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Snowflake DEA-C01 Exam Questions
- Snowflake SnowPro Certifications
- Snowflake SnowPro Advanced Certifications
- Topic 1: Data Movement: Snowflake Data Engineers and Software Engineers are assessed on their proficiency to load, ingest, and troubleshoot data in Snowflake. It evaluates skills in building continuous data pipelines, configuring connectors, and designing data sharing solutions.
- Topic 2: Performance Optimization: This topic assesses the ability to optimize and troubleshoot underperforming queries in Snowflake. Candidates must demonstrate knowledge in configuring optimal solutions, utilizing caching, and monitoring data pipelines. It focuses on ensuring engineers can enhance performance based on specific scenarios, crucial for Snowflake Data Engineers and Software Engineers.
- Topic 3: Storage and Data Protection: The topic tests the implementation of data recovery features and the understanding of Snowflake's Time Travel and micro-partitions. Engineers are evaluated on their ability to create new environments through cloning and ensure data protection, highlighting essential skills for maintaining Snowflake data integrity and accessibility.
- Topic 4: Security: The Security topic of the DEA-C01 test covers the principles of Snowflake security, including the management of system roles and data governance. It measures the ability to secure data and ensure compliance with policies, crucial for maintaining secure data environments for Snowflake Data Engineers and Software Engineers.
- Topic 5: Data Transformation: The SnowPro Advanced: Data Engineer exam evaluates skills in using User-Defined Functions (UDFs), external functions, and stored procedures. It assesses the ability to handle semi-structured data and utilize Snowpark for transformations. This section ensures Snowflake engineers can effectively transform data within Snowflake environments, critical for data manipulation tasks.
Free Snowflake DEA-C01 Exam Actual Questions
Note: Premium Questions for DEA-C01 were last updated On Jul. 01, 2025 (see below)
A new customer table is created by a data pipeline in a Snowflake schema where MANAGED ACCESS enabled.
.... Can gran access to the CUSTOMER table? (Select THREE.)
The roles that can grant access to the CUSTOMER table are the role that owns the schema, the role that owns the database, and the SECURITYADMIN role. These roles have the ownership or the manage grants privilege on the schema or the database level, which allows them to grant access to any object within them. The other options are incorrect because they do not have the necessary privilege to grant access to the CUSTOMER table. Option C is incorrect because the role that owns the customer table cannot grant access to itself or to other roles. Option D is incorrect because the SYSADMIN role does not have the manage grants privilege by default and cannot grant access to objects that it does not own. Option F is incorrect because the USERADMIN role with the manage grants privilege can only grant access to users and roles, not to tables.
Which methods will trigger an action that will evaluate a DataFrame? (Select TWO)
The methods that will trigger an action that will evaluate a DataFrame are DataFrame.collect() and DataFrame.show(). These methods will force the execution of any pending transformations on the DataFrame and return or display the results. The other options are not methods that will evaluate a DataFrame. Option A, DataFrame.random_split(), is a method that will split a DataFrame into two or more DataFrames based on random weights. Option C, DataFrame.select(), is a method that will project a set of expressions on a DataFrame and return a new DataFrame. Option D, DataFrame.col(), is a method that will return a Column object based on a column name in a DataFrame.
What is a characteristic of the use of external tokenization?
External tokenization is a feature in Snowflake that allows users to replace sensitive data values with tokens that are generated and managed by an external service. External tokenization allows the preservation of analytical values after de-identification, such as preserving the format, length, or range of the original values. This way, users can perform analytics on the tokenized data without compromising the security or privacy of the sensitive data.
A CSV file around 1 TB in size is generated daily on an on-premise server A corresponding table. Internal stage, and file format have already been created in Snowflake to facilitate the data loading process
How can the process of bringing the CSV file into Snowflake be automated using the LEAST amount of operational overhead?
This option is the best way to automate the process of bringing the CSV file into Snowflake with the least amount of operational overhead. SnowSQL is a command-line tool that can be used to execute SQL statements and scripts on Snowflake. By scheduling a SQL file that executes a PUT command, the CSV file can be pushed from the on-premise server to the internal stage in Snowflake. Then, by creating a pipe that runs a COPY INTO statement that references the internal stage, Snowpipe can automatically load the file from the internal stage into the table when it detects a new file in the stage. This way, there is no need to manually start or monitor a virtual warehouse or task.
A Data Engineer defines the following masking policy:
....
must be applied to the full_name column in the customer table:
Which query will apply the masking policy on the full_name column?
The query that will apply the masking policy on the full_name column is ALTER TABLE customer MODIFY COLUMN full_name SET MASKING POLICY name_policy;. This query will modify the full_name column and associate it with the name_policy masking policy, which will mask the first and last names of the customers with asterisks. The other options are incorrect because they do not follow the correct syntax for applying a masking policy on a column. Option B is incorrect because it uses ADD instead of SET, which is not a valid keyword for modifying a column. Option C is incorrect because it tries to apply the masking policy on two columns, first_name and last_name, which are not part of the table structure. Option D is incorrect because it uses commas instead of dots to separate the database, schema, and table names
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