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| Section | Weight | Objectives |
|---|
| Account Access and Security | 10% | - Authentication and authorization
- 1. User management
- 2. Roles and privileges
- Data security features
- 1. Encryption
- 2. Network policies
|
| Data Loading and Unloading | 20% | - Data ingestion
- 1. Stages (internal/external)
- 2. COPY INTO command
- Data export
|
| Performance and Optimization | 15% | - Query performance
- 1. Caching mechanisms
- 2. Query profiling
- Virtual warehouse optimization
|
| Data Sharing and Marketplace | 10% | - Secure data sharing - Snowflake Marketplace
- 1. Data exchange concepts
|
| Snowflake Architecture and Data Cloud Concepts | 25% | - Data Cloud fundamentals
- 1. Shared data architecture
- 2. Separation of storage and compute
- Snowflake architecture overview
- 1. Cloud services layer
- 2. Compute layer (virtual warehouses)
- 3. Storage layer
|
| Data Transformation and Processing | 20% | - Data pipelines
- 1. Data transformation workflows
- 2. Streams and tasks
- SQL processing
- 1. Joins and subqueries
- 2. SELECT queries
|
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Snowflake SnowPro Core Certification Exam Sample Questions (Q436-Q441):
NEW QUESTION # 436
Assume there is a table consisting of five micro-partitions with values ranging from A to Z.
Which diagram indicates a well-clustered table?
Answer: A
NEW QUESTION # 437
Which Snowflake technique can be used to improve the performance of a query?
- A. Using INDEX__HINTS
- B. Indexing
- C. Clustering
- D. Fragmenting
Answer: C
Explanation:
Explanation
https://docs.snowflake.com/en/user-guide/tables-clustering-keys.html
NEW QUESTION # 438
Where can a user find and review the failed logins of a specific user for the past 30 days?
- A. The SESSIONS view in ACCOUNT_USAGE
- B. The ACCESS_HISTORY view in ACCOUNT_USAGE
- C. The LOGIN_HISTORY view in ACCOUNT_USAGE
- D. The USERS view in ACCOUNT_USAGE
Answer: C
Explanation:
The LOGIN_HISTORY view in the ACCOUNT_USAGE schema provides information about login attempts, including both successful and failed logins. This view can be used to review the failed login attempts of a specific user for the past 30 days. References: [COF-C02] SnowPro Core Certification Exam Study Guide
NEW QUESTION # 439
During periods of warehouse contention which parameter controls the maximum length of time a warehouse will hold a query for processing?
- A. STATEMENT_TIMEOUT__IN__SECONDS
- B. STATEMENT_QUEUED_TIMEOUT_IN_SECONDS
- C. QUERY_TIMEOUT_IN_SECONDS
- D. MAX_CONCURRENCY__LEVEL
Answer: B
Explanation:
The parameter STATEMENT_QUEUED_TIMEOUT_IN_SECONDS sets the limit for a query to wait in the queue in order to get its chance of running on the warehouse. The query will quit after reaching this limit.
By default, the value of this parameter is 0 which mean the queries will wait indefinitely in the waiting queue
https://community.snowflake.com/s/article/Warehouse-Concurrency-and-Statement-Timeout-Parameters#:~:text
NEW QUESTION # 440
Which types of subqueries does Snowflake support? (Select TWO).
- A. EXISTS, ANY / ALL, and IN subqueries in where clauses: these subqueries can be correlated only
- B. EXISTS, ANY / ALL, and IN subqueries in WHERE clauses: these subqueries can be uncorrelated only
- C. Uncorrelated scalar subqueries in any place that a value expression can be used
- D. EXISTS, ANY /ALL, and IN subqueries in WHERE clauses: these subqueries can be correlated or uncorrelated
- E. Uncorrelated scalar subqueries in WHERE clauses
Answer: C,D
Explanation:
Snowflake supports a variety of subquery types, including both correlated and uncorrelated subqueries. The correct answers are B and E, which highlight Snowflake's flexibility in handling subqueries within SQL queries.
* Uncorrelated Scalar Subqueries: These are subqueries that can execute independently of the outer query. They return a single value and can be used anywhere a value expression is allowed, offering great flexibility in SQL queries.
* EXISTS, ANY/ALL, and IN Subqueries: These subqueries are used in WHERE clauses to filter the results of the main query based on the presence or absence of matching rows in a subquery. Snowflake supports both correlated and uncorrelated versions of these subqueries, providing powerful tools for complex data analysis scenarios.
* Examples and Usage:
* Uncorrelated Scalar Subquery:
SELECT * FROM employees WHERE salary > (SELECT AVG(salary) FROM employees);
* Correlated EXISTS Subquery:
SELECT * FROM orders o WHERE EXISTS (SELECT 1 FROM customer c WHERE c.id = o.customer_id AND c.region = 'North America'); Reference: For comprehensive details on the types of subqueries supported by Snowflake and examples of their usage, consult the Snowflake documentation on subqueries:
https://docs.snowflake.com/en/sql-reference/constructs/subqueries.html
NEW QUESTION # 441
......
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