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Snowflake SOL-C01 exam pdf dumps

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Snowflake Certified SnowPro Associate - Platform Certification Sample Questions (Q211-Q216):

NEW QUESTION # 211
A large e-commerce company is experiencing seasonal spikes in sales traffic, leading to significant increases in data ingestion and query processing. You are tasked with optimizing their Snowflake environment. Which of the following strategies BEST leverage Snowflake's elastic storage and compute capabilities to handle these fluctuating workloads efficiently and cost- effectively?

A. Pre-process all incoming data into smaller, aggregated tables before loading it into Snowflake to reduce the storage footprint.
B. Utilize Snowflake's auto-scaling feature for virtual warehouses to automatically adjust compute resources based on real-time demand, coupled with setting resource monitors to control spending.
C. Use only the X-Small virtual warehouse size, since Snowflake's cloud-based architecture distributes processing across nodes automatically without needing larger warehouses.
D. Rely solely on Snowflake's automatic storage scaling and ignore the virtual warehouse size, as storage is the primary bottleneck during peak seasons.
E. Manually resize the Snowflake virtual warehouse based on anticipated peak loads at the beginning of each season and reduce it back down after the peak ends.

Answer: B

Explanation:
Option B is the most effective strategy. Snowflake's auto-scaling feature automatically adjusts compute resources to match demand, ensuring optimal performance during peak periods.
Resource monitors prevent runaway costs. Option A is less efficient as it relies on manual intervention and might not react quickly enough to changing demands. Option C neglects the importance of compute scaling. Option D introduces unnecessary complexity and potential data loss. Option E will lead to poor performance during peak loads.

NEW QUESTION # 212
You are using PARSE DOCUMENT on documents stored in an external stage. You notice that some documents are skipped during processing without any explicit error messages. Which of the following could be the MOST likely reason(s) for this behavior, and how would you address it?

A. The documents are in an unsupported format. Ensure that the documents are in a supported format (PDF, DOCX, etc.) and that Snowflake has the necessary permissions to access the external stage. Check the 'VALIDATE function to verify if the data file is valid before parsing.
B. The virtual warehouse is being automatically suspended due to inactivity. Configure auto-resume for the virtual warehouse to ensure it remains active during processing. Also, explicitly specify the virtual warehouse to use by using USE WAREHOUSE
C. Snowflake encountered an internal error during processing and silently skipped the document.
Review the Snowflake query history for error messages or contact Snowflake support for assistance.
D. The documents are too large. Increase the virtual warehouse size and set the MAX FILE SIZE parameter in the file format to a higher value (e.g., 16MB), and retry the operation.
E. The documents do not contain any text. Verify that the documents actually contain readable text and are not just images or empty files. Remove the files without text

Answer: A,B

Explanation:
Option B is a common cause, where unsupported format and permissions are key things to consider and `VALIDATE' helps ensure data is valid. Option C is also correct, because virtual warehouse suspension can interrupt processing and specifying the warehouse ensures it's used.
Option A, while relevant to performance, doesn't explain silent skipping unless the files grossly exceed limits. Option D is possible but less likely than B or C and harder to diagnose without specific error messages. Option E is less likely, because even if the content is not parseable, it still doesn't explain why document is skipped silently.

NEW QUESTION # 213
You are loading data from a JSON file into a Snowflake table named 'ORDERS'. The JSON file contains nested arrays and objects. You need to extract specific fields from within these nested structures and load them into corresponding columns in the 'ORDERS' table. The 'ORDERS' table has columns: 'order_id' (VARCHAR), 'customer name' (VARCHAR), 'order_date' (DATE), and 'total_amount' (NUMBER). The JSON structure is as follows:

Which of the following COPY INTO statements correctly extracts the data from the JSON file and loads it into the `ORDERS table?

Answer: B

Explanation:
Option E is the correct solution. It uses a SELECT statement within the COPY INTO statement to extract the nested JSON fields. '$1' refers to the entire JSON document. The syntax extracts the value of the `orderld' field. The , and :NUMBER casts ensure that the data types are correctly converted to match the table schema. Options A does not extract any data it only load the data into the target column but as the target column require extracted data, so it will be incorrect .Option B lacks the necessary data type casting. Option C includes a separate SELECT statement, which is not part of the COPY INTO operation and therefore will not load the data.
Option D has an incorrect syntax for the 'FROM' clause within the 'COPY INTO' statement.

NEW QUESTION # 214
A data engineer is loading data into Snowflake from an external stage. They need to transform data during the load process. Which of the following is the MOST efficient and recommended approach for performing this transformation in Snowflake?

A. Use a COPY INTO statement with a SELECT statement that performs the transformations.
B. Use Snowpipe with a transformation script running on a separate compute instance.
C. Download the data from the stage, transform it using a Python script, and then upload it into Snowflake using the Snowflake connector.
D. Use a Stored Procedure to read the data from the stage, transform it, and then insert it into the target table.
E. Create a View that transforms the data after loading it into a staging table.

Answer: A

Explanation:
The COPY INTO statement with a SELECT statement provides the most efficient and recommended way to transform data during the load process. It leverages Snowflake's compute engine for transformations, avoiding the overhead of external scripts or stored procedures for simple transformations. Using views would transform the data on read, not during load.

NEW QUESTION # 215
A data engineer is experiencing inconsistent query performance during peak hours. They have a single X- Small warehouse currently configured. After analyzing query history, they observed significant queuing delays. Which of the following actions would MOST effectively address this performance issue while minimizing costs in the short term?

A. Enable multi-cluster warehouse with a minimum and maximum cluster count of 2. This will scale out the compute resources as needed.
B. Create a separate X-Small warehouse for each user to isolate their workloads.
C. Enable auto-suspend on the warehouse to reduce credits consumed during idle periods.
D. Upgrade the warehouse size to Large. This will provide more resources to handle concurrent queries.
E. Enable auto-resume on the warehouse to ensure it's always available.

Answer: A

Explanation:
Enabling a multi-cluster warehouse allows Snowflake to automatically scale out compute resources by adding additional clusters as needed to handle concurrent queries and reduce queuing. Upgrading the warehouse size (option A) is more expensive and might not be the most cost-effective initial solution. Auto-suspend/resume (options B & C) do not address concurrency issues. Creating separate warehouses per user (option E) adds administrative overhead and may not be efficient if users have varying workload demands.

NEW QUESTION # 216
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