AWS Glue 101 UPDATED ACTUAL Exam Questions and
CORRECT Answers
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? -
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? - 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.
Contact AWS support: If you have exhausted all other troubleshooting options, contact AWS
support for further assistance. AWS support can help diagnose and resolve any
What are options for worker type and worker numbers for an aws glue job? - Standard Worker
Type(most+gen), G.1X Worker Type (CPU), G.2X Worker Type(highest CPU), #workers:2-1K
AWS Glue provides several options for worker type and worker numbers for a Glue job. The
worker type and number you choose will depend on the size and complexity of your dataset, the
type of transformation you are performing, and your budget. Here are the available worker types
and their associated configurations:
Standard Worker Type: This worker type is suitable for most Glue jobs, with 4 vCPU and 16 GB
of memory. You can choose a minimum of 2 and a maximum of 1000 instances for the job.
G.1X Worker Type: This worker type provides a higher CPU-to-memory ratio for jobs that
require more computation power. It has 8 vCPU and 32 GB of memory. You can choose a
minimum of 2 and a maximum of 1000 instances for the job.
G.2X Worker Type: This worker type provides the highest CPU-to-memory ratio for jobs that
require maximum computation power. It has 16 vCPU and 64 GB of memory. You can choose a
minimum of 2 and a maximum of 100 instances for the job.
In addition to worker type, you can also choose the number of workers for your job. The number
of workers can significantly impact the performance of your job, with more workers resulting in
CORRECT Answers
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? -
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? - 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.
Contact AWS support: If you have exhausted all other troubleshooting options, contact AWS
support for further assistance. AWS support can help diagnose and resolve any
What are options for worker type and worker numbers for an aws glue job? - Standard Worker
Type(most+gen), G.1X Worker Type (CPU), G.2X Worker Type(highest CPU), #workers:2-1K
AWS Glue provides several options for worker type and worker numbers for a Glue job. The
worker type and number you choose will depend on the size and complexity of your dataset, the
type of transformation you are performing, and your budget. Here are the available worker types
and their associated configurations:
Standard Worker Type: This worker type is suitable for most Glue jobs, with 4 vCPU and 16 GB
of memory. You can choose a minimum of 2 and a maximum of 1000 instances for the job.
G.1X Worker Type: This worker type provides a higher CPU-to-memory ratio for jobs that
require more computation power. It has 8 vCPU and 32 GB of memory. You can choose a
minimum of 2 and a maximum of 1000 instances for the job.
G.2X Worker Type: This worker type provides the highest CPU-to-memory ratio for jobs that
require maximum computation power. It has 16 vCPU and 64 GB of memory. You can choose a
minimum of 2 and a maximum of 100 instances for the job.
In addition to worker type, you can also choose the number of workers for your job. The number
of workers can significantly impact the performance of your job, with more workers resulting in
