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| Certification Vendor: | Snowflake |
|---|---|
| Exam Name: | Snowflake Certified SnowPro Associate - Platform Certification |
| Exam Number: | SOL-C01 |
| Real Exam Qty: | 65 |
| Exam Format: | Multiple Select, Interactive, Multiple Choice |
| Available Languages: | English, Japanese, Chinese |
| Related Certifications: | SnowPro Core Certification |
| Exam Price: | $100 USD |
| Passing Score: | 750 |
| Certificate Validity Period: | 2 years |
| Exam Duration: | 85 minutes |
| Sample Questions: | Snowflake SOL-C01 Sample Questions |
| Exam Way: | Unproctored online exam delivered through Pearson VUE. Once purchased, you have 24 hours to launch and complete the exam. Must be completed within 85 minutes from starting with no exceptions. |
| Pre Condition: | No prerequisites. Target audience: individuals with 3+ months of knowledge using Snowflake. |
| Official Syllabus URL: | https://learn.snowflake.com/en/pages/snowpro-platform-announcementFAQs |
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NEW QUESTION # 21
A Snowflake architect is designing a data lake solution using Snowflake's external tables. The data lake contains JSON files stored in AWS S3. The architect needs to ensure that Snowflake can automatically discover new JSON files added to the S3 bucket and make them available for querying through the external table. Which of the following approaches provides the MOST efficient and scalable solution for automatically refreshing the external table metadata?
Schedule a daily task in Snowflake to execute 'ALTER EXTERNAL TABLE REFRESH' to manually update the metadata.
Answer: B
Explanation:
Using Event Tables (Option C) is the most efficient and scalable solution. Snowflake Event Tables are designed to capture and process events directly within Snowflake, making them ideal for this scenario. The stream and task can automatically trigger the refresh whenever a new S3 event is detected. Option A (scheduled task) is less efficient. Option B (Lambda function) adds external complexity. Option D (Snowpipe) loads data into Snowflake, not updating external table metadata. Option E (automatic data clustering) does not automatically update the metadata of external tables when new files are added.
NEW QUESTION # 22
A data science team needs to access data from several databases and schemas for model training. To simplify access management, you create a custom role, `DATA SCIENCE ROLE.
You need to grant this role the ability to SELECT from all tables in any schema across the following databases: `DB ANALYTICS, 'DB REPORTING'. Which of the following is the MOST efficient approach, minimizing the number of GRANT statements while adhering to least privilege?
Answer: C
Explanation:
Option C is the most efficient. Granting USAGE on the databases is necessary. Granting USAGE on ALL SCHEMAS allows access to existing schemas. 'GRANT SELECT ON FUTURE TABLES IN SCHEMA' is more efficient than granting SELECT on all existing tables and ensures that the role automatically has access to any newly created tables within those schemas.Option A Grants select on ALL tables, but doesn't include future tables. Option B Grants select on ALL tables, but doesn't include future tables and also does not grant USAGE on the SCHEMAS directly. option D Grants select on FUTURE TABLES for the database directly, which is not possible.
NEW QUESTION # 23
A user executes the following SQL statement in Snowflake:
SELECT FROM MY TABLE;
The user reports that the query is running very slowly. Assuming the user has the necessary permissions to access the table, which of the following could be the cause of the slow query performance and how could you verify that the root cause is the bottleneck?
Answer: B,C,E
Explanation:
A virtual warehouse that is undersized will lead to queries that spill to local or remote disks. Table being not clustered properly. Table being too wide and inefficient. Option D is highly unlikely as you can see how the query processes internally in query profile and it can be ruled out quickly.
Check the query profile in Snowsight to observe spill to local disk or remote storage. Run function to check the clustering depth and verify how much the table is clustered. The query profile also shows that the 'SELECT operation is taking a significant amount of time.
NEW QUESTION # 24
What Snowflake parameter is configured in the Query Processing layer?
Answer: B
Explanation:
The Query Processing layer of Snowflake is wherevirtual warehouses operate, so warehouse sizing parameters (X-Small to 6X-Large) fall under this layer. Warehouse size determines compute power, concurrency, and performance behavior for SQL workloads. Administrators configure warehouse size based on workload intensity, response time requirements, and cost considerations.
Serverless compute limits and micro-partition limits belong to storage and services layers. Table types (permanent, transient, temporary) are storage-level configurations, not part of Query Processing.
Thus, warehouse sizing is the correct parameter configured at the Query Processing layer.
NEW QUESTION # 25
A data warehouse contains a table 'orders' with columns 'order_id', 'customer_id', 'order_date' , and 'order_details' (VARIANT type containing nested JSON). You need to create a new table
'customer_orders' that aggregates the total order value per customer for orders placed in the year
2023. The order value is calculated as the sum of the 'price' field for each item in the 'items' array within the 'order_detailS JSON. Which of the following SQL statements achieves this with optimal performance, assuming a large dataset and leveraging best practices for querying VARIANT data?
Answer: A
Explanation:
Option D provides the most optimized and correct solution. Using 'CROSS APPLY FLATTEN' (or
`LATERAL FLATTEN' which is equivalent) efficiently un-nests the `items' array. Filtering by =
2023' before the 'FLATTEN' operation can significantly improve performance by reducing the number of rows processed by the FLATTEN' function. Option A is syntactically correct, however CROSS APPLY is preferrable to use for readability. Option B is incorrect. It attempts to use array slicing syntax which is not valid for summing the prices of all items within the array. Option C is incorrect. The is unnecessary as the 'item' is already a JSON object. The correct syntax is
'item.value' when using Option E is invalid syntax. The FLATTEN function is not valid inside SUM.
NEW QUESTION # 26
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