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Snowflake SnowPro Core Practice Test - Questions Answers, Page 63

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When should a multi-cluster virtual warehouse be used in Snowflake?

A.

When queuing is delaying query execution on the warehouse

A.

When queuing is delaying query execution on the warehouse

Answers
B.

When there is significant disk spilling shown on the Query Profile

B.

When there is significant disk spilling shown on the Query Profile

Answers
C.

When dynamic vertical scaling is being used in the warehouse

C.

When dynamic vertical scaling is being used in the warehouse

Answers
D.

When there are no concurrent queries running on the warehouse

D.

When there are no concurrent queries running on the warehouse

Answers
Suggested answer: A

Explanation:

A multi-cluster virtual warehouse in Snowflake is designed to handle high concurrency and workload demands by allowing multiple clusters of compute resources to operate simultaneously. The correct scenario to use a multi-cluster virtual warehouse is:

A . When queuing is delaying query execution on the warehouse: Multi-cluster warehouses are ideal when the demand for compute resources exceeds the capacity of a single cluster, leading to query queuing. By enabling additional clusters, you can distribute the workload across multiple compute clusters, thereby reducing queuing and improving query performance.

This is especially useful in scenarios with fluctuating workloads or where it's critical to maintain low response times for a large number of concurrent queries.

Snowflake Documentation: Multi-Cluster Warehouses at Snowflake Documentation

A JSON object is loaded into a column named data using a Snowflake variant datatype. The root node of the object is BIKE. The child attribute for this root node is BIKEID.

Which statement will allow the user to access BIKEID?

A.

select data:BIKEID

A.

select data:BIKEID

Answers
B.

select data.BIKE.BIKEID

B.

select data.BIKE.BIKEID

Answers
C.

select data:BIKE.BIKEID

C.

select data:BIKE.BIKEID

Answers
D.

select data:BIKE:BIKEID

D.

select data:BIKE:BIKEID

Answers
Suggested answer: C

Explanation:

In Snowflake, when accessing elements within a JSON object stored in a variant column, the correct syntax involves using a colon (:) to navigate the JSON structure. The BIKEID attribute, which is a child of the BIKE root node in the JSON object, is accessed using data:BIKE.BIKEID. This syntax correctly references the path through the JSON object, utilizing the colon for JSON field access and dot notation to traverse the hierarchy within the variant structure.

Reference: Snowflake documentation on accessing semi-structured data, which outlines how to use the colon and dot notations for navigating JSON structures stored in variant columns.

Which Snowflake tool is recommended for data batch processing?

A.

SnowCD

A.

SnowCD

Answers
B.

SnowSQL

B.

SnowSQL

Answers
C.

Snowsight

C.

Snowsight

Answers
D.

The Snowflake API

D.

The Snowflake API

Answers
Suggested answer: B

Explanation:

For data batch processing in Snowflake, the recommended tool is:

B . SnowSQL: SnowSQL is the command-line client for Snowflake. It allows for executing SQL queries, scripts, and managing database objects. It's particularly suitable for batch processing tasks due to its ability to run SQL scripts that can execute multiple commands or queries in sequence, making it ideal for automated or scheduled tasks that require bulk data operations.

SnowSQL provides a flexible and powerful way to interact with Snowflake, supporting operations such as loading and unloading data, executing complex queries, and managing Snowflake objects from the command line or through scripts.

Snowflake Documentation: SnowSQL (CLI Client) at Snowflake Documentation

How does the Snowflake search optimization service improve query performance?

A.

It improves the performance of range searches.

A.

It improves the performance of range searches.

Answers
B.

It defines different clustering keys on the same source table.

B.

It defines different clustering keys on the same source table.

Answers
C.

It improves the performance of all queries running against a given table.

C.

It improves the performance of all queries running against a given table.

Answers
D.

It improves the performance of equality searches.

D.

It improves the performance of equality searches.

Answers
E.

It improves the performance of equality searches: The service optimizes the performance of queries that use equality search conditions (e.g., WHERE column = value). It creates and maintains a search index on the table's columns, which significantly speeds up the retrieval of rows based on those equality search conditions. This optimization is particularly beneficial for large tables where traditional scans might be inefficient for equality searches. By using the Search Optimization Service, Snowflake can leverage the search indexes to quickly locate the rows that match the search criteria without scanning the entire table.

E.

It improves the performance of equality searches: The service optimizes the performance of queries that use equality search conditions (e.g., WHERE column = value). It creates and maintains a search index on the table's columns, which significantly speeds up the retrieval of rows based on those equality search conditions. This optimization is particularly beneficial for large tables where traditional scans might be inefficient for equality searches. By using the Search Optimization Service, Snowflake can leverage the search indexes to quickly locate the rows that match the search criteria without scanning the entire table.

Answers
Suggested answer: D

Explanation:

The Snowflake Search Optimization Service is designed to enhance the performance of specific types of queries on large tables. The correct answer is:

Snowflake Documentation: Search Optimization Service at Snowflake Documentation

What compute resource is used when loading data using Snowpipe?

A.

Snowpipe uses virtual warehouses provided by the user.

A.

Snowpipe uses virtual warehouses provided by the user.

Answers
B.

Snowpipe uses an Apache Kafka server for its compute resources.

B.

Snowpipe uses an Apache Kafka server for its compute resources.

Answers
C.

Snowpipe uses compute resources provided by Snowflake.

C.

Snowpipe uses compute resources provided by Snowflake.

Answers
D.

Snowpipe uses cloud platform compute resources provided by the user.

D.

Snowpipe uses cloud platform compute resources provided by the user.

Answers
Suggested answer: C

Explanation:

Snowpipe is Snowflake's continuous data ingestion service that allows for loading data as soon as it's available in a cloud storage stage. Snowpipe uses compute resources managed by Snowflake, separate from the virtual warehouses that users create for querying data. This means that Snowpipe operations do not consume the computational credits of user-created virtual warehouses, offering an efficient and cost-effective way to continuously load data into Snowflake.

Snowflake Documentation: Understanding Snowpipe

What is one of the characteristics of data shares?

A.

Data shares support full DML operations.

A.

Data shares support full DML operations.

Answers
B.

Data shares work by copying data to consumer accounts.

B.

Data shares work by copying data to consumer accounts.

Answers
C.

Data shares utilize secure views for sharing view objects.

C.

Data shares utilize secure views for sharing view objects.

Answers
D.

Data shares are cloud agnostic and can cross regions by default.

D.

Data shares are cloud agnostic and can cross regions by default.

Answers
Suggested answer: C

Explanation:

Data sharing in Snowflake allows for live, read-only access to data across different Snowflake accounts without the need to copy or transfer the data. One of the characteristics of data shares is the ability to use secure views. Secure views are used within data shares to restrict the access of shared data, ensuring that consumers can only see the data that the provider intends to share, thereby preserving privacy and security.

Snowflake Documentation: Understanding Secure Views in Data Sharing

Which DDL/DML operation is allowed on an inbound data share?

A.

ALTER TA3LE

A.

ALTER TA3LE

Answers
B.

INSERT INTO

B.

INSERT INTO

Answers
C.

MERGE

C.

MERGE

Answers
D.

SELECT

D.

SELECT

Answers
Suggested answer: D

Explanation:

In Snowflake, an inbound data share refers to the data shared with an account by another account. The only DDL/DML operation allowed on an inbound data share is SELECT. This restriction ensures that the shared data remains read-only for the consuming account, maintaining the integrity and ownership of the data by the sharing account.

Snowflake Documentation: Using Data Shares

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