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Snowflake COF-C02 Exam Syllabus Topics:

Section	Weight	Objectives
Performance Concepts & Optimization	15%	- Warehouse sizing & tuning 1. Query profiling and optimization techniques 2. Scaling, clustering, and concurrency management - Data optimization features 1. Caching and result reuse 2. Clustering keys, search optimization, materialized views
Snowflake Architecture & Cloud Concepts	25%	- Account structure and hierarchy 1. Organization, account, database, schema levels 2. Object ownership and naming conventions - Cloud data platform fundamentals 1. Virtual warehouses and resource management 2. Data storage structure and formats 3. Service layers and separation of storage/compute
Data Pipelines & Automation	10%	- Workflow & automation 1. Streams, tasks, and pipes 2. Change data capture and processing - Integration & connectivity 1. Connectors and ETL/ELT patterns 2. External functions and API integration
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
Data Loading & Transformation	20%	- Data ingestion methods 1. File formats and data type handling 2. Bulk loading, continuous loading, external stages - Data transformation & querying 1. Working with structured, semi-structured, unstructured data 2. DDL/DML operations and SQL functions
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 (Q982-Q987):

NEW QUESTION # 982
Which Snowflake mechanism is used to limit the number of micro-partitions scanned by a query?

A. Caching
B. Query pruning
C. Retrieval optimization
D. Cluster depth

Answer: B

NEW QUESTION # 983
What are the main differences between the account usage views and the information schema views? (Select TWO).

A. Account issue views contain dropped objects but information schema views do not.
B. Data retention for account usage views is 1 year but is 7 days to 6 months for information schema views, depending on the view.
C. Information schema views are read-only but account usage views are not.
D. Account usage views do not contain data about tables but information schema views do.
E. No active warehouse to needed to query account usage views but one is needed to query information schema views.

Answer: A,B

NEW QUESTION # 984
What is the benefit of using the STRIP_OUTER_ARRAY parameter with the COPY INTO <table> command when loading data from a JSON file into a table?

A. It transforms a pivoted table into an array.
B. It tokenizes each data string using the defined delimiters.
C. It flattens multiple arrays into a single array.
D. It removes the outer array structure and loads separate rows of data

Answer: D

Explanation:
The STRIP_OUTER_ARRAY parameter in the COPY INTO <table> command is used when loading data from a JSON file into a table. This parameter removes the outer array structure from the JSON data and loads separate rows of data into the table.
Understanding the STRIP_OUTER_ARRAY Parameter:
JSON files often contain data in an array format where multiple records are nested within a single outer array.
The STRIP_OUTER_ARRAY parameter helps in simplifying the loading process by removing this outer array, allowing each element within the array to be loaded as a separate row in the target table.
How It Works:
When the STRIP_OUTER_ARRAY parameter is set to TRUE, Snowflake treats each item within the outer array as an individual record.
This eliminates the need for additional parsing or transformation steps that would otherwise be required to handle nested arrays.
Example Usage:
FROM @my_stage/file.json
FILE_FORMAT = (TYPE = 'JSON' STRIP_OUTER_ARRAY = TRUE);
In this example, the JSON file containing an array of objects is loaded into the table my_table.
Each object within the array is loaded as a separate row, without the outer array structure.
Benefits:
Simplifies data loading: By removing the outer array, the data is directly loaded into the table without additional manipulation.
Enhances performance: Streamlines the loading process, reducing the complexity and potential errors in handling nested JSON structures.
Reference:
Snowflake Documentation: COPY INTO <table>
Snowflake Documentation: JSON File Format Options

NEW QUESTION # 985
Which of the following are best practices for loading data into Snowflake? (Choose three.)

A. Enclose fields that contain delimiter characters in single or double quotes.
B. When planning which warehouse(s) to use for data loading, start with the largest warehouse possible.
C. Partition the staged data into large folders with random paths, allowing Snowflake to determine the best way to load each file.
D. Aim to produce data files that are between 100 MB and 250 MB in size, compressed.
E. Split large files into a greater number of smaller files to distribute the load among the compute resources in an active warehouse.
F. Load data from files in a cloud storage service in a different region or cloud platform from the service or region containing the Snowflake account, to save on cost.

Answer: A,B,F

NEW QUESTION # 986
Which statements are correct concerning the leveraging of third-party data from the Snowflake Data Marketplace? (Choose two.)

A. Data is available without copying or moving.
B. Data transformations are required when combining Data Marketplace datasets with existing data in Snowflake.
C. Data is not available for copying or moving to an individual Snowflake account.
D. Data is live, ready-to-query, and can be personalized.
E. Data needs to be loaded into a cloud provider as a consumer account.

Answer: A,D

NEW QUESTION # 987
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