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Snowflake COF-C02 (SnowPro Core Certification) Exam covers a wide range of topics, including Snowflake architecture, data loading and unloading techniques, security and access control, and query optimization. COF-C02 exam is divided into multiple sections, each of which focuses on a different aspect of the Snowflake platform. COF-C02 Exam comprises of 70 multiple-choice questions that need to be answered within 90 minutes. COF-C02 exam is available in multiple languages, including English, Japanese, and German.
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The SnowPro Core Certification exam is a computer-based exam that consists of 90 multiple-choice questions. COF-C02 exam is timed and lasts for 2 hours. Candidates must achieve a score of at least 80% to pass the exam and earn their certification. COF-C02 exam is available in multiple languages and can be taken online from anywhere in the world.
Snowflake COF-C02 (SnowPro Core Certification) Certification Exam is a comprehensive assessment of an individual's knowledge and skills in using the Snowflake data platform. SnowPro Core Certification Exam certification exam is designed to validate the candidate's ability to work with Snowflake's data warehouse and cloud data platform, including data loading, querying, and administration tasks. COF-C02 Exam Tests the knowledge of the candidate in the areas of data warehousing, data modeling, data loading, querying, and administration of Snowflake. Snowflake SnowPro Core Certification Exam Sample Questions (Q203-Q208):NEW QUESTION # 203
Which of the following describes how multiple Snowflake accounts in a single organization relate to various cloud providers?
A. Each Snowflake account can be hosted in a different cloud vendor, but must be in the same region.
B. Each Snowflake account can be hosted in a different cloud vendor and region.
C. All Snowflake accounts must be hosted in the same cloud vendor and region
D. Each Snowflake account must be hosted in a different cloud vendor and region
Answer: B
Explanation:
Snowflake's architecture allows for flexibility in account hosting across different cloud vendors and regions.
This means that within a single organization, different Snowflake accounts can be set up in various cloud environments, such as AWS, Azure, or GCP, and in different geographical regions. This allows organizations to leverage the global infrastructure of multiple cloud providers and optimize their data storage and computing needs based on regional requirements, data sovereignty laws, and other considerations. https://docs.snowflake.com/en/user-guide/intro-regions.html
NEW QUESTION # 204
What action should be taken if a large number of concurrent queries are queued in a virtual warehouse?
A. Scale-up by resizing the warehouse.
B. Enable auto-resume on the warehouse.
C. Disable auto-suspend on the warehouse.
D. Scale-out with a multi-cluster warehouse.
Answer: D
Explanation:
When a large number of concurrent queries are queued in a virtual warehouse in Snowflake, the most effective action is to scale out using a multi-cluster warehouse. Here's why:
* Understanding the Problem:
* When many queries are queued, it indicates that the computational resources of the warehouse are insufficient to handle the demand.
* Each virtual warehouse in Snowflake provides a fixed amount of compute resources. A single cluster in the warehouse can only process a finite number of concurrent queries efficiently.
* Scale-Out with Multi-Cluster Warehouse:
* Snowflake's multi-cluster warehouse feature allows a warehouse to dynamically add or remove clusters based on the current workload.
* With scale-out, additional clusters are automatically spun up when the query load increases. This helps distribute the workload and reduces queuing without impacting ongoing query performance.
* Once the load decreases, the additional clusters are automatically suspended, optimizing costs.
* Why Not Other Options?:
* A. Scale-up by resizing the warehouse: While scaling up (choosing a larger size, e.g., from X- Small to Medium) increases compute power, it may not efficiently resolve queuing when many concurrent queries need to be processed. It's better suited for improving the performance of individual queries rather than handling more queries simultaneously.
* C. Disable auto-suspend on the warehouse: This ensures the warehouse remains active but does not address the queuing issue. Keeping a warehouse perpetually active can lead to unnecessary costs.
* D. Enable auto-resume on the warehouse: This ensures the warehouse activates when needed but does not directly impact query concurrency.
* Best Practices for Scaling with Multi-Cluster Warehouses:
* Configure the minimum and maximum cluster settings to allow Snowflake to handle varying workloads efficiently.
* Use auto-scaling policies like "economy" or "standard" to control cluster behavior based on cost or performance requirements.
* Steps to Implement Multi-Cluster Warehouse:
* Navigate to the Snowflake console.
* Go to the "Warehouses" tab and select the target warehouse.
* Edit the warehouse settings to set:
* Minimum Clusters: The minimum number of active clusters.
* Maximum Clusters: The upper limit of clusters that Snowflake can scale out to.
* Auto-Resume and Auto-Suspend: Ensure these are enabled for cost efficiency.
References:
* Snowflake Documentation: Scaling a Multi-Cluster Warehouse
* Snowflake Documentation: Query Processing and Queuing
* Snowflake Best Practices for Warehouses
NEW QUESTION # 205
When working with table MY_TABLE that contains 10 rows, which sampling query will always return exactly 5 rows?
A. SELECT * FROM MY_TABLE SAMPLE BERNOULLI (5);
B. SELECT * FROM MY_TABLE SAMPLE (5 ROWS);
C. SELECT * FROM MY_TABLE SAMPLE SYSTEM (5);
D. SELECT * FROM MY_TABLE SAMPLE SYSTEM (1) SEED (5);
Answer: D
Explanation:
In Snowflake,SAMPLE (5 ROWS)ensures an exact count of 5 rows is returned from MY_TABLE, regardless of table size. This is different from SAMPLE SYSTEM or SAMPLE BERNOULLI, which use percentage- based sampling, potentially returning varying row counts based on probabilistic methods.
The ROWS option is deterministic and does not depend on percentage, making it ideal when an exact row count is required.
NEW QUESTION # 206
When cloning a database containing stored procedures and regular views, that have fully qualified table references, which of the following will occur?
A. The cloned views and the stored procedures will reference the cloned tables in the cloned database.
B. An error will occur, as stored objects cannot be cloned.
C. The stored procedures and views will refer to tables in the source database.
D. An error will occur, as views with qualified references cannot be cloned.
Answer: A
Explanation:
When cloning a database containing stored procedures and regular views with fully qualified table references, the cloned views and stored procedures will reference the cloned tables in the cloned database (A). This ensures that the cloned database is a self-contained copy of the original, with all references pointing to objects within the same cloned database. Reference: SnowPro Core Certification cloning database stored procedures views
NEW QUESTION # 207
What is the purpose of the use of the VALIDATE command?
A. To see all errors from a previously run COPY INTO <table> statement
B. To view any queries that encountered an error
C. To prevent a put statement from running if an error occurs
D. To verify that a SELECT query will run without error
Answer: A
Explanation:
The VALIDATE command in Snowflake is used to check for errors that occurred during the execution of a COPY INTO <table> statement. This command helps users identify and resolve data loading issues.
Run the COPY INTO Statement: Execute the COPY INTO <table> command to load data from a stage into a table.
COPY INTO my_table
FROM @my_stage
FILE_FORMAT = (FORMAT_NAME = 'my_format');
Validate the Load: Use the VALIDATE function to see if there were any errors during the data load.
SELECT *
FROM TABLE(VALIDATE(my_table, JOB_ID => 'my_copy_job_id'));
Review Errors: The VALIDATE function will return details about any errors that occurred, such as parsing errors or data type mismatches.
Reference:
Snowflake Documentation: Validating Data Loads
Snowflake Documentation: COPY INTO <table>