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Snowflake DEA-C01 Exam Syllabus Topics:

Topic	Details
Topic 1	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 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	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 4	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 5	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.

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Snowflake SnowPro Advanced: Data Engineer Certification Exam Sample Questions (Q128-Q133):

NEW QUESTION # 128
A company uses AWS Glue jobs to implement several data pipelines. The pipelines are critical to the company.
The company needs to implement a monitoring mechanism that will alert stakeholders if the pipelines fail.
Which solution will meet these requirements with the LEAST operational overhead?

A. Create an Amazon EventBridge rule to match AWS Glue job failure events. Define an Amazon CloudWatch metric based on the EventBridge rule. Set up a CloudWatch alarm based on the metric to send notifications to an Amazon Simple Notification Service (Amazon SNS) topic.
B. Create an Amazon EventBridge rule to match AWS Glue job failure events. Configure the rule to target an AWS Lambda function to process events. Configure the function to send notifications to an Amazon Simple Notification Service (Amazon SNS) topic.
C. Configure an Amazon CloudWatch Logs log group for the AWS Glue jobs. Create an Amazon EventBridge rule to match new log creation events in the log group. Configure the rule to target an AWS Lambda function that reads the logs and sends notifications to an Amazon Simple Notification Service (Amazon SNS) topic if AWS Glue job failure logs are present.
D. Configure an Amazon CloudWatch Logs log group for the AWS Glue jobs. Create an Amazon EventBridge rule to match new log creation events in the log group. Configure the rule to send notifications to an Amazon Simple Notification Service (Amazon SNS) topic.

Answer: A

Explanation:
By creating an EventBridge rule that matches Glue job state-change events where the state is FAILED, you can emit a custom CloudWatch metric without writing any code. Then you define a CloudWatch alarm on that metric to notify an SNS topic upon breach. This approach requires no Lambda functions or log parsing (just the rule, the metric, and the alarm) minimizing operational overhead.

NEW QUESTION # 129
A data engineer is using an AWS Glue ETL job to remove outdated customer records from a table that contains customer account information. The data engineer is using the following SQL command to remove customers that exist in a table named monthly_accounts_update table from the customer accounts table:
MERGE INTO accounts t USING monthly_accounts_update s
ON t.customer = s.customer
WHEN MATCHED
THEN DELETE
What will happen when the data engineer runs the SQL command?

A. No records will be deleted because the command syntax is not valid in AWS Glue.
B. Only customer records that are present in both tables will be retained in the customer accounts table.
C. The monthly_accounts_update table will be deleted.
D. All customer records that exist in both the customer accounts table and the monthly_accounts_update table will be deleted from the accounts table.

Answer: D

Explanation:
That MERGE statement deletes every row in accounts where the customer value matches a row in monthly_accounts_update. All overlapping customer records will be removed from the accounts table.

NEW QUESTION # 130
You're working as a data engineer for a financial services company processing real-time stock market data feeds. This data includes stock prices, trading volumes, and market news. You need a robust infrastructure to capture, process, and analyze this real-time data stream to make informed investment decisions. Downtime or data loss in your streaming pipeline can have significant consequences. Which option ensures high availability and fault tolerance with minimal operational overhead?

A. Use Amazon MSK to create a managed Kafka cluster.
B. Implement a custom Kafka cluster on ECS with Fargate.
C. Set up a Kafka cluster on EC2 instances across multiple availability zones.
D. Use AWS Lambda to process streaming data and write it to an S3 bucket.

Answer: A

Explanation:
Amazon MSK (Managed Streaming for Apache Kafka) provides a fully managed Kafka service that ensures high availability and fault tolerance with minimal operational overhead.

NEW QUESTION # 131
Ryan, a Data Engineer, accidently drop the Share named SF_SHARE which results in immediate access revoke for all the consumers (i.e., accounts who have created a database from that SF_SHARE). What action he can take to recover the dropped Share?

A. A dropped share cannot be restored. The share must be created again using the CRE-ATE SHARE command and then configured using GRANT <privilege> ... TO SHARE and ALTER SHARE.
B. He can recreate a share with the same name as a previous share which does restore the databases created (by any consumers) from the share SF_SHARE.
C. Consumer accounts that have created databases from the share will still be able to que-ry these databases as Share is separate securable object & it's still accessible using time travel feature.
D. By Executing UNDROP command he could possibly recover the dropped Share SF_SHARE & its associated Databases for immediate consumer access.

Answer: A

Explanation:
Explanation
You can drop a share at any time using the DROP SHARE command.
Dropping a share instantly invalidates all databases created from the share by consumer accounts.
All queries and other operations performed on these databases will no longer work.
After dropping a share, you can recreate it with the same name;
however, this does not restore any of the databases created from the share by consumer accounts.
The recreated share is treated as a new share and all consumer accounts must create a new database from the new share.

NEW QUESTION # 132
A data engineer needs to use AWS Step Functions to design an orchestration workflow. The workflow must parallel process a large collection of data files and apply a specific transformation to each file.
Which Step Functions state should the data engineer use to meet these requirements?

A. Wait state
B. Map state
C. Parallel state
D. Choice state

Answer: B

Explanation:
Map state is designed precisely for the requirement described. It allows you to iterate over a collection of items, processing each item individually. The Map state can automatically manage the iteration and execute the specified transformation on each item in parallel, making it the perfect choice for parallel processing of a large collection of data files.

NEW QUESTION # 133
......

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