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Question 41

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A sales table FCT_SALES has 100 million records.

The following Query was executed

SELECT COUNT (1) FROM FCT__SALES;

How did Snowflake fulfill this query?

Query against the result set cache
Query against the result set cache
Query against a virtual warehouse cache
Query against a virtual warehouse cache
Query against the most-recently created micro-partition
Query against the most-recently created micro-partition
Query against the metadata excite
Query against the metadata excite
Suggested answer: D

Explanation:

Snowflake is designed to optimize query performance by utilizing metadata for certain types of queries. When executing aCOUNTquery, Snowflake can often fulfill the request by accessing metadata about the table's row count, rather than scanning the entire table or micro-partitions. This is particularly efficient for large tables likeFCT_SALESwith a significant number of records. The metadata layer maintains statistics about the table, including the row count, which enables Snowflake to quickly return the result of aCOUNTquery without the need to perform a full scan.

Snowflake Documentation on Metadata Management

SnowPro Core Certification Study Guide

asked 23/09/2024
Arash Rind
42 questions

Question 42

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Which cache type is used to cache data output from SQL queries?

Metadata cache
Metadata cache
Result cache
Result cache
Remote cache
Remote cache
Local file cache
Local file cache
Suggested answer: B

Explanation:

TheResult cacheis used in Snowflake to cache the data output from SQL queries. This feature is designed to improve performance by storing the results of queries for a period of time. When the same or similar query is executed again, Snowflake can retrieve the result from this cache instead of re-computing the result, which saves time and computational resources.

Snowflake Documentation on Query Results Cache

SnowPro Core Certification Study Guide

asked 23/09/2024
Wallace Davison
35 questions

Question 43

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What is a key feature of Snowflake architecture?

Zero-copy cloning creates a mirror copy of a database that updates with the original
Zero-copy cloning creates a mirror copy of a database that updates with the original
Software updates are automatically applied on a quarterly basis
Software updates are automatically applied on a quarterly basis
Snowflake eliminates resource contention with its virtual warehouse implementation
Snowflake eliminates resource contention with its virtual warehouse implementation
Multi-cluster warehouses allow users to run a query that spans across multiple clusters
Multi-cluster warehouses allow users to run a query that spans across multiple clusters
Snowflake automatically sorts DATE columns during ingest for fast retrieval by date
Snowflake automatically sorts DATE columns during ingest for fast retrieval by date
Suggested answer: C

Explanation:

One of the key features of Snowflake's architecture is its unique approach to eliminating resource contention through the use of virtual warehouses. This is achieved by separating storage and compute resources, allowing multiple virtual warehouses to operate independently on the same data without affecting each other. This means that different workloads, such as loading data, running queries, or performing complex analytics, can be processed simultaneously without any performance degradation due to resource contention.

Snowflake Documentation on Virtual Warehouses

SnowPro Core Certification Study Guide

asked 23/09/2024
Vu Tung
29 questions

Question 44

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What is a limitation of a Materialized View?

A Materialized View cannot support any aggregate functions
A Materialized View cannot support any aggregate functions
A Materialized View can only reference up to two tables
A Materialized View can only reference up to two tables
A Materialized View cannot be joined with other tables
A Materialized View cannot be joined with other tables
A Materialized View cannot be defined with a JOIN
A Materialized View cannot be defined with a JOIN
Suggested answer: D

Explanation:

Materialized Views in Snowflake are designed to store the result of a query and can be refreshed to maintain up-to-date data. However, they have certain limitations, one of which is that they cannot be defined using a JOIN clause. This means that a Materialized View can only be created based on a single source table and cannot combine data from multiple tables using JOIN operations.

Snowflake Documentation on Materialized Views

SnowPro Core Certification Study Guide

asked 23/09/2024
Ty Murray
36 questions

Question 45

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What features does Snowflake Time Travel enable?

Querying data-related objects that were created within the past 365 days
Querying data-related objects that were created within the past 365 days
Restoring data-related objects that have been deleted within the past 90 days
Restoring data-related objects that have been deleted within the past 90 days
Conducting point-in-time analysis for Bl reporting
Conducting point-in-time analysis for Bl reporting
Analyzing data usage/manipulation over all periods of time
Analyzing data usage/manipulation over all periods of time
Suggested answer: B, C

Explanation:

Snowflake Time Travel is a powerful feature that allows users to access historical data within a defined period. It enables two key capabilities:

B) Restoring data-related objects that have been deleted within the past 90 days: Time Travel can be used to restore tables, schemas, and databases that have been accidentally or intentionally deleted within the Time Travel retention period.

C) Conducting point-in-time analysis for BI reporting: It allows users to query historical data as it appeared at a specific point in time within the Time Travel retention period, which is crucial for business intelligence and reporting purposes.

While Time Travel does allow querying of past data, it is limited to the retention period set for the Snowflake account, which is typically 1 day for standard accounts and can be extended up to 90 days for enterprise accounts. It does not enable querying or restoring objects created or deleted beyond the retention period, nor does it provide analysis over all periods of time.

Snowflake Documentation on Time Travel

SnowPro Core Certification Study Guide

asked 23/09/2024
Marcin Golec
33 questions

Question 46

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Which statement about billing applies to Snowflake credits?

Credits are billed per-minute with a 60-minute minimum
Credits are billed per-minute with a 60-minute minimum
Credits are used to pay for cloud data storage usage
Credits are used to pay for cloud data storage usage
Credits are consumed based on the number of credits billed for each hour that a warehouse runs
Credits are consumed based on the number of credits billed for each hour that a warehouse runs
Credits are consumed based on the warehouse size and the time the warehouse is running
Credits are consumed based on the warehouse size and the time the warehouse is running
Suggested answer: D

Explanation:

Snowflake credits are the unit of measure for the compute resources used in Snowflake. The number of credits consumed depends on the size of the virtual warehouse and the time it is running. Larger warehouses consume more credits per hour than smaller ones, and credits are billed for the time the warehouse is active, regardless of the actual usage within that time.

asked 23/09/2024
Veridjan Hoxha
36 questions

Question 47

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What Snowflake features allow virtual warehouses to handle high concurrency workloads? (Select TWO)

The ability to scale up warehouses
The ability to scale up warehouses
The use of warehouse auto scaling
The use of warehouse auto scaling
The ability to resize warehouses
The ability to resize warehouses
Use of multi-clustered warehouses
Use of multi-clustered warehouses
The use of warehouse indexing
The use of warehouse indexing
Suggested answer: B, D

Explanation:

Snowflake's architecture is designed to handle high concurrency workloads through several features, two of which are particularly effective:

B) The use of warehouse auto scaling: This feature allows Snowflake to automatically adjust the compute resources allocated to a virtual warehouse in response to the workload. If there is an increase in concurrent queries, Snowflake can scale up the resources to maintain performance.

D) Use of multi-clustered warehouses: Multi-clustered warehouses enable Snowflake to run multiple clusters of compute resources simultaneously. This allows for the distribution of queries across clusters, thereby reducing the load on any single cluster and improving the system's ability to handle a high number of concurrent queries.

These features ensure that Snowflake can manage varying levels of demand without manual intervention, providing a seamless experience even during peak usage.

Snowflake Documentation on Virtual Warehouses

SnowPro Core Certification Study Guide

asked 23/09/2024
An Khang Nguyen
48 questions

Question 48

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When reviewing the load for a warehouse using the load monitoring chart, the chart indicates that a high volume of Queries are always queuing in the warehouse According to recommended best practice, what should be done to reduce the Queue volume? (Select TWO).

Use multi-clustered warehousing to scale out warehouse capacity.
Use multi-clustered warehousing to scale out warehouse capacity.
Scale up the warehouse size to allow Queries to execute faster.
Scale up the warehouse size to allow Queries to execute faster.
Stop and start the warehouse to clear the queued queries
Stop and start the warehouse to clear the queued queries
Migrate some queries to a new warehouse to reduce load
Migrate some queries to a new warehouse to reduce load
Limit user access to the warehouse so fewer queries are run against it.
Limit user access to the warehouse so fewer queries are run against it.
Suggested answer: A, B

Explanation:

To address a high volume of queries queuing in a warehouse, Snowflake recommends two best practices:

A) Use multi-clustered warehousing to scale out warehouse capacity: This approach allows for the distribution of queries across multiple clusters within a warehouse, effectively managing the load and reducing the queue volume.

B) Scale up the warehouse size to allow Queries to execute faster: Increasing the size of the warehouse provides more compute resources, which can reduce the time it takes for queries to execute and thus decrease the number of queries waiting in the queue.

These strategies help to optimize the performance of the warehouse by ensuring that resources are scaled appropriately to meet demand.

Snowflake Documentation on Multi-Cluster Warehousing

SnowPro Core Certification best practices

asked 23/09/2024
BurtAnderson Carter
35 questions

Question 49

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Which of the following objects can be shared through secure data sharing?

Masking policy
Masking policy
Stored procedure
Stored procedure
Task
Task
External table
External table
Suggested answer: D

Explanation:

Secure data sharing in Snowflake allows users to share various objects between Snowflake accounts without physically copying the data, thus not consuming additional storage. Among the options provided, external tables can be shared through secure data sharing. External tables are used to query data directly from files in a stage without loading the data into Snowflake tables, making them suitable for sharing across different Snowflake accounts.

Snowflake Documentation on Secure Data Sharing

SnowPro Core Certification Companion: Hands-on Preparation and Practice

asked 23/09/2024
Michael Amann
41 questions

Question 50

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Which of the following commands cannot be used within a reader account?

CREATE SHARE
CREATE SHARE
ALTER WAREHOUSE
ALTER WAREHOUSE
DROP ROLE
DROP ROLE
SHOW SCHEMAS
SHOW SCHEMAS
DESCRBE TABLE
DESCRBE TABLE
Suggested answer: A

Explanation:

In Snowflake, a reader account is a type of account that is intended for consuming shared data rather than performing any data management or DDL operations. TheCREATE SHAREcommand is used to share data from your account with another account, which is not a capability provided to reader accounts. Reader accounts are typically restricted from creating shares, as their primary purpose is to read shared data rather than to share it themselves.

Snowflake Documentation on Reader Accounts

SnowPro Core Certification Study Guide

asked 23/09/2024
Norman Camacho
47 questions
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