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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	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 4	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 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 (Q359-Q364):

NEW QUESTION # 359
A data engineer is using an Apache Iceberg framework to build a data lake that contains 100 TB of data. The data engineer wants to run AWS Glue Apache Spark jobs that use the Iceberg framework.
What combination of steps will meet these requirements? (Choose two.)

A. Create a key named --conf for an AWS Glue job. Set Iceberg as a value for the --datalake-formats job parameter.
B. Add the --job-bookmark-option: job-bookmark-enable parameter to an AWS Glue job.
C. Set the --enable-auto-scaling parameter to true.
D. Specify the path to a specific version of Iceberg by using the -extra-jars job parameter. Set Iceberg as a value for the datalake-formats job parameter.
E. Set Iceberg as a value for the --datalake-formats job parameter.

Answer: D,E

Explanation:
To run AWS Glue Apache Spark jobs with the Apache Iceberg framework, you must:
Set --datalake-formats to iceberg so Glue knows to use the Iceberg format.
Specify the Iceberg library by using the --extra-jars parameter to include the appropriate Iceberg JAR files for Glue to access the Iceberg table format.

NEW QUESTION # 360
A data engineer needs a fully automated solution to check for new data in multiple databases and process data that the solution finds. The solution must run every hour. The solution must be compatible with Amazon RDS, Amazon DynamoDB, and Amazon OpenSearch Service. The solution must be able to process up to 10 MB of data at one time. The solution must be optimized for costs and operational overhead. The solution must have robust error handling capabilities.
Which solution will meet these requirements?

A. Configure an Apache Spark application to run on Amazon EMR to check for data. Implement error handling in the application. Use Amazon EventBridge to invoke the application every hour.
B. Use Amazon EventBridge to invoke an AWS Lambda function every hour to check for data.
Configure the function to send a message to an Amazon Simple Queue Service (Amazon SQS) queue when the function finds new data. Use a second Lambda function to read the queue and perform the processing.
C. Use Amazon EventBridge to invoke AWS Step Functions every hour to deploy an AWS Lambda function to check for data. Configure Step Functions steps to process data that the Lambda function finds. Implement error handling in each state.
D. Use Amazon Managed Workflows for Apache Airflow (Amazon MWAA) to create a workflow that runs a directed acyclic graph (DAG) every hour to check for data. Configure the DAG to process identified data. Implement error handling in a Python operator.

Answer: C

Explanation:
Amazon EventBridge can invoke AWS Step Functions on an hourly schedule, and Step Functions is a serverless orchestration service that is well suited for coordinating checks across multiple data sources while providing built-in retry and catch mechanisms for robust error handling. AWS Lambda can connect to Amazon RDS, Amazon DynamoDB, and Amazon OpenSearch Service through AWS SDKs, and Lambda can handle workloads well beyond 10 MB in memory, so this design meets the processing-size requirement with low cost and minimal operational overhead compared with EMR or MWAA.
Reference:
https://docs.aws.amazon.com/step-functions/latest/dg/tutorial-handling-error-conditions.html
https://docs.aws.amazon.com/opensearch-service/latest/developerguide/configuration- samples.html
https://docs.aws.amazon.com/lambda/latest/dg/configuration-memory.html
https://docs.aws.amazon.com/step-functions/latest/dg/workflow-studio-process-error.html

NEW QUESTION # 361
To troubleshoot data load failure in one of your Copy Statement, Data Engineer have Executed a COPY statement with the VALIDATION_MODE copy option set to RETURN_ALL_ERRORS with reference to the set of files he had attempted to load. Which below function can facilitate analysis of the problematic records on top of the Results produced? [Select 2]

A. Rejected_record
B. RESULT_SCAN
C. LOAD_ERROR
D. LAST_QUERY_ID

Answer: B,D

Explanation:
Explanation
LAST_QUERY_ID() Function
Returns the ID of a specified query in the current session. If no query is specified, the most recently executed query is returned.
RESULT_SCAN() Function
Returns the result set of a previous command (within 24 hours of when you executed the query) as if the result was a table.
The following example validates a set of files (SFfile.csv.gz) that contain errors. To facilitate analy-sis of the errors, a COPY INTO <location> statement then unloads the problematic records into a text file so they could be analyzed and fixed in the original data files. The statement queries the RESULT_SCAN table.
1.#copy into Snowtable
2.from @SFstage/SFfile.csv.gz
3.validation_mode=return_all_errors;
4.#set qid=last_query_id();
5.#copy into @SFstage/errors/load_errors.txt from (select rejected_record from ta-ble(result_scan($qid))); Note: Other options are not valid functions.

NEW QUESTION # 362
A company uses AWS Glue Apache Spark jobs to handle extract, transform, and load (ETL) workloads. The company has enabled logging and monitoring for all AWS Glue jobs.
One of the AWS Glue jobs begins to fail. A data engineer investigates the error and wants to examine metrics for all individual stages within the job.
How can the data engineer access the stage metrics?

A. Examine the AWS Glue job and stage details by using the run insights feature on the job.
B. Examine the AWS Glue job and stage details in the Spark UI.
C. Examine the AWS Glue job and stage metrics in Amazon CloudWatch.
D. Examine the AWS Glue job and stage logs in AWS CloudTrail logs.

Answer: B

Explanation:
AWS Glue provides a built-in Spark UI (accessible from the Glue console's job run details) that exposes per-stage metrics, such as task counts, shuffle read/write sizes, and execution times, for each Spark stage. This is the most direct way to drill down into the individual stages when troubleshooting a failing Glue Spark job.

NEW QUESTION # 363
A university is developing an educational application that analyzes student essays. The application provides personalized feedback with accurate citations to the university's textbooks.
The application needs to process essays in multiple languages. Application responses must include direct references to specific sections in the course materials and must be in the student's selected language. Which solution will meet these requirements with the LEAST operational overhead?

A. Create a knowledge base in Amazon Bedrock Knowledge Bases with the university's textbooks.
Configure a multilingual model to generate responses with source citations.
B. Use Amazon SageMaker to host a custom-trained large language model (LLM) that has been fine-tuned on the university's textbooks to generate personalized feedback with citations.
C. Build a custom vector database by using Amazon OpenSearch Serverless. Store textbook content as multilingual embeddings. Create an AWS Lambda function that queues the database when generating responses with Amazon Bedrock.
D. Use Amazon Comprehend to detect the language and key topics in the essays. Use Amazon Kendra to search for relevant textbook passages. Create an AWS Lambda function that formats the textbook passages into feedback.

Answer: A

Explanation:
Amazon Bedrock Knowledge Bases is the lowest-overhead solution because it provides a managed retrieval-augmented generation workflow over the university's textbooks and can return generated responses with citations to the source material. The RetrieveAndGenerate capability is designed to generate answers from retrieved textbook content and include source citations, which directly matches the requirement for accurate references to specific course materials. By choosing a multilingual model in Bedrock for generation, the application can respond in the student's selected language without building and operating a custom retrieval stack.
Reference:
https://docs.aws.amazon.com/bedrock/latest/userguide/knowledge-base.html
https://docs.aws.amazon.com/bedrock/latest/userguide/kb-test-retrieve-generate.html
https://docs.aws.amazon.com/bedrock/latest/userguide/kb-how-retrieval.html

NEW QUESTION # 364
......

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