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Snowflake DEA-C01 Exam Syllabus Topics:

Topic	Details
Topic 1	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 2	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 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 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.

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Snowflake SnowPro Advanced: Data Engineer Certification Exam Sample Questions (Q194-Q199):

NEW QUESTION # 194
As a skilled data engineer, you have been tasked with managing the Spark ETL jobs that run on the EMR and store incremental data in S3. However, you have noticed that some jobs are failing, and you are eager to take proactive steps to troubleshoot the issue. What would be the primary course of action to resolve this problem and ensure the smooth operation of the ETL process?

A. Increase the number of EC2 instances in the cluster.
B. Restart the EMR cluster.
C. Check the available disk space on the HDFS cluster.
D. Review the Spark job logs for errors or warnings.

Answer: D

Explanation:
Reviewing Spark job logs is the first step to identify errors, warnings, or slow stages that indicate the root cause of the performance issue.

NEW QUESTION # 195
A data engineer at a large company needs to create centralized datasets that are optimized for Amazon Redshift performance. The company has multiple downstream teams that use their own AWS accounts and dedicated Amazon Redshift clusters with RA3 nodes. All downstream teams need access to the centralized datasets. Which solution will provide immediate access to the datasets and maintain the current Amazon Redshift performance?

A. Set up Amazon Redshift data sharing between the Amazon Redshift producer clusters and the consumer clusters to provide access to the centralized datasets.
B. Copy the datasets to an Amazon S3 bucket by using the UNLOAD command. Register the table definitions in a dedicated AWS Glue Data Catalog schema. Share the schema with the other AWS accounts by using AWS Lake Formation. Use Amazon Redshift Spectrum to access the data.
C. Set up an AWS DataSync job that automatically syncs the data between the Amazon Redshift producer clusters and the consumer clusters.
D. Create a daily extract, transform, and load (ETL) job to unload the data to an Amazon S3 staging area. Instruct the teams to copy the data into their Amazon Redshift clusters.

Answer: A

Explanation:
Amazon Redshift data sharing allows multiple Redshift clusters (including across AWS accounts) to access centralized datasets in real time without copying or moving data. This provides immediate access while maintaining performance because queries run directly on the producer cluster's managed storage, eliminating duplication and ETL overhead.

NEW QUESTION # 196
Data engineer designed the data pipelines using Snowpipe to load data files into Snowflake tables, what will happen in case few files with same name but modified data are queued for reloading?

A. Snowpipe uses file loading metadata associated with each table object, so no metadata available to prevent duplication.
B. Data will be reloaded as files are modified & its associated metadata also changed. But Snowflake handle implicitly deduplication.
C. eTAG is changed for Files even they are having same name, so data will be duplicated in SnowFlake tables.
D. Snowpipe uses file loading metadata associated with each pipe object to prevent reload-ing the same files (and duplicating data) in a table.

Answer: D

Explanation:
Explanation
Snowflake uses file loading metadata to prevent reloading the same files (and duplicating data) in a table.
Snowpipe prevents loading files with the same name even if they were later modified (i.e. have a different eTag).
The file loading metadata is associated with the pipe object rather than the table. As a result:
Staged files with the same name as files that were already loaded are ignored, even if they have been modified, e.g. if new rows were added or errors in the file were corrected.
Truncating the table using the TRUNCATE TABLE command does not delete the Snowpipe file loading metadata.

NEW QUESTION # 197
A data engineer creates an AWS Lambda function that an Amazon EventBridge event will invoke.
When the data engineer tries to invoke the Lambda function by using an EventBridge event, an AccessDeniedException message appears.
How should the data engineer resolve the exception?

A. Ensure that EventBridge schemas are valid and that the event mapping configuration is correct.
B. Ensure that the trust policy of the Lambda function execution role allows EventBridge to assume the execution role.
C. Ensure that both the IAM role that EventBridge uses and the Lambda function's resource-based policy have the necessary permissions.
D. Ensure that the subnet where the Lambda function is deployed is configured to be a private subnet.

Answer: C

Explanation:
The lambda resource based policy must allow the events principle to invoke the lambda function.
Amazon SQS, Amazon SNS, Lambda, CloudWatch Logs, and EventBridge bus targets do not use roles, and permissions to EventBridge must be granted via a resource policy.
https://docs.aws.amazon.com/eventbridge/latest/userguide/eb-run-lambda-schedule.html#eb- schedule-create-rule
https://docs.aws.amazon.com/eventbridge/latest/userguide/eb-run-lambda-schedule.html#eb- schedule-create-rule

NEW QUESTION # 198
A company has a data processing pipeline that includes several dozen steps. The data processing pipeline needs to send alerts in real time when a step fails or succeeds. The data processing pipeline uses a combination of Amazon S3 buckets, AWS Lambda functions, and AWS Step Functions state machines.
A data engineer needs to create a solution to monitor the entire pipeline.
Which solution will meet these requirements?

A. Configure an Amazon EventBridge rule to react when the execution status of a state machine changes. Configure the rule to send a message to an Amazon Simple Notification Service (Amazon SNS) topic that sends notifications.
B. figure the AWS Lambda functions to store notifications in an Amazon S3 bucket when the state machines finish running. Enable S3 event notifications on the S3 bucket.
C. Use AWS CloudTrail to send a message to an Amazon Simple Notification Service (Amazon SNS) topic that sends notifications when a state machine fails to run or succeeds to run.
D. Configure the Step Functions state machines to store notifications in an Amazon S3 bucket when the state machines finish running. Enable S3 event notifications on the S3 bucket.

Answer: A

Explanation:
Amazon EventBridge can monitor the execution status of AWS Step Functions state machines and trigger specific actions based on state changes, such as when a state machine succeeds or fails. By configuring an EventBridge rule to capture these execution status changes and forward the details to an Amazon SNS topic, real-time notifications can be sent to alert users. This solution ensures a scalable and event-driven approach to monitoring the entire data processing pipeline in real time.

NEW QUESTION # 199
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