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Snowflake DEA-C01 Exam Syllabus Topics:

Topic	Details
Topic 1	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.
Topic 2	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 3	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 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 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.

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Snowflake SnowPro Advanced: Data Engineer Certification Exam Sample Questions (Q75-Q80):

NEW QUESTION # 75
An application uses an AWS Lambda function that is configured with managed runtimes. The Lambda function successfully writes logs to the default Amazon CloudWatch Logs log group. A data engineer wants to modify the logging behavior to show only ERROR level logs for application logs and WARN level logs for system logs. Which solution will meet these requirements?

A. Configure the Lambda function to send logs to a custom log group.
B. Configure the Lambda function to use the JSON log format.
C. Set the log level to ERROR in the Lambda function code.
D. Add additional permissions to the Lambda execution role.

Answer: B

Explanation:
AWS Lambda supports configuring advanced logging controls for managed runtimes when using structured JSON log format. This allows separate log level settings for application logs and system logs, enabling ERROR level for application logs and WARN level for system logs without modifying permissions or log destinations.

NEW QUESTION # 76
Snowflake supports using key pair authentication for enhanced authentication security as an alterna-tive to basic authentication (i.e. username and password). Select the list of SnowFlake Clients sup-port the same?
[Select All that Apply]

A. Go Driver
B. SnowCD
C. SnowFlake Connector for Spark
D. Node.js
E. SnowSQL

Answer: A,C,D,E

NEW QUESTION # 77
A company receives a data file from a partner each day in an Amazon S3 bucket. The company uses a daily AWS Glue extract, transform, and load (ETL) pipeline to clean and transform each data file. The output of the ETL pipeline is written to a CSV file named Daily.csv in a second S3 bucket.
Occasionally, the daily data file is empty or is missing values for required fields. When the file is missing data, the company can use the previous day's CSV file.
A data engineer needs to ensure that the previous day's data file is overwritten only if the new daily file is complete and valid.
Which solution will meet these requirements with the LEAST effort?

A. Run a SQL query in Amazon Athena to read the CSV file and drop missing rows. Copy the corrected CSV file to the second S3 bucket.
B. Use AWS Glue Studio to change the code in the ETL pipeline to fill in any missing values in the required fields with the most common values for each field.
C. Invoke an AWS Lambda function to check the file for missing data and to fill in missing values in required fields.
D. Configure the AWS Glue ETL pipeline to use AWS Glue Data Quality rules. Develop rules in Data Quality Definition Language (DQDL) to check for missing values in required fields and empty files.

Answer: D

Explanation:
AWS Glue Data Quality rules can be integrated directly into the AWS Glue ETL pipeline to automatically check for missing or invalid data in the daily file. You can use Data Quality Definition Language (DQDL) to define rules that verify the completeness and validity of the data before the pipeline writes the output. This solution ensures that the previous day's file is only overwritten if the new data is complete and valid, minimizing manual intervention and operational effort.
While Lambda could be used, it requires additional development and integration outside of the existing Glue ETL pipeline. This adds complexity and operational overhead.
The "Use AWS Glue Studio to change the code in the ETL pipeline to fill in any missing values in the required fields with the most common values for each field" approach assumes that filling missing values with the most common ones is acceptable, but it might not always be the correct solution for all cases. The requirement specifies that the file should be validated and replaced only if it's complete and valid.
Using Athena to drop missing rows doesn't fully address the problem, as it could result in loss of data and still requires manual intervention to validate the file before copying it. Additionally, it introduces unnecessary steps compared to using built-in AWS Glue Data Quality capabilities.

NEW QUESTION # 78
Mark the Correct Statements for the VALIDATION_MODE option used by Data Engineer for Da-ta loading operations in his/her COPY INTO <table> command:

A. VALIDATION_MODE only support Data loading operation i.e., do not work while da-ta unloading.
B. VALIDATION_MODE instructs the COPY command to validate the data files instead of loading them into the specified table; i.e., the COPY command tests the files for er-rors but does not load them.
C. VALIDATION_MODE does not support COPY statements that transform data during a load. If the parameter is specified, the COPY statement returns an error.
D. VALIDATION_MODE option supported these values:
RETURN_n_ROWS,
RETURN_ERRORS,
RETURN_ALL_ERRORS

Answer: B,C,D

Explanation:
Explanation
All the Statements are correct except the statement saying VALIDATION_MODE only support Data loading operation.
VALIDATION_MODE can be used with COPY INTO <location> command as well i.e for data unloading operation.
VALIDATION_MODE = RETURN_ROWS can be used at the time of Data unloading.
This option instructs the COPY command to return the results of the query in the SQL statement instead of unloading the results to the specified cloud storage location. The only supported valida-tion option is RETURN_ROWS. This option returns all rows produced by the query.
When you have validated the query, you can remove the VALIDATION_MODE to perform the unload operation.

NEW QUESTION # 79
A legal company is building a data pipeline to power an application that will handle peak traffic during business hours. The application will provide information about relevant laws and available lawyers. The legal document database will be updated one time each day.
The application must display up-to-date lawyer availability from a calendar database and provide complex full-text search of legal documents. The company wants to use AWS Glue for extract, transform, and load (ETL) processes. Lawyer availability information must be current within 5 minutes of any schedule changes.
Which solution will meet these requirements with the LEAST operational overhead?

A. Use AWS Glue workflows with time-based triggers and event-based triggers. Store processed data in Amazon DynamoDB. Create a custom search solution by using AWS Lambda functions.
B. Use Amazon EventBridge to schedule all AWS Glue jobs. Store processed data in Amazon RDS.Use Amazon Kendra for full-text search capabilities.
C. Use AWS Step Functions to orchestrate AWS Glue jobs with time-based triggers and event-based triggers. Store processed data in Amazon S3. Use Amazon RDS for the search functionality.
D. Use AWS Step Functions to orchestrate AWS Glue jobs with time-based triggers and event-based triggers. Store processed data in Amazon S3. Use Amazon OpenSearch Service for full-text search capabilities.

Answer: D

Explanation:
Amazon OpenSearch Service is the AWS service in these choices that is purpose-built for complex full-text search of legal documents. AWS Glue can run ETL jobs, and AWS Step Functions can orchestrate both time-based and event-based processing so the daily legal document refresh and the frequent lawyer availability updates can be handled in one workflow pattern. This lets the company keep lawyer availability current within the required 5-minute window while using a managed search service and avoiding custom search implementation.

NEW QUESTION # 80
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