Free Access Snowflake.DEA-C01.v2026-09-04.q215 Practice Test (Page 41)

Question 196

Mark the correct statements about Cache?

A.Materialized views are more flexible than, but typically slower than, cached results.

B.Materialized views are faster than tables because of their "cache" (i.e. the query results for the view); in addition, if data has changed, they can use their "cache" for data that hasn't changed and use the base table for any data that has changed.

C.For persisted query results of all sizes, the cache expires after 24 hours.

D.The size of the warehouse cache is determined by the compute resources in the ware-house.

E.Warehouse cache is dropped when the warehouse is suspended, which may result in slower initial performance for some queries after the warehouse is resumed.

Correct Answer: A,B,C,D,E

Explanation
How Does Warehouse Caching Impact Queries?
Each warehouse, when running, maintains a cache of table data accessed as queries are processed by the warehouse. This enables improved performance for subsequent queries if they are able to read from the cache instead of from the table(s) in the query. The size of the cache is determined by the compute resources in the warehouse (i.e. the larger the warehouse and, therefore, more compute re-sources in the warehouse), the larger the cache.
This cache is dropped when the warehouse is suspended, which may result in slower initial perfor-mance for some queries after the warehouse is resumed. As the resumed warehouse runs and pro-cesses more queries, the cache is rebuilt, and queries that are able to take advantage of the cache will experience improved performance.
Keep this in mind when deciding whether to suspend a warehouse or leave it running. In other words, consider the trade-off between saving credits by suspending a warehouse versus maintaining the cache of data from previous queries to help with performance.
Using Persisted Query Results
When a query is executed, the result is persisted (i.e. cached) for a period of time. At the end of the time period, the result is purged from the system.
Snowflake uses persisted query results to avoid re-generating results when nothing has changed (i.e. "retrieval optimization"). In addition, you can use persisted query results to post-process the results (e.g. layering a new query on top of the results already calculated).
For persisted query results of all sizes, the cache expires after 24 hours.
Both materialized views and cached query results provide query performance benefits:
Materialized views are more flexible than, but typically slower than, cached results.
Materialized views are faster than tables because of their "cache" (i.e. the query results for the view); in addition, if data has changed, they can use their "cache" for data that hasn't changed and use the base table for any data that has changed.
Regular views do not cache data, and therefore cannot improve performance by caching.

Question 197

A company needs to generate a one-time performance report by joining data that is stored in Amazon DynamoDB. Amazon RDS. Amazon Redshift. and Amazon S3. The company wants to avoid unnecessary data movement and to minimize query execution time. Which solution will meet these requirements?

A.Capture data from DynamoDB by using DynamoDB Streams. Migrate data from Amazon RDS by using AWS DMS. Export Amazon Redshift data. Store all data in Amazon S3. Use Redshift Spectrum to run queries.

B.Set up an AWS Glue ETL pipeline to extract, transform, and centralize data in Amazon S3. Use Amazon Athena to run analytical queries.

C.Deploy an Amazon EMR cluster powered by Apache Spark to ingest, process, and merge datasets from multiple sources. Run analytical workloads on the merged data.

D.Use Amazon Athena Federated Query to perform one-time joins and analysis across DynamoDB, Amazon RDS, Amazon Redshift, and Amazon S3.

Question 198

A company stores customer data that contains personally identifiable information (PII) in an Amazon Redshift cluster. The company's marketing, claims, and analytics teams need to be able to access the customer data.
The marketing team should have access to obfuscated claim information but should have full access to customer contact information. The claims team should have access to customer information for each claim that the team processes. The analytics team should have access only to obfuscated PII data.
Which solution will enforce these data access requirements with the LEAST administrative overhead?

A.Move the customer data to an Amazon S3 bucket. Use AWS Lake Formation to create a data lake. Use fine-grained security capabilities to grant each team appropriate permissions to access the data.

B.Create a separate Redshift cluster for each team. Load only the required data for each team.
Restrict access to clusters based on the teams.

C.Create views that include required fields for each of the data requirements. Grant the teams access only to the view that each team requires.

D.Create a separate Amazon Redshift database role for each team. Define masking policies that apply for each team separately. Attach appropriate masking policies to each team role.

Question 199

A research company stores data in an Amazon Redshift cluster. The company needs to share data between departments and maintain regulatory compliance. The company needs a solution that gives researchers access to only the records from their own departments and does not create multiple dataset copies. The solution must also ensure that personally identifiable information (PII) is protected from unauthorized access.
Which solution will meet these requirements?

A.Create a datashare in Amazon Redshift for each department. Use cross-Region data sharing to distribute copies of the entire dataset to each department's Amazon Redshift cluster.

B.Implement row-level security policies with basic SQL filters based on department. Attach the security policies to the data tables. Grant EXPLAIN RLS permission to authorized researchers.

C.Create separate schemas for each department with appropriate views that filter data. Grant each department access to only their respective schema.

D.Use row-level security policies with multi-condition SQL predicates. Attach the security policies to the data tables. Grant each department's role access to the appropriate policies.

Question 200

Pivoting in SQL is mainly used to transform data from:

A.Only one column to one row

B.Multiple columns into multiple rows

C.Multiple rows into multiple columns

D.One row to one column

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Question 196

Question 197

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