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| Certification Vendor: | Snowflake |
|---|---|
| Exam Name: | SnowPro Advanced: Data Engineer Certification Exam |
| Exam Number: | DEA-C01 |
| Exam Duration: | 115 minutes |
| Certificate Validity Period: | 2 years |
| Exam Price: | $375 USD |
| Real Exam Qty: | Approximately 65 questions |
| Available Languages: | English |
| Exam Format: | Multiple select, Multiple choice, Scenario-based questions |
| Passing Score: | Not publicly disclosed (Snowflake uses scaled scoring) |
| Related Certifications: | SnowPro Core Certification |
| Recommended Training: | Snowflake University Training SnowPro Advanced Data Engineer Exam Guide |
| Exam Registration: | Snowflake Certification Portal |
| Sample Questions: | Snowflake DEA-C01 Sample Questions |
| Exam Way: | Online proctored or authorized testing center |
| Pre Condition: | Recommended: SnowPro Core Certification or equivalent Snowflake experience |
| Official Syllabus URL: | https://www.snowflake.com/certifications/snowpro-advanced-data-engineer/ |
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NEW QUESTION # 103
In one of your created Schema, you have been required to create Internal Stages, what are the In-correct considerations you can noticed from the below options? [Select All that Apply]
Answer: A
Explanation:
Explanation
A stage specifies where data files are stored (i.e. "staged") so that the data in the files can be loaded into a table.
Types of Internal Stages
User Stages
Table Stages
Named Stages
By default, each user and table in Snowflake is automatically allocated an internal stage for staging data files to be loaded. In addition, you can create named internal stages.
File staging information is required during both steps in the data loading process:
You must specify an internal stage in the PUT command when uploading files to Snowflake.
You must specify the same stage in the COPY INTO <table> command when loading data into a table from the staged files.
Consider the best type of stage for specific data files. Each option provides benefits and potential drawbacks.
User Stages
Each user has a Snowflake stage allocated to them by default for storing files. This stage is a con-venient option if your files will only be accessed by a single user, but need to be copied into multi-ple tables.
User stages have the following characteristics and limitations:
User stages are referenced using @~; e.g. use LIST @~ to list the files in a user stage.
Unlike named stages, user stages cannot be altered or dropped.
User stages do not support setting file format options. Instead, you must specify file format and copy options as part of the COPY INTO <table> command.
This option is not appropriate if:
Multiple users require access to the files.
The current user does not have INSERT privileges on the tables the data will be loaded into.
Table Stages
Each table has a Snowflake stage allocated to it by default for storing files. This stage is a conven-ient option if your files need to be accessible to multiple users and only need to be copied into a sin-gle table.
Table stages have the following characteristics and limitations:
Table stages have the same name as the table; e.g. a table named mytable has a stage referenced as
@%mytable.
Unlike named stages, table stages cannot be altered or dropped.
Table stages do not support transforming data while loading it (i.e. using a query as the source for the COPY command).
Note that a table stage is not a separate database object; rather, it is an implicit stage tied to the table itself. A table stage has no grantable privileges of its own. To stage files to a table stage, list the files, query them on the stage, or drop them, you must be the table owner (have the role with the OWNERSHIP privilege on the table).
This option is not appropriate if you need to copy the data in the files into multiple tables.
Named Stages
Named stages are database objects that provide the greatest degree of flexibility for data loading:
Users with the appropriate privileges on the stage can load data into any table.
Because the stage is a database object, the security/access rules that apply to all objects apply. The privileges to use a stage can be granted or revoked from roles. In addition, ownership of the stage can be transferred to another role.
If you plan to stage data files that will be loaded only by you, or will be loaded only into a single table, then you may prefer to simply use either your user stage or the stage for the table into which you will be loading data.
Named stages are optional but recommended when you plan regular data loads that could involve multiple users and/or tables.
NEW QUESTION # 104
Within a Snowflake account permissions have been defined with custom roles and role hierarchies.
To set up column-level masking using a role in the hierarchy of the current user, what command would be used?
Answer: C
Explanation:
Explanation
The IS_ROLE_IN_SESSION function is used to set up column-level masking using a role in the hierarchy of the current user. Column-level masking is a feature in Snowflake that allows users to apply dynamic data masking policies to specific columns based on the roles of the users who access them. The IS_ROLE_IN_SESSION function takes a role name as an argument and returns true if the role is in the current user's session, or false otherwise. The function can be used in a masking policy expression to determine whether to mask or unmask a column value based on the role of the user. For example:
CREATE OR REPLACE MASKING POLICY email_mask AS (val string) RETURNS string -> CASE WHEN IS_ROLE_IN_SESSION('HR') THEN val ELSE REGEXP_REPLACE(val, '(.).(.@.)', '\1****\2') END; In this example, the IS_ROLE_IN_SESSION function is used to create a masking policy for an email column.
The masking policy returns the original email value if the user has the HR role in their session, or returns a masked email value with asterisks if not.
NEW QUESTION # 105
A company stores sensitive data in an Amazon Redshift table. The company needs to give specific users the ability to access the sensitive data. The company must not create duplication in the data.
Customer support users must be able to see the last four characters of the sensitive data. Audit users must be able to see the full value of the sensitive data. No other users can have the ability to access the sensitive information.
Which solution will meet these requirements?
Answer: C
Explanation:
Amazon Redshift's dynamic data masking lets you define masking policies on sensitive columns without duplicating data. You attach a masking policy that exposes only the last four characters to customer support IAM roles, while audit IAM roles see the full values. This meets the requirement for role-based column-level access with no data duplication.
NEW QUESTION # 106
A data engineer is building an automated extract, transform, and load (ETL) ingestion pipeline by using AWS Glue. The pipeline ingests compressed files that are in an Amazon S3 bucket. The ingestion pipeline must support incremental data processing.
Which AWS Glue feature should the data engineer use to meet this requirement?
Answer: B
Explanation:
https://docs.aws.amazon.com/glue/latest/dg/monitor-continuations.html
NEW QUESTION # 107
A hotel management company receives daily data files from each of its hotels. The company wants to upload its data to AWS. The company plans to use Amazon Athena to access the files.
The company needs to protect the files from accidental deletion. The company will develop an application on its on-premises servers to automatically forward the files to a fully managed AWS ingestion service.
Which solution will meet these requirements with the LEAST operational overhead?
Answer: A
Explanation:
Kinesis Agent on-prem sends files to fully managed Kinesis Data Firehose, which delivers them to Amazon S3. Enabling S3 versioning protects against accidental deletions, and Athena can query directly from S3 - meeting requirements with minimal operational overhead.
NEW QUESTION # 108
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