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Snowflake SnowPro Advanced: Data Engineer (DEA-C02) Sample Questions (Q112-Q117):

NEW QUESTION # 112
You are tasked with implementing a projection policy in Snowflake to restrict access to certain columns of the 'EMPLOYEE table based on the user's role. The table contains columns like 'EMPLOYEE 'NAME, 'SALARY', and 'DEPARTMENT. Users with the 'HR MANAGER role should have access to all columns, while other users should only be able to see 'EMPLOYEE ID, 'NAME, and DEPARTMENT. The initial attempt to create the projection policy results in an error. What could be the reasons?

A. The user attempting to create the projection policy does not have the 'OWNERSHIP' privilege on the 'EMPLOYEE table.
B. Projection policies are not supported in Snowflake.
C. The EMPLOYEE table must have row-level security policies enabled before applying a projection policy.
D. Projection policies can only be applied at the database level, not at the table level.
E. The projection policy definition might contain syntax errors or reference non-existent roles or columns.

Answer: A,E

Explanation:
Options D and E are correct. To create a projection policy, the user needs the 'OWNERSHIP privilege on the table. A syntax error can cause an error. Option A is incorrect since projection policies are applied at the table or view level. Projection policies exist in Snowflake, and do not depend on Row Access Policies to be enabled so B and C are incorrect.

NEW QUESTION # 113
You are implementing a data pipeline to load data from AWS S3 into Snowflake. The source data consists of CSV files with a header row. Some of the CSV files have inconsistent data types in a specific column (e.g., sometimes an integer, sometimes a string). You want to use the 'COPY' command to load the data and handle these data type inconsistencies gracefully. Which of the following 'COPY' command options, used in conjunction, would BEST address this issue and avoid load failures? Assume the file format is already defined to specify CSV type, header skip, and field delimiter.

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

Answer: A

Explanation:
Option C using "ON ERROR = 'CONTINUE'" allows the copy operation to proceed even if some rows have data type conversion errors. Rows with errors will be skipped, but the load will not fail entirely. 'SKIP FILE (Options A, D, and E) would skip the entire file if any error is encountered, which is too aggressive. 'VALIDATION MODE = RETURN ALL ERRORS' (Option B and D) is useful for identifying errors, but doesn't change the behavior of the 'COPY' command itself regarding how to handle errors. We want to ingest the data gracefully, not just identify the issues.

NEW QUESTION # 114
You are tasked with setting up a Kafka Connector to ingest data into Snowflake. You need to ensure fault tolerance. Which of the following Kafka Connect configurations are essential for enabling fault tolerance and ensuring minimal data loss during connector failures? Select all that apply.

A. Set 'tasks.max' to a value greater than 1.
B. Configure 'errors.deadletterqueue.topic.name' to specify a Dead Letter Queue (DLQ) topic.
C. Configure 'errors.tolerance' to 'all'.
D. Utilize Snowflake's auto-ingest feature alongside the Kafka Connector.
E. Enable Kafka Connect's internal offset storage by configuring 'offset.storage.topic' and 'config.storage.topic'.

Answer: A,B,E

Explanation:
The correct answers are A, C, and E. A: Increasing 'tasks.max' allows the connector to run multiple tasks in parallel, enabling faster processing and improved fault tolerance as the workload is distributed. If one task fails, others continue to operate. C: Kafka Connect relies on internal topics Coffset.storage.topic' and 'config.storage.topic') to store connector configurations and offset information. This ensures that upon a connector restart or worker failure, the connector can resume from where it left off, preventing data loss and duplication. E: Configuring a Dead Letter Queue (DLQ) topic ('errors.deadletterqueue.topic.nam€) allows the connector to route records that cannot be processed due to errors to a separate topic. This ensures that problematic records do not halt the entire ingestion process and can be analyzed and reprocessed later. B is incorrect since will only provide details on errors in the log but will not process data and stop the connector. D utilizes Snowflake's auto-ingest, which is independent of the Kafka connector; hence, it will not influence its fault tolerance.

NEW QUESTION # 115

A. The Snowflake external function is not correctly parsing the JSON response from the Lambda function. Implement a wrapper function in Snowflake to parse the JSON and extract the discount value before returning it.
B. The 'RETURNS NULL ON NULL INPUT clause in the external function definition is causing the function to return NULL even when valid inputs are provided. Remove this clause.
C. The Lambda function is returning a string instead of a number. Modify the Lambda function to return the discount as a number (e.g., 'discount = 0.15' instead of 'discount = '0.15")
D. The data types in the Lambda function and Snowflake function definition do not match. Specifically, the Lambda function expects strings while Snowflake is sending numbers and vice versa. Modify the Lambda function to handle numeric inputs and ensure the Snowflake function definition aligns with the expected output data type (FLOAT).
E. The Lambda function returns the discount within a nested JSON structure Tdata': [[discount]]}'. The Snowflake function is not designed to handle this. The lambda function should return '{'data':

Answer: A

Explanation:
The most likely cause is (B). Snowflake expects the external function to return a single value directly convertible to the declared return type. The Lambda function is returning a JSON object that needs to be parsed. Snowflake needs a wrapper function to extract the numerical result from the json response. All other issues have been taken care of in the question and is not the cause of the problem.

NEW QUESTION # 116
You are developing a data pipeline that extracts data from an on-premise PostgreSQL database, transforms it, and loads it into Snowflake. You want to use the Snowflake Python connector in conjunction with a secure method for accessing the PostgreSQL database. Which of the following approaches provides the MOST secure and manageable way to handle the PostgreSQL connection credentials in your Python script when deploying to a production environment?

A. Store the PostgreSQL username and password in a dedicated secrets management service (e.g., AWS Secrets Manager, HashiCorp Vault, Azure Key Vault) and retrieve them in the Python script using the appropriate API.
B. Store the PostgreSQL username and password in a configuration file (e.g., JSON or YAML) and load the file in the Python script.
C. Hardcode the PostgreSQL username and password directly into the Python script.
D. Store the PostgreSQL username and password in environment variables and retrieve them in the Python script using 'os.environ'
E. Prompt the user for the PostgreSQL username and password each time the script is executed.

Answer: A

Explanation:
Option D, using a dedicated secrets management service, provides the most secure and manageable approach. Secrets management services are designed to securely store and manage sensitive information like database credentials. They offer features like encryption, access control, auditing, and versioning, making them the best choice for production environments. Option A is highly insecure. Options B and C are better than A but still less secure than using a secrets management service, as environment variables and configuration files can be accidentally exposed or committed to version control. Option E is impractical and insecure for automated pipelines.

NEW QUESTION # 117
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