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2025 Snowflake First-grade DEA-C02: Exam SnowPro Advanced: Data Engineer (DEA-C02) Simulator Online

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Snowflake SnowPro Advanced: Data Engineer (DEA-C02) Sample Questions (Q216-Q221):

NEW QUESTION # 216
A data engineering team is implementing Row Access Policies (RAP) on a table 'employee_data' containing sensitive salary information. They need to ensure that only managers can see the salary information of their direct reports. A user-defined function (UDF) 'GET returns a comma-separated string of manager usernames for a given username. Which of the following SQL statements correctly creates and applies a RAP to achieve this?

A. Option A
B. Option E
C. Option D
D. Option B
E. Option C

Answer: C

Explanation:
Option D correctly uses EXISTS and SPLIT TO TABLE to check if the employee's username is present in the list of managers returned by the GET_MANAGERS UDE Options A, B, C, and E have logical errors in how they determine manager-employee relationships, or don't correctly handle the comma-separated string of managers returned by the UDF. Options A and C also use CURRENT ROLE() = 'MANAGER' which requires the user to explicitly set their role to 'MANAGER', which might not be practical.

NEW QUESTION # 217
You have implemented a Snowpipe using auto-ingest to load data from an AWS S3 bucket. The pipe is configured to load data into a table with a 'DATE column ('TRANSACTION DATE'). The data files in S3 contain a date field in the format 'YYYYMMDD'. Occasionally, you observe data loading failures in Snowpipe with the error message indicating an issue converting the string to a date. The 'FILE FORMAT' definition includes 'DATE FORMAT = 'YYYYMMDD''. Furthermore, you are also noticing that after a while, some files are not being ingested even though they are present in the S3 bucket. How to effectively diagnose and resolve these issues?

A. Snowflake's auto-ingest feature has limitations and may not be suitable for inconsistent data formats. Consider using the Snowpipe REST API to implement custom error handling and data validation logic. Monitor the Snowflake event queue to ensure events are being received.
B. The 'DATE FORMAT parameter is case-sensitive. Ensure it matches the case of the incoming data. Also, check the 'VALIDATION MODE and ERROR parameters to ensure error handling is appropriately configured for files with date format errors. For the files that are not ingested use 'SYSTEM$PIPE to find the cause of the issue.
C. The error could be due to invalid characters in the source data files. Implement data cleansing steps to remove invalid characters from the date fields before uploading to S3. For files not being ingested, check S3 event notifications for missing or failed events.
D. Verify that the 'DATE FORMAT is correct and that all files consistently adhere to this format. Check for corrupted files in S3 that may be preventing Snowpipe from processing subsequent files. Additionally, review the Snowpipe error notifications in Snowflake to identify the root cause of ingestion failures. Use 'SYSTEM$PIPE to troubleshoot the files not ingested
E. The issue may arise if the time zone of the Snowflake account does not match the time zone of your data in AWS S3. Try setting the 'TIMEZONE parameter in the FILE FORMAT definition. For files that are not being ingested, manually refresh the Snowpipe with 'ALTER PIPE ... REFRESH'.

Answer: B,D

Explanation:
Option A is partially correct as the validation _ mode parameter in file format needs to be reviewed, not only the casesensitivity for the date. Case sensitivity isn't strictly enforced for DATE FORMAT. Snowflake's documentation specifies the valid specifiers (YYYY, MM, DD, etc.) which are generally case-insensitive in this context.The 'VALIDATION MODE and 'ON ERROR parameters in the copy option are critical. Incorrect handling of files that fails can cause future file ingests to stop. Option E highlights the importance of verifying the data format consistency and checking for corrupted files. Corrupted files or files that do not adhere to the specified format can cause Snowpipe to fail and potentially stop processing further files. Option B is incorrect, while timezone mismatches can cause issues, they don't directly lead to data loading failures with format conversion if the format is wrong or if file validation caused the issue. Option C's suggestion of data cleansing is valid in general, but it addresses a different problem (data quality) than the specific error described in the question. Option D proposes switching to the REST API, which is an overkill for this scenario. The auto-ingest feature is suitable; the problem is likely with data format inconsistencies or error handling.

NEW QUESTION # 218
You are working with a directory table named associated with an external stage containing a large number of small JSON files. You need to process only the files containing specific sensor readings based on a substring match within their filenames (e.g., files containing 'temperature' in the filename). You also want to load these files into a Snowflake table 'sensor_readings. Consider performance and cost-effectiveness. Which of the following approaches is the MOST efficient and cost-effective to achieve this? Choose TWO options.

A. Use a Python UDF to iterate through the files listed in , filter based on filename, and then load each matching file individually using the Snowflake Python Connector.
B. Create a masking policy based on filenames to control which files users can see.
C. Load all files from the stage using 'COPY INTO' into a staging table, and then use a Snowflake task to filter and move the relevant records into the 'sensor_readingS table.
D. Create a view on top of the directory table that filters the 'relative_patW based on the substring match, and then use 'COPY INTO' with the 'FILES' parameter to load the filtered files.
E. Use 'COPY INTO' with the 'PATTERN' parameter, constructing a regular expression that includes the substring match against the filename obtained from the directory table's 'relative_path' column.

Answer: D,E

Explanation:
Options B and C are the most efficient and cost-effective. Option B (Create a view and use COPY INTO with FILES): Creating a view that filters the directory table allows you to isolate the relevant filenames. Then, using 'COPY INTO' with the 'FILES' parameter pointing to this filtered view directly instructs Snowflake to load only the specified files, minimizing unnecessary data processing. This is efficient as it leverages Snowflake's built-in capabilities. Option C (COPY INTO with the PATTERN parameter): The 'PATTERN' parameter within the 'COPY INTO' command allows you to specify a regular expression. By incorporating the substring match into this regular expression against the metadata$filename" , you can directly filter which files are loaded during the 'COPY INTO operation. This avoids loading irrelevant data and is generally more performant than iterating through files using a UDF. Other options are less efficient or less cost-effective: Option A (Python UDF): Using a Python UDF for this task is generally less efficient. Snowflake is designed to handle this processing natively, and using UDF can lead to performance overhead due to data serialization and deserialization between Snowflake and the UDF environment. Option D (Load all and filter later): Loading all files into a staging table and then filtering is wasteful. It increases data processing time and costs since you're loading unnecessary data. It's always better to filter data closer to the source if possible. Option E (Masking Policy): Masking policies are for security, not data transformation. They are applied at the query level to prevent users from seeing data, but do not help in efficiently processing only specific files.

NEW QUESTION # 219
A data engineering team is using a Snowflake stream to capture changes made to a source table named 'orders'. They want to only capture 'INSERT and 'UPDATE operations but exclude 'DELETE operations from being captured in the stream. Which of the following configurations will achieve this requirement? Assume the stream has already been created and is named 'orders_stream'.

A. Create a Snowflake task that periodically truncates the stream's metadata table, removing DELETE records.
B. Create a view on top of the base table that filters out deleted rows, and then create a stream on the view.
C. It's impossible to configure a stream to exclude specific DML operations. All changes are always tracked.
D. Use task and stream combination. In the task, create view using 'select from orders where metadata$isDelete = false' and create stream on that view.
E. Alter the stream using the 'HIDE_DELETES parameter: 'ALTER STREAM orders_stream SET HIDE_DELETES = TRUE;'

Answer: E

Explanation:
Snowflake streams can be configured to hide delete operations using the parameten Setting 'HIDE_DELETES = TRUE' will prevent delete operations from being exposed through the stream. Option A is incorrect as streams can be configured. Option B, while functional, adds an extra layer of complexity. Option D doesn't exist as a valid parameter for streams. Option E is a highly unconventional and unsupported approach.

NEW QUESTION # 220
You are developing a JavaScript UDF in Snowflake to perform complex data validation on incoming data'. The UDF needs to validate multiple fields against different criteria, including checking for null values, data type validation, and range checks. Furthermore, you need to return a JSON object containing the validation results for each field, indicating whether each field is valid or not and providing an error message if invalid. Which approach is the MOST efficient and maintainable way to structure your JavaScript UDF to achieve this?

A. Create separate JavaScript functions for each validation check (e.g., 'isNull', 'isValidType', 'isWithinRange'). Call these functions from the main UDF and aggregate the results into a JSON object.
B. Use a single, monolithic JavaScript function with nested if-else statements to handle all validation logic. Return a JSON string containing the validation results.
C. Define a JavaScript object containing validation rules and corresponding validation functions. Iterate through the object and apply the rules to the input data, collecting the validation results in a JSON object. This object is returned as a string.
D. Directly embed SQL queries within the JavaScript UDF to perform data validation checks using Snowflake's built-in functions. Return a JSON string containing the validation results.
E. Utilize a JavaScript library like Lodash or Underscore.js within the UDF to perform data manipulation and validation. Return a JSON string containing the validation results.

Answer: C

Explanation:
Option D provides the most maintainable and efficient approach. By defining validation rules and corresponding functions in a JavaScript object, you can easily add, modify, or remove validation rules without affecting the core logic of the UDF. This approach promotes code reusability and makes the UDF easier to understand and maintain. Options A leads to unmaintainable code. Options B can be better than A but is still less elegant than D. Option C, while potentially useful for certain tasks, adds unnecessary overhead. Option E is generally discouraged due to performance limitations and the complexity of embedding SQL within JavaScript.

NEW QUESTION # 221
......

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