AWS Glue 101 UPDATED ACTUAL Exam
Questions and CORRECT Answers
How do you optimize glue jobs? - CORRECT ANSWER - column-level filtering, approp
worker-type,number, caching, pre-warming techniques, minimize data shuffling, optimize data
lake, monitor+tune job performance, use Glue with other AWS services!
There are several ways to optimize AWS Glue jobs to improve their performance and efficiency:
Use column-level filtering: When processing large datasets, use column-level filtering to reduce
the amount of data being processed. This can significantly improve job performance.
Select the appropriate worker type and number: AWS Glue allows you to select the worker type
and number of workers for your jobs. By selecting the appropriate worker type and number, you
can optimize job performance and reduce costs.
Use caching and pre-warming techniques: Caching and pre-warming techniques can improve job
performance by reducing the amount of time spent on data discovery and schema inference. This
can be particularly useful for jobs that process the same data repeatedly.
Minimize data shuffling: Data shuffling can be a significant bottleneck in ETL jobs. To minimize
shuffling, consider partitioning your data by key, using appropriate join types, and selecting the
appropriate number of partitions.
Optimize your data lake storage: Your data lake storage can significantly impact your job
performance. Ensure that your storage is optimized for your specific use case, and consider using
partitioning and compression techniques to improve performance.
Monitor and tune your job performance: AWS Glue provides detailed metrics and logs that can
help you monitor and tune your job performance. Use these tools to identify bottlenecks and
optimize your jobs for performance.
,Use AWS Glue with other AWS services: AWS Glue can be integrated with other AWS services,
such as Amazon S3, AWS Lambda, and Amazon EMR, to provide a more comprehensive and
scala
How do you choose appropriate worker type and number in an effort to optimize glue jobs? -
CORRECT ANSWER - dataset size==>approp worker type,number, job complexity,
budget, test+iterate, spot instances
Choosing the appropriate worker type and number is an important step in optimizing AWS Glue
jobs. Here are some guidelines to help you make the right choices:
Evaluate the size of your dataset: The size of your dataset can impact your choice of worker type
and number. If you are processing a large dataset, you may need a larger number of workers to
ensure that the job completes within a reasonable time frame.
Evaluate the complexity of your job: The complexity of your job can also impact your choice of
worker type and number. Jobs that require a lot of computation or network bandwidth may
require a more powerful worker type or a larger number of workers.
Determine your budget: The cost of your Glue job will depend on the worker type and number of
workers you choose. Determine your budget and choose a worker type and number that meets
your performance needs while staying within your budget.
Test and iterate: The best way to determine the optimal worker type and number is to test
different configurations and monitor their performance. Start with a small number of workers
and gradually increase until you find the optimal configuration.
Consider spot instances: AWS offers spot instances for Glue jobs at a significant cost savings
compared to on-demand instances. If your job can tolerate occasional interruptions, spot
instances may be a good choice.
Consult the AWS Glue documentation: The AWS Glue documentation provides guidance on
choosing the appropriate worker type and number for your specific use case. Review the
documentation to ensure you are making an informed decision.
, Overall, selecting the appropriate worker type and number can significantly impact the
performance and cost of your Glue job. By evaluating your dat
How do you troubleshoot a glue job that is randomly timing out? - CORRECT ANSWER -
job metrics, worker logs, Glue connection, Glue job configuration, monitor Glue job execution,
retries+error handling
If a Glue job is randomly timing out, there could be a number of factors contributing to the issue.
Here are some steps you can take to troubleshoot and resolve the issue:
Check job metrics: Use the job metrics provided by AWS Glue to determine whether there are
any obvious issues with the job. Check the number of records processed, job duration, and any
errors or warnings.
Check worker logs: AWS Glue provides detailed logs for each worker in the job. Review these
logs to determine if any workers are experiencing errors or failures. If a specific worker is
experiencing issues, it could be a resource problem that requires increasing the worker type or
number.
Check the Glue connection to your data source: Ensure that the Glue connection to your data
source is stable and not experiencing any issues. Check the connectivity and latency of the data
source and ensure that there is enough bandwidth to support the job.
Check the Glue job configuration: Review the configuration of the Glue job to ensure that it is
set up correctly. Check the job script, connection, schema, partitioning, and other settings to
ensure that they are optimized for the job.
Monitor Glue job execution: Use AWS CloudWatch logs to monitor the Glue job execution in
real-time. This can help you identify any bottlenecks or performance issues that may be causing
the job to time out.
Use retries and error handling: Configure your Glue job to handle errors and retries. This can
help ensure that the job continues to execute even if there are occasional errors or timeouts.
Questions and CORRECT Answers
How do you optimize glue jobs? - CORRECT ANSWER - column-level filtering, approp
worker-type,number, caching, pre-warming techniques, minimize data shuffling, optimize data
lake, monitor+tune job performance, use Glue with other AWS services!
There are several ways to optimize AWS Glue jobs to improve their performance and efficiency:
Use column-level filtering: When processing large datasets, use column-level filtering to reduce
the amount of data being processed. This can significantly improve job performance.
Select the appropriate worker type and number: AWS Glue allows you to select the worker type
and number of workers for your jobs. By selecting the appropriate worker type and number, you
can optimize job performance and reduce costs.
Use caching and pre-warming techniques: Caching and pre-warming techniques can improve job
performance by reducing the amount of time spent on data discovery and schema inference. This
can be particularly useful for jobs that process the same data repeatedly.
Minimize data shuffling: Data shuffling can be a significant bottleneck in ETL jobs. To minimize
shuffling, consider partitioning your data by key, using appropriate join types, and selecting the
appropriate number of partitions.
Optimize your data lake storage: Your data lake storage can significantly impact your job
performance. Ensure that your storage is optimized for your specific use case, and consider using
partitioning and compression techniques to improve performance.
Monitor and tune your job performance: AWS Glue provides detailed metrics and logs that can
help you monitor and tune your job performance. Use these tools to identify bottlenecks and
optimize your jobs for performance.
,Use AWS Glue with other AWS services: AWS Glue can be integrated with other AWS services,
such as Amazon S3, AWS Lambda, and Amazon EMR, to provide a more comprehensive and
scala
How do you choose appropriate worker type and number in an effort to optimize glue jobs? -
CORRECT ANSWER - dataset size==>approp worker type,number, job complexity,
budget, test+iterate, spot instances
Choosing the appropriate worker type and number is an important step in optimizing AWS Glue
jobs. Here are some guidelines to help you make the right choices:
Evaluate the size of your dataset: The size of your dataset can impact your choice of worker type
and number. If you are processing a large dataset, you may need a larger number of workers to
ensure that the job completes within a reasonable time frame.
Evaluate the complexity of your job: The complexity of your job can also impact your choice of
worker type and number. Jobs that require a lot of computation or network bandwidth may
require a more powerful worker type or a larger number of workers.
Determine your budget: The cost of your Glue job will depend on the worker type and number of
workers you choose. Determine your budget and choose a worker type and number that meets
your performance needs while staying within your budget.
Test and iterate: The best way to determine the optimal worker type and number is to test
different configurations and monitor their performance. Start with a small number of workers
and gradually increase until you find the optimal configuration.
Consider spot instances: AWS offers spot instances for Glue jobs at a significant cost savings
compared to on-demand instances. If your job can tolerate occasional interruptions, spot
instances may be a good choice.
Consult the AWS Glue documentation: The AWS Glue documentation provides guidance on
choosing the appropriate worker type and number for your specific use case. Review the
documentation to ensure you are making an informed decision.
, Overall, selecting the appropriate worker type and number can significantly impact the
performance and cost of your Glue job. By evaluating your dat
How do you troubleshoot a glue job that is randomly timing out? - CORRECT ANSWER -
job metrics, worker logs, Glue connection, Glue job configuration, monitor Glue job execution,
retries+error handling
If a Glue job is randomly timing out, there could be a number of factors contributing to the issue.
Here are some steps you can take to troubleshoot and resolve the issue:
Check job metrics: Use the job metrics provided by AWS Glue to determine whether there are
any obvious issues with the job. Check the number of records processed, job duration, and any
errors or warnings.
Check worker logs: AWS Glue provides detailed logs for each worker in the job. Review these
logs to determine if any workers are experiencing errors or failures. If a specific worker is
experiencing issues, it could be a resource problem that requires increasing the worker type or
number.
Check the Glue connection to your data source: Ensure that the Glue connection to your data
source is stable and not experiencing any issues. Check the connectivity and latency of the data
source and ensure that there is enough bandwidth to support the job.
Check the Glue job configuration: Review the configuration of the Glue job to ensure that it is
set up correctly. Check the job script, connection, schema, partitioning, and other settings to
ensure that they are optimized for the job.
Monitor Glue job execution: Use AWS CloudWatch logs to monitor the Glue job execution in
real-time. This can help you identify any bottlenecks or performance issues that may be causing
the job to time out.
Use retries and error handling: Configure your Glue job to handle errors and retries. This can
help ensure that the job continues to execute even if there are occasional errors or timeouts.
