need to revert 'STG CUSTOMERS' to its state before the erroneous load. However, you also need to investigate the cause of the bug using the incorrect data in the current version of 'STG CUSTOMERS. What steps can you take to achieve both data recovery and root cause analysis effectively?

A. Create a clone of 'STG_CUSTOMERS' before reverting it to its previous state using Time Travel.
Analyze the cloned table to identify the bug.
B. Set the 'DATA RETENTION_TIME IN DAYS parameter to 0 on 'STG_CUSTOMERS and perform a full table refresh from the source system.
C. Revert 'STG CUSTOMERS to its previous state using Time Travel. The incorrect data is unrecoverable as Time Travel replaces the current data. The bug will have to be found later.
D. Immediately drop and recreate the 'STG CUSTOMERS' table from a backup. Investigate the bug later by examining code logs.

Answer: A

Explanation:
Creating a clone of 'STG_CUSTOMERS' before reverting it allows you to preserve the incorrect data for analysis while restoring the original table to its correct state using Time Travel. This allows for both data recovery and root cause analysis. Option A doesn't preserve the incorrect data for analysis. Option C loses the incorrect data, hindering debugging. Option D disables Time Travel and relies on a full refresh, which might be slow and complex.

NEW QUESTION # 98
You are working with a Snowflake Notebook to process data from an external stage (AWS S3).
You need to access the S3 stage using a named stage object and a storage integration configured with IAM roles. Which of the following options represents the correct sequence of steps and Snowflake SQL commands within the notebook to achieve this?

A. 1. Create the storage integration with appropriate IAM roles. 2. Create the external stage referencing the storage integration. 3. Create a file format object. 4. Use `COPY INTO command to load data from the stage into a Snowflake table, specifying the stage name and the file format.
B. 1. Create the storage integration with appropriate IAM roles. 2. Create the external stage referencing the storage integration. 3. Use 'COPY INTO' command to load data from the stage into a Snowflake table, specifying the stage name.
C. 1. Create the storage integration with appropriate IAM roles. 2. Use the 'LIST @stage' command to verify the stage connectivity and file listing. 3. Create an external table pointing to the external stage. 4. Use the 'REFRESH EXTERNAL TABLE command to load the metadata. 5. Query data directly from the external table.
D. 1. Create the external stage specifying the S3 bucket URL and credentials. 2. Create a file format object. 3. Use `COPY INTO' command to load data from the stage into a Snowflake table, specifying the stage name and the file format.
E. 1. Create the external stage specifying the S3 bucket URL and credentials. 2. Create a file format object. 3. Use 'SELECT FROM @stage/file.csv' to query the data directly from the stage.

Answer: A,B

Explanation:
Options A and D are correct. The correct and secure approach involves using storage integrations with IAM roles. Creating the storage integration first establishes trust between Snowflake and AWS. The stage then references this integration. The 'COPY INTO' command (Option A) is used for loading data into Snowflake tables. External tables (option C) are read-only and designed for querying data in place, not loading it. Option D Corrects A by including the file format, which is generally required. Option B does not use storage integrations, which is more secure . Option E Requires credential in stage definition, which is not encouraged.

NEW QUESTION # 99
You are tasked with loading data from a series of CSV files stored in an Amazon S3 bucket into Snowflake. The CSV files contain a header row, but some files have slight variations in the number and order of columns. You want to ensure that all relevant data is loaded correctly, even if the column order differs, and that any extra columns are ignored. Which of the following approaches is the MOST appropriate and efficient?

A. Create a separate external table for each CSV file with a different column structure.
B. Create a VIEW on top of the external table to ensure that column names are consistent across all files.
Then load the data into view.
C. Pre-process the CSV files to standardize the column order and names before loading them into Snowflake.
D. Define a single external table with a VARIANT column and use Snowflake's CSV parsing capabilities to load all files into that column. Then, extract the relevant data using JSON path expressions.
E. Create a single target table with all possible columns from all CSV files, using 'SKIP_HEADER = 1' and explicitly map the columns in the 'COPY INTO' statement to the correct columns in the target table, using the 'FILE FORMAT option to specify the correct field delimiter.

Answer: E

Explanation:
Creating a single target table with all possible columns and explicitly mapping the columns in the
'COPY INTO' statement is the most appropriate. This approach handles variations in column order by explicitly mapping columns from the CSV files to the target table. It is more performant than VARIANT, and doesn't require external preprocessing. Option A is not scalable and difficult to maintain. Option B is suitable for schema evolution but is not recommended if schemas are already known. Option D loading data into view, is not direct approach, and requires external table and COPY command need a table not view to load. Option E pre-processing helps if data consistency is high priority, but adds complexity to workflow and is not part of Snowflake functionalities.

NEW QUESTION # 100
You are tasked with deploying a new data application to Snowflake. This application requires several schemas for staging, transformation, and reporting. What is the recommended approach to create these schemas using Infrastructure as Code (laC) and ensuring consistency across multiple Snowflake environments (DEV, TEST, PROD)?

A. Copy the DDL (Data Definition Language) scripts generated by the Snowflake UI after manually creating schemas in one environment and executing them in others.
B. Utilize a third-party lac tool (e.g., Terraform) to define the schemas as resources, managing their creation and updates across environments.
C. Utilize Snowflake's data replication feature to replicate schemas between different environments
D. Use a Snowflake client (e.g., SnowSQL) and a script to execute 'CREATE SCHEMA' statements, parameterizing the environment-specific database and schema names.
E. Manually create the schemas in each environment using the Snowflake web UI, documenting the steps for repeatability.

Answer: B

Explanation:
Using a dedicated lac tool like Terraform is the best practice for managing Snowflake resources, including schemas. It ensures consistency, repeatability, and version control. Options A and D are manual and error-prone. Option B is better than A and D but lacks the full features of lac tools (state management, dependency resolution, etc.). Option E is for data replication and not schema creation.

NEW QUESTION # 101
A data engineering team is tasked with loading a large dataset (5TB) into Snowflake from an external S3 bucket. The data loading process is experiencing significant performance bottlenecks. Which of the following strategies would MOST effectively improve the data loading performance, assuming the network bandwidth between Snowflake and S3 is sufficient?

A. Partition the data in S3 into smaller files and ensure the virtual warehouse is appropriately sized for concurrent processing of these files.
B. Use a larger virtual warehouse indefinitely to handle any potential performance peaks, even after the initial data load.
C. Disable auto-suspend on the virtual warehouse to prevent it from idling during the data load.
D. Use multiple virtual warehouses concurrently to load different subsets of the data from S3.
E. Increase the size of the virtual warehouse to a larger size (e.g., from SMALL to LARGE) before loading the data, and then resize it back down after the load is complete.

Answer: A,E

Explanation:
Options A and E are the most effective. Increasing warehouse size provides more compute resources for parallel loading. Partitioning the data into smaller files in S3 allows Snowflake to parallelize the load process across multiple compute nodes within the warehouse. Option B might seem useful but Snowflake inherently parallelizes loading from S3 with a single warehouse if the data is properly partitioned. Option C is not cost-effective. Option D might help avoid some overhead but is less impactful than warehouse sizing and data partitioning.

NEW QUESTION # 102
......

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