COF-C02 Certified Questions - New COF-C02 Study Notes
COF-C02 Certified Questions - New COF-C02 Study Notes
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The SnowPro Core Certification Exam is comprised of 60 multiple-choice questions that must be completed within a 90-minute time frame. COF-C02 Exam covers a wide range of topics related to Snowflake, including data loading and unloading, SQL and Snowflake Functions, Snowflake architecture, performance optimization, and troubleshooting. COF-C02 exam is designed to test the candidate’s understanding of Snowflake and their ability to apply that knowledge in real-world scenarios.
To prepare for the SnowPro Core Certification exam, candidates should have a good understanding of Snowflake technology and its capabilities. Candidates can enroll in training courses and workshops offered by Snowflake to gain a better understanding of the technology. Additionally, candidates can also take practice exams to get a feel for the type of questions that will be asked in the actual exam.
The SnowPro Core Certification exam is ideal for data analysts, administrators, and engineers who want to demonstrate their expertise in Snowflake. By obtaining this certification, professionals can verify their understanding of Snowflake's platform capabilities and learn advanced tools to optimize Snowflake data workloads.
>> COF-C02 Certified Questions <<
New COF-C02 Study Notes - COF-C02 Simulation Questions
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Snowflake SnowPro Core Certification Exam Sample Questions (Q68-Q73):
NEW QUESTION # 68
What is the difference between a stored procedure and a User-Defined Function (UDF)?
- A. Multiple stored procedures can be called as part of a single executable statement while a single SQL statement can only call one UDF at a time.
- B. Stored procedures can execute database operations while UDFs cannot.
- C. Values returned by a stored procedure can be used directly in a SQL statement while the values returned by a UDF cannot.
- D. Returning a value is required in a stored procedure while returning values in a UDF is optional.
Answer: B
NEW QUESTION # 69
What is the recommended file sizing for data loading using Snowpipe?
- A. A compressed file size greater than 1 GB, and up to 2 GB
- B. A compressed file size greater than 10 MB, and up to 100 MB
- C. A compressed file size greater than 100 GB, and up to 250 GB
- D. A compressed file size greater than 100 MB, and up to 250 MB
Answer: B
Explanation:
For data loading using Snowpipe, the recommended file size is a compressed file greater than 10 MB and up to
100 MB. This size range is optimal for Snowpipe's continuous, micro-batch loading process, allowing for efficient and timely data ingestion without overwhelming the system with files that are too large or too small.
References:
* [COF-C02] SnowPro Core Certification Exam Study Guide
* Snowflake Documentation on Snowpipe1
NEW QUESTION # 70
Which command is used to upload data files from a local directory or folder on a client machine to an internal stage, for a specified table?
- A. GET
- B. COPY INTO <location>
- C. CREATE STREAM
- D. PUT
Answer: D
Explanation:
To upload data files from a local directory or folder on a client machine to an internal stage in Snowflake, the PUT command is used. The PUT command takes files from the local file system and uploads them to an internal Snowflake stage (or a specified stage) for the purpose of preparing the data to be loaded into Snowflake tables.
Syntax Example:
PUT file://<local_file_path> @<stage_name>;
This command is crucial for data ingestion workflows in Snowflake, especially when preparing to load data using the COPY INTO command.
Reference: Snowflake Documentation on Using the PUT Command: https://docs.snowflake.com/en/sql- reference/sql/put.html
NEW QUESTION # 71
Which data formats are supported by Snowflake when unloading semi-structured data? (Select TWO).
- A. Plain text file containing XML elements
- B. Binary file in Parquet
- C. Newline Delimited JSON
- D. Comma-separated JSON
- E. Binary file in Avro
Answer: B,C
Explanation:
Snowflake supports a variety of file formats for unloading semi-structured data, among which Parquet and Newline Delimited JSON (NDJSON) are two widely used formats.
B . Binary file in Parquet: Parquet is a columnar storage file format optimized for large-scale data processing and analysis. It is especially suited for complex nested data structures.
D . Newline Delimited JSON (NDJSON): This format represents JSON records separated by newline characters, facilitating the storage and processing of multiple, separate JSON objects in a single file.
These formats are chosen for their efficiency and compatibility with data analytics tools and ecosystems, enabling seamless integration and processing of exported data.
Reference:
Snowflake Documentation: Data Unloading
NEW QUESTION # 72
A query runs significantly slower than expected. Which Query Profile metrics will help identify the issue?
(Select TWO).
- A. Processing
- B. Query Plan execution steps
- C. Partitions scanned
- D. Bytes scanned
- E. Resource usage
Answer: B,C
NEW QUESTION # 73
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