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Snowflake ARA-C01 Exam Syllabus Topics:

Section	Objectives
Performance and Optimization	- Cost optimization 1. Resource monitoring and cost control 2. Storage and compute efficiency - Query performance tuning 1. Query profiling and optimization techniques 2. Warehouse sizing and scaling
Data Architecture and Design	- Design scalable Snowflake architectures 1. Account and environment strategy 2. Multi-cluster virtual warehouse design - Data modeling and schema design 1. Star and snowflake schema concepts 2. Semi-structured data modeling
Security, Governance, and Compliance	- Data governance 1. Audit and compliance features 2. Data classification and masking policies - Access control and security model 1. Role-based access control (RBAC) 2. Network security and authentication
Data Pipelines and Data Engineering	- Data ingestion and transformation 1. Snowpipe and continuous data loading 2. Batch and streaming ingestion - Data orchestration 1. Pipeline design patterns 2. Tasks and Streams usage
Data Sharing and Collaboration	- Secure data sharing 1. Data marketplace concepts 2. Cross-account data sharing

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Snowflake SnowPro Advanced Architect Certification Sample Questions (Q159-Q164):

NEW QUESTION # 159
What transformations are supported in the below SQL statement? (Select THREE).
CREATE PIPE ... AS COPY ... FROM (...)

A. Incoming data can be joined with other tables.
B. Data can be filtered by an optional where clause.
C. The ON ERROR - ABORT statement command can be used.
D. Columns can be omitted.
E. Type casts are supported.
F. Columns can be reordered.

Answer: B,D,F

NEW QUESTION # 160
What are some of the characteristics of result set caches? (Choose three.)

A. The result set cache is not shared between warehouses.
B. The retention period can be reset for a maximum of 31 days.
C. Each time persisted results for a query are used, a 24-hour retention period is reset.
D. The data stored in the result cache will contribute to storage costs.
E. Time Travel queries can be executed against the result set cache.
F. Snowflake persists the data results for 24 hours.

Answer: A,C,F

Explanation:
In Snowflake, the characteristics of result set caches include persistence of data results for 24 hours (B), each use of persisted results resets the 24-hour retention period (C), and result set caches are not shared between different warehouses (F). The result set cache is specifically designed to avoid repeated execution of the same query within this timeframe, reducing computational overhead and speeding up query responses. These caches do not contribute to storage costs, and their retention period cannot be extended beyond the default duration nor up to 31 days, as might be misconstrued.
Reference: Snowflake Documentation on Result Set Caching.

NEW QUESTION # 161
A company is using Snowflake in Azure in the Netherlands. The company analyst team also has data in JSON format that is stored in an Amazon S3 bucket in the AWS Singapore region that the team wants to analyze.
The Architect has been given the following requirements:
1. Provide access to frequently changing data
2. Keep egress costs to a minimum
3. Maintain low latency
How can these requirements be met with the LEAST amount of operational overhead?

A. Use AWS Transfer Family to replicate data between the S3 bucket in AWS Singapore and an Azure Netherlands Blob storage, then use an external table against the Blob storage.
B. Use a materialized view on top of an external table against the S3 bucket in AWS Singapore.
C. Copy the data between providers from S3 to Azure Blob storage to collocate, then use Snowpipe for data ingestion.
D. Use an external table against the S3 bucket in AWS Singapore and copy the data into transient tables.

Answer: D

Explanation:
Explanation: Option A is the best design to meet the requirements because it uses a materialized view on top of an external table against the S3 bucket in AWS Singapore. A materialized view is a database object that contains the results of a query and can be refreshed periodically to reflect changes in the underlying data1. An external table is a table that references data files stored in a cloud storage service, such as Amazon S32. By using a materialized view on top of an external table, the company can provide access to frequently changing data, keep egress costs to a minimum, and maintain low latency. This is because the materialized view will cache the query results in Snowflake, reducing the need to access the external data files and incur network charges. The materialized view will also improve the query performance by avoiding scanning the external data files every time. The materialized view can be refreshed on a schedule or on demand to capture the changes in the external data files1.
Option B is not the best design because it uses an external table against the S3 bucket in AWS Singapore and copies the data into transient tables. A transient table is a table that is not subject to the Time Travel and Fail-safe features of Snowflake, and is automatically purged after a period of time3. By using an external table and copying the data into transient tables, the company will incur more egress costs and operational overhead than using a materialized view. This is because the external table will access the external data files every time a query is executed, and the copy operation will also transfer data from S3 to Snowflake. The transient tables will also consume more storage space in Snowflake and require manual maintenance to ensure they are up to date.
Option C is not the best design because it copies the data between providers from S3 to Azure Blob storage to collocate, then uses Snowpipe for data ingestion. Snowpipe is a service that automates the loading of data from external sources into Snowflake tables4. By copying the data between providers, the company will incur high egress costs and latency, as well as operational complexity and maintenance of the infrastructure. Snowpipe will also add another layer of processing and storage in Snowflake, which may not be necessary if the external data files are already in a queryable format.
Option D is not the best design because it uses AWS Transfer Family to replicate data between the S3 bucket in AWS Singapore and an Azure Netherlands Blob storage, then uses an external table against the Blob storage. AWS Transfer Family is a service that enables secure and seamless transfer of files over SFTP, FTPS, and FTP to and from Amazon S3 or Amazon EFS5. By using AWS Transfer Family, the company will incur high egress costs and latency, as well as operational complexity and maintenance of the infrastructure. The external table will also access the external data files every time a query is executed, which may affect the query performance.

NEW QUESTION # 162
You ran a query and the query
SELECT * FROM inventory WHERE BIBNUMBER = 2805127;
The query profile looks as below. If you would like to further tune the query, what is the best thing to do?

A. alter table inventory cluster by (BIBNUMBER);
B. Execute the below query to enable auto clustering
C. Divide the table into multiple smaller tables
D. Create an index on column BIBNUMBER

Answer: A

NEW QUESTION # 163
What are characteristics of the use of transactions in Snowflake? (Select TWO).

A. Explicit transactions should contain only DML statements and query statements. All DDL statements implicitly commit active transactions.
B. Explicit transactions can contain DDL, DML, and query statements.
C. The autocommit setting can be changed inside a stored procedure.
D. A transaction can be started explicitly by executing a begin transaction statement and end explicitly by executing an end transaction statement.
E. A transaction can be started explicitly by executing a begin work statement and end explicitly by executing a commit work statement.

Answer: B,C

NEW QUESTION # 164
......

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