COF-C02 by Snowflake Actual Free Exam Questions And Answers [UPDATED 2025]
COF-C02 Questions Truly Valid For Your Snowflake Exam!
Snowflake COF-C02 (SnowPro Core Certification) Exam is an industry-recognized certification exam designed to validate the skills and knowledge of professionals working with Snowflake technology. COF-C02 exam is designed to test a candidate’s proficiency in using Snowflake, a cloud-based data warehousing platform, to store, process, and analyze data. SnowPro Core Certification Exam certification exam is administered by Snowflake, a leading cloud data platform provider that offers solutions for data warehousing, data lakes, and data engineering.
NEW QUESTION # 90
What are the default Time Travel and Fail-safe retention periods for transient tables?
- A. Time Travel - 1 day. Fail-safe - 0 days
- B. Transient tables are retained in neither Fail-safe nor Time Travel
- C. Time Travel - 0 days. Fail-safe - 1 day
- D. Time Travel - 1 day. Fail-safe - 1 day
Answer: A
Explanation:
Explanation
https://docs.snowflake.com/en/user-guide/data-cdp-storage-costs.html#:~:text=Temporary%20and%20Transient
NEW QUESTION # 91
Which formats are supported for unloading data from Snowflake? Choose 2 answers
- A. ORC
- B. JSON
- C. Delimited (CSV, TSV, etc.)
- D. Avro
Answer: B,C
NEW QUESTION # 92
Based on a review of a Query Profile, which scenarios will benefit the MOST from the use of a data clustering key? (Select TWO.)
- A. A column that appears most frequently in GROUP BY operations
- B. A column that appears most frequently in r.im z operations
- C. A column that appears most frequently in aggregate operations
- D. A column that appears most frequently in ORDER BY operations
- E. A column that appears most frequently in join operations
Answer: B,D
Explanation:
* Data Clustering Keys help Snowflake optimize performance by physically organizing data in micro- partitions based on specified columns.
* Columns frequently used in ORDER BY or WHERE clauses benefit the most because clustering improves pruning, reducing the number of micro-partitions scanned.
Why Other Options Are Incorrect:
* C. GROUP BY: Aggregate operations benefit less from clustering.
* D. Aggregate operations: Clustering has limited effect here unless combined with filtering in WHERE.
* E. JOIN operations: Joins rely more on table structure than clustering.
References:
* Clustering Keys Documentation
NEW QUESTION # 93
What is the MAXIMUM size limit for a record of a VARIANT data type?
- A. 16MB
- B. 32MB
- C. 8MB
- D. 128MB
Answer: A
NEW QUESTION # 94
Which option will replace NULL values with an empty string while unloading data into a file?
- A. FIELD_OPTIONALLY_ENCLOSED_BY = NONE
- B. null_if= ('')
- C. null_if=('NULL')
- D. EMPTY_FIELD_AS_NULL = TRUE
Answer: B
NEW QUESTION # 95
A query containing a WHERE clause is running longer than expected. The Query Profile shows that all micro- partitions being scanned How should this query be optimized?
- A. Add a limit clause to the query.
- B. Add a clustering key to the table
- C. Add a Dynamic Data Masking policy to the table.
- D. Create a view on the table.
Answer: B
Explanation:
When a query containing a WHERE clause is running longer than expected, and the Query Profile shows that all micro-partitions are being scanned, the query can be optimized by adding a clustering key to the table.
* Understanding Micro-Partitioning in Snowflake:
* Snowflake automatically partitions tables into micro-partitions for efficient storage and query performance.
* Each micro-partition contains metadata about the range of values it holds, which helps in pruning irrelevant partitions during query execution.
* Role of Clustering Keys:
* A clustering key defines how data in a table is organized within micro-partitions.
* By specifying a clustering key, you can control the physical layout of data, ensuring that related rows are stored together.
* This organization improves query performance by reducing the number of micro-partitions that need to be scanned.
* Optimizing Queries with Clustering Keys:
* Adding a clustering key based on columns frequently used in WHERE clauses helps Snowflake quickly locate and scan relevant micro-partitions.
* This minimizes the amount of data scanned and reduces query execution time.
* Example:
ALTER TABLE my_table CLUSTER BY (column1, column2);
* This command adds a clustering key to my_table using column1 and column2.
* Future queries that filter on these columns will benefit from improved performance.
* Benefits:
* Reduced query execution time: Fewer micro-partitions need to be scanned.
* Improved resource utilization: More efficient data retrieval leads to lower compute costs.
References:
* Snowflake Documentation: Clustering Keys
* Snowflake Documentation: Query Profile
NEW QUESTION # 96
What does Snowflake recommend when planning virtual warehouse usage for a data load?
- A. Dedicate a separate warehouse for loading data.
- B. Increase the size of the warehouse used.
- C. Use several single-duster warehouses.
- D. Load the fewest possible number of large files.
Answer: A
NEW QUESTION # 97
What is the LEAST-PRIVILEGED role that can create a share in Snowflake by default?
- A. USERADMIN
- B. SECURITYADMIN
- C. SYSADMIN
- D. ACCOUNTADMIN
Answer: B
NEW QUESTION # 98
If a Snowflake user decides a table should be clustered, what should be used as the cluster key?
- A. The columns that are queried in the select clause.
- B. The columns with very high cardinality.
- C. The columns most actively used in the select filters.
- D. The columns with many different values.
Answer: C
Explanation:
When deciding on a clustering key for a table, Snowflake recommends using the columns that are most actively used in the select filters. This is because clustering by these columns can improve the performance of queries that filter on these values, leading to more efficient scans and better overall query performance2.
References: [COF-C02] SnowPro Core Certification Exam Study Guide
NEW QUESTION # 99
Which data formats are supported by Snowflake when unloading semi-structured data? (Select TWO).
- A. Newline Delimited JSON
- B. Plain text file containing XML elements
- C. Binary file in Avro
- D. Comma-separated JSON
- E. Binary file in Parquet
Answer: A,B
NEW QUESTION # 100
What is the MINIMUM size requirement when creating a Snowpark-optimized virtual warehouse?
- A. Small
- B. X-Small
- C. Large
- D. Medium
Answer: A
Explanation:
When creating a Snowpark-optimized virtual warehouse in Snowflake, the minimum size requirement is Small. Snowpark is designed to handle data processing workloads efficiently, and the Small size ensures adequate resources for such tasks.
Virtual Warehouse Sizes:
Snowflake offers different sizes for virtual warehouses, ranging from X-Small to 6X-Large.
Each size corresponds to a specific level of compute resources.
Minimum Size Requirement for Snowpark:
A Small virtual warehouse is the minimum size required to optimize performance and resource allocation for Snowpark workloads.
This ensures that the warehouse has sufficient capacity to handle data processing and transformation tasks efficiently.
Reference:
Snowflake Documentation: Virtual Warehouse Sizes
NEW QUESTION # 101
Which of the following is the Snowflake Account_Usage.Metering_History view used for?
- A. Gathering the hourly credit usage for an account
- B. Compiling an account's average cloud services cost over the previous month
- C. Summarizing the throughput of Snowpipe costs for an account
- D. Calculating the funds left on an account's contract
Answer: A
Explanation:
The Snowflake Account_Usage.Metering_History view is used to gather the hourly credit usage for an account. This view provides details on the credits consumed by various services within Snowflake for the last
365 days1.
NEW QUESTION # 102
Which semi-structured file format is a compressed, efficient, columnar data representation?
- A. TSV
- B. Parquet
- C. JSON
- D. Avro
Answer: B
Explanation:
Parquet is a columnar storage file format that is optimized for efficiency in both storage and processing. It supports compression and encoding schemes that significantly reduce the storage space needed and speed up data retrieval operations, making it ideal for handling large volumes of data. Unlike JSON or TSV, which are row-oriented and typically uncompressed, Parquet is designed specifically for use with big data frameworks, offering advantages in terms of performance and cost when storing and querying semi-structured data.References: Apache Parquet Documentation
NEW QUESTION # 103
Authorization to execute CREATE <object> statements comes only from which role?
- A. Database role
- B. Application role
- C. Secondary role
- D. Primary role
Answer: D
Explanation:
In Snowflake, the authorization to execute CREATE <object> statements, such as creating tables, views, databases, etc., is determined by the role currently set as the user's primary role. The primary role of a user or session specifies the set of privileges (including creation privileges) that the user has. While users can have multiple roles, only the primary role is used to determine what objects the user can create unless explicitly specified in the session.
NEW QUESTION # 104
What are best practice recommendations for using the ACCOUNTADMIN system-defined role in Snowflake? (Choose two.)
- A. Ensure all ACCOUNTADMIN roles use Multi-factor Authentication (MFA).
- B. The ACCOUNTADMIN role must be granted to only one user.
- C. All users granted ACCOUNTADMIN role must be owned by the ACCOUNTADMIN role.
- D. All users granted ACCOUNTADMIN role must also be granted SECURITYADMIN role.
- E. Assign the ACCOUNTADMIN role to at least two users, but as few as possible.
Answer: A,E
NEW QUESTION # 105
The fail-safe retention period is how many days?
- A. 7 days
- B. 45 days
- C. 1 day
- D. 90 days
Answer: A
Explanation:
Reference: https://docs.snowflake.com/en/user-guide/data-failsafe.html
NEW QUESTION # 106
Use of which feature or setting consumes Snowflake credits, but generates no storage costs?
- A. Query acceleration service
- B. Materialized view
- C. Search optimization service
- D. Table clustering
Answer: C
NEW QUESTION # 107
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