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Snowflake COF-C02 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Data Loading & Transformation	20%	- Data ingestion methods 1. Bulk loading, continuous loading, external stages 2. File formats and data type handling - Data transformation & querying 1. DDL/DML operations and SQL functions 2. Working with structured, semi-structured, unstructured data
Topic 2: Data Pipelines & Automation	10%	- Integration & connectivity 1. Connectors and ETL/ELT patterns 2. External functions and API integration - Workflow & automation 1. Streams, tasks, and pipes 2. Change data capture and processing
Topic 3: Account Access & Security	20%	- Authentication & authorization 1. Users, roles, privileges and access control 2. Network policies and security settings - Data protection & governance 1. Encryption at rest and in transit 2. Data masking and row-level security
Topic 4: Snowflake Architecture & Cloud Concepts	25%	- Cloud data platform fundamentals 1. Virtual warehouses and resource management 2. Service layers and separation of storage/compute 3. Data storage structure and formats - Account structure and hierarchy 1. Object ownership and naming conventions 2. Organization, account, database, schema levels
Topic 5: Performance Concepts & Optimization	15%	- Data optimization features 1. Caching and result reuse 2. Clustering keys, search optimization, materialized views - Warehouse sizing & tuning 1. Query profiling and optimization techniques 2. Scaling, clustering, and concurrency management
Topic 6: Data Protection & Data Sharing	10%	- Data recovery & retention 1. Time Travel, Fail-safe, cloning capabilities 2. Data retention policies and backup - Secure data sharing 1. Sharing security and access control 2. Direct sharing, data exchanges, reader accounts

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Snowflake SnowPro Core Certification Exam Sample Questions (Q792-Q797):

NEW QUESTION # 792
When semi-structured data includes dates and timestamps as string values, what Snowflake recommendation will OPTIMIZE pruning and MINIMIZE storage consumption?

A. Flatten the key data into relational columns.
B. Strip the outer array using TRY_PARSE_JSON.
C. Store the data as an ARRAY data type.
D. Store the data as a VARIANT data type.

Answer: A

NEW QUESTION # 793
True or False: Loading data into Snowflake requires that source data files be no larger than 16MB.

A. False
B. True

Answer: A

Explanation:
Explanation
By default, COPY INTO location statements separate table data into a set of output files to take advantage of parallel operations. The maximum size for each file is set using the MAX_FILE_SIZE copy option. The default value is 16777216 (16 MB) but can be increased to accommodate larger files. The maximum file size supported is 5 GB for Amazon S3, Google Cloud Storage, or Microsoft Azure stages. To unload data to a single output file (at the potential cost of decreased performance), specify the SINGLE = true copy option in your statement. You can optionally specify a name for the file in the path.

NEW QUESTION # 794
What are key characteristics of virtual warehouses in Snowflake? (Select TWO).

A. Warehouses can be resized at any time, even while running.
B. Warehouses are billed on a per-minute usage basis.
C. Warehouses that are multi-cluster can have nodes of different sizes.
D. Warehouses can only be used for querying and cannot be used for data loading.
E. Warehouses can be started and stopped at any time.

Answer: A,E

Explanation:
Virtual warehouses in Snowflake can be started and stopped at any time, providing flexibility in managing compute resources. They can also be resized at any time, even while running, to accommodate varying workloads910. References: [COF-C02] SnowPro Core Certification Exam Study Guide

NEW QUESTION # 795
Which of the following indicates that it may be appropriate to use a clustering key for a table? (Select TWO).

A. Queries on the table are running slower than expected
B. The table contains a column that has very low cardinality
C. DML statements that are being issued against the table are blocked
D. The clustering depth for the table is large
E. The table has a small number of micro-partitions

Answer: A,D

Explanation:
A clustering key in Snowflake is used to co-locate similar data within the same micro-partitions to improve query performance, especially for large tables where data is not naturally ordered or has become fragmented due to extensive DML operations. The appropriate use of a clustering key can lead to improved scan efficiency and better column compression, resulting in faster query execution times.
The indicators that it may be appropriate to use a clustering key for a table include:
D . Queries on the table are running slower than expected: This can happen when the data in the table is not well-clustered, leading to inefficient scans during query execution.
E . The clustering depth for the table is large: A large clustering depth indicates that the table's data is spread across many micro-partitions, which can degrade query performance as more data needs to be scanned.
Reference:
Snowflake Documentation on Clustering Keys & Clustered Tables
Snowflake Documentation on SYSTEM$CLUSTERING_INFORMATION
Stack Overflow discussion on cluster key selection in Snowflake

NEW QUESTION # 796
True or False: Data in Fail-safe can be deleted by a user or the Snowflake team before it expires.

A. False
B. True

Answer: A

NEW QUESTION # 797
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