Free Access Snowflake.DEA-C01.v2026-09-04.q215 Practice Test (Page 37)

Question 176

A company stores data in a data lake that is in Amazon S3. Some data that the company stores in the data lake contains personally identifiable information (PII). Multiple user groups need to access the raw data. The company must ensure that user groups can access only the PII that they require.
Which solution will meet these requirements with the LEAST effort?

A.Use Amazon Athena to query the data. Set up AWS Lake Formation and create data filters to establish levels of access for the company's IAM roles. Assign each user to the IAM role that matches the user's PII access requirements.

B.Use Amazon QuickSight to access the data. Use column-level security features in QuickSight to limit the PII that users can retrieve from Amazon S3 by using Amazon Athena. Define QuickSight access levels based on the PII access requirements of the users.

C.Build a custom query builder UI that will run Athena queries in the background to access the data.
Create user groups in Amazon Cognito. Assign access levels to the user groups based on the PII access requirements of the users.

D.Create IAM roles that have different levels of granular access. Assign the IAM roles to IAM user groups. Use an identity-based policy to assign access levels to user groups at the column level.

Question 177

A company needs to use Amazon Athena to analyze data that is in an Amazon S3 bucket. A data engineer needs to configure AWS Glue table partitions for year, month, and day. The data engineer needs to create the partitions every day to adjust to schema changes in the data.
Which solution will meet these requirements?

A.Use AWS Glue DataBrew to create the partitions for the AWS Glue table.

B.Use an AWS Lambda function to create the partitions for the AWS Glue table.

C.Set partition projection properties for the AWS Glue table.

D.Configure an AWS Glue crawler to run on a set schedule.

Question 178

If external software i.e. TIBCO, exports Data fields enclosed in quotes but inserts a leading space before the opening quotation character for each field, How Snowflake handle it? [Select 2]

A.Snowflake automatically handles leading spaces by trimming implicitly & removes the quotation marks enclosing each field.

B.field_optionally_enclosed_by option along with TRIM_IF function in COPY INTO statement can be used to handle this scenario successfully.

C.Snowflake reads the leading space rather than the opening quotation character as the beginning of the field and the quotation characters are interpreted as string data.
(Correct)

D.COPY command trims the leading space and removes the quotation marks enclosing each field
1.copy into SFtable
2.from @%SFtable
3.file_format = (type = csv trim_space=true field_optionally_enclosed_by = '0x22');

Question 179

Find out the odd one out:

A.1. Bulk Data Load: Loads are always performed in a single transaction.
2. SnowPipe: Loads are combined or split into a single or multiple transactions based on the number and size of the rows in each data file.

B.1. Bulk Data Load: Requires a user-specified warehouse to execute COPY statements.
2. SnowPipe: Uses Snowflake-supplied compute resources.

C.1. Bulk Data Load: Billed for the amount of time each virtual warehouse is active.
2. SnowPipe: Billed according to the compute resources used in the Snowpipe ware-house while loading the files.

D.1. Bulk Data Load: Load history Stored in the metadata of the target table for 365 days.
2. SnowPipe: Load history Stored in the metadata of the pipe for 64 days.

Question 180

A company maintains an Amazon Redshift provisioned cluster that the company uses for extract, transform, and load (ETL) operations to support critical analysis tasks. A sales team within the company maintains a Redshift cluster that the sales team uses for business intelligence (BI) tasks.
The sales team recently requested access to the data that is in the ETL Redshift cluster so the team can perform weekly summary analysis tasks. The sales team needs to join data from the ETL cluster with data that is in the sales team's BI cluster.
The company needs a solution that will share the ETL cluster data with the sales team without interrupting the critical analysis tasks. The solution must minimize usage of the computing resources of the ETL cluster.
Which solution will meet these requirements?

A.Set up the sales team BI cluster as a consumer of the ETL cluster by using Redshift data sharing.

B.Create materialized views based on the sales team's requirements. Grant the sales team direct access to the ETL cluster.

C.Create database views based on the sales team's requirements. Grant the sales team direct access to the ETL cluster.

D.Unload a copy of the data from the ETL cluster to an Amazon S3 bucket every week. Create an Amazon Redshift Spectrum table based on the content of the ETL cluster.

Other Version: 1887Snowflake.DEA-C01.v2024-01-27.q66

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Question 176

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