DUMPS
BASE
EXAM DUMPS
AMAZON
MLA-C01
28% OFF Automatically For You
AWS Certified Machine Learning Engineer -
Associate
,1.You are a machine learning engineer at a fintech company tasked with developing
and deploying an end-to-end machine learning workflow for fraud detection. The
workflow involves multiple steps, including data extraction, preprocessing, feature
engineering, model training, hyperparameter tuning, and deployment. The company
requires the solution to be scalable, support complex dependencies between tasks,
and provide robust monitoring and versioning capabilities. Additionally, the workflow
needs to integrate seamlessly with existing AWS services.
Which deployment orchestrator is the MOST SUITABLE for managing and
automating your ML workflow?
A. Use AWS Step Functions to build a serverless workflow that integrates with
SageMaker for model training and deployment, ensuring scalability and fault tolerance
B. Use AWS Lambda functions to manually trigger each step of the ML workflow,
enabling flexible execution without needing a predefined orchestration tool
m
xa
C. Use Amazon SageMaker Pipelines to orchestrate the entire ML workflow,
E
01
leveraging its built-in integration with SageMaker features like training, tuning, and
-C
LA
deployment
M
e
th
D. Use Apache Airflow to define and manage the workflow with custom DAGs
r
fo
(Directed Acyclic
ed
ar
Graphs), integrating with AWS services through operators and hooks
p
re
P
Answer: C
lly
Fu
Explanation:
re
A
Correct option:
ou
Y
Use Amazon SageMaker Pipelines to orchestrate the entire ML workflow, leveraging
g
in
ur
its built-in integration with SageMaker features like training, tuning, and deployment
ns
-E
Amazon SageMaker Pipelines is a purpose-built workflow orchestration service to
)
02
automate machine learning (ML) development.
8.
(V
SageMaker Pipelines is specifically designed for orchestrating ML workflows. It
ps
um
provides native integration with SageMaker features like model training, tuning, and
D
01
deployment. It also supports versioning, lineage tracking, and automatic execution of
-C
LA
workflows, making it the ideal choice for managing end-to-end ML workflows in AWS.
M
on
via - https://docs.aws.amazon.com/sagemaker/latest/dg/pipelines.html
az
m
A
Incorrect options:
t
es
Use Apache Airflow to define and manage the workflow with custom DAGs (Directed
B
Acyclic Graphs), integrating with AWS services through operators and hooks -
Apache Airflow is a powerful orchestration tool that allows you to define complex
workflows using custom DAGs. However, it requires significant setup and
maintenance, and while it can integrate with AWS services, it does not provide the
seamless, built-in integration with SageMaker that SageMaker Pipelines offers.
Amazon Managed Workflows for Apache Airflow (Amazon MWAA):
,via - https://aws.amazon.com/managed-workflows-for-apache-airflow/
Use AWS Step Functions to build a serverless workflow that integrates with
m
SageMaker for model training and deployment, ensuring scalability and fault tolerance
xa
E
- AWS Step Functions is a serverless orchestration service that can integrate with
01
-C
SageMaker and other AWS services. However, it is more general-purpose and lacks
LA
M
some of the ML-specific features, such as model lineage tracking and hyperparameter
e
th
r
tuning, that are built into SageMaker Pipelines.
fo
ed
Use AWS Lambda functions to manually trigger each step of the ML workflow,
par
re
enabling flexible execution without needing a predefined orchestration tool - AWS
P
lly
Lambda is useful for triggering specific tasks, but manually managing each step of a
Fu
re
complex ML workflow without a comprehensive orchestration tool is not scalable or
A
ou
maintainable. It does not provide the task dependency management, monitoring, and
Y
g
in
versioning required for an end-to-end ML workflow.
ur
ns
References:
-E
)
https://docs.aws.amazon.com/sagemaker/latest/dg/pipelines.html
02
8.
(V
https://aws.amazon.com/managed-workflows-for-apache-airflow/
ps
um
D
01
-C
2.You are tasked with building a predictive model for customer lifetime value (CLV)
LA
M
using Amazon SageMaker. Given the complexity of the model, it’s crucial to optimize
on
az
hyperparameters to achieve the best possible performance. You decide to use
m
A
t
SageMaker’s automatic model tuning (hyperparameter optimization) with Random
es
B
Search strategy to fine-tune the model. You have a large dataset, and the tuning job
involves several hyperparameters, including the learning rate, batch size, and dropout
rate. During the tuning process, you observe that some of the trials are not
converging effectively, and the results are not as expected. You suspect that the
hyperparameter ranges or the strategy you are using may need adjustment.
Which of the following approaches is MOST LIKELY to improve the effectiveness of
the hyperparameter tuning process?
A. Decrease the number of total trials but increase the number of parallel jobs to
speed up the tuning process
B. Switch from the Random Search strategy to the Bayesian Optimization strategy
, and narrow the range of critical hyperparameters
C. Use the Grid Search strategy with a wide range for all hyperparameters and
increase the number of total trials
D. Increase the number of hyperparameters being tuned and widen the range for all
hyperparameters
Answer: B
Explanation:
Correct option:
Switch from the Random Search strategy to the Bayesian Optimization strategy and
narrow the range of critical hyperparameters
When you’re training machine learning models, each dataset and model needs a
different set of hyperparameters, which are a kind of variable. The only way to
determine these is through multiple experiments, where you pick a set of
m
xa
hyperparameters and run them through your model. This is called hyperparameter
E
01
tuning. In essence, you're training your model sequentially with different sets of
-C
LA
hyperparameters. This process can be manual, or you can pick one of several
M
e
th
automated hyperparameter tuning methods.
r
fo
Bayesian Optimization is a technique based on Bayes’ theorem, which describes the
ed
ar
probability of an event occurring related to current knowledge. When this is applied to
p
re
P
hyperparameter optimization, the algorithm builds a probabilistic model from a set of
lly
Fu
hyperparameters that optimizes a specific metric. It uses regression analysis to
re
A
iteratively choose the best set of hyperparameters.
ou
Y
Random Search selects groups of hyperparameters randomly on each iteration. It
g
in
ur
works well when a relatively small number of the hyperparameters primarily determine
ns
-E
the model outcome.
)
02
Bayesian Optimization is more efficient than Random Search for hyperparameter
8.
(V
tuning, especially when dealing with complex models and large hyperparameter
ps
um
spaces. It learns from previous trials to predict the best set of hyperparameters, thus
D
01
focusing the search more effectively. Narrowing the range of critical hyperparameters
-C
LA
can further improve the chances of finding the optimal values, leading to better model
M
on
convergence and performance.
az
m
A
How hyperparameter tuning with Amazon SageMaker works:
t
es
B
BASE
EXAM DUMPS
AMAZON
MLA-C01
28% OFF Automatically For You
AWS Certified Machine Learning Engineer -
Associate
,1.You are a machine learning engineer at a fintech company tasked with developing
and deploying an end-to-end machine learning workflow for fraud detection. The
workflow involves multiple steps, including data extraction, preprocessing, feature
engineering, model training, hyperparameter tuning, and deployment. The company
requires the solution to be scalable, support complex dependencies between tasks,
and provide robust monitoring and versioning capabilities. Additionally, the workflow
needs to integrate seamlessly with existing AWS services.
Which deployment orchestrator is the MOST SUITABLE for managing and
automating your ML workflow?
A. Use AWS Step Functions to build a serverless workflow that integrates with
SageMaker for model training and deployment, ensuring scalability and fault tolerance
B. Use AWS Lambda functions to manually trigger each step of the ML workflow,
enabling flexible execution without needing a predefined orchestration tool
m
xa
C. Use Amazon SageMaker Pipelines to orchestrate the entire ML workflow,
E
01
leveraging its built-in integration with SageMaker features like training, tuning, and
-C
LA
deployment
M
e
th
D. Use Apache Airflow to define and manage the workflow with custom DAGs
r
fo
(Directed Acyclic
ed
ar
Graphs), integrating with AWS services through operators and hooks
p
re
P
Answer: C
lly
Fu
Explanation:
re
A
Correct option:
ou
Y
Use Amazon SageMaker Pipelines to orchestrate the entire ML workflow, leveraging
g
in
ur
its built-in integration with SageMaker features like training, tuning, and deployment
ns
-E
Amazon SageMaker Pipelines is a purpose-built workflow orchestration service to
)
02
automate machine learning (ML) development.
8.
(V
SageMaker Pipelines is specifically designed for orchestrating ML workflows. It
ps
um
provides native integration with SageMaker features like model training, tuning, and
D
01
deployment. It also supports versioning, lineage tracking, and automatic execution of
-C
LA
workflows, making it the ideal choice for managing end-to-end ML workflows in AWS.
M
on
via - https://docs.aws.amazon.com/sagemaker/latest/dg/pipelines.html
az
m
A
Incorrect options:
t
es
Use Apache Airflow to define and manage the workflow with custom DAGs (Directed
B
Acyclic Graphs), integrating with AWS services through operators and hooks -
Apache Airflow is a powerful orchestration tool that allows you to define complex
workflows using custom DAGs. However, it requires significant setup and
maintenance, and while it can integrate with AWS services, it does not provide the
seamless, built-in integration with SageMaker that SageMaker Pipelines offers.
Amazon Managed Workflows for Apache Airflow (Amazon MWAA):
,via - https://aws.amazon.com/managed-workflows-for-apache-airflow/
Use AWS Step Functions to build a serverless workflow that integrates with
m
SageMaker for model training and deployment, ensuring scalability and fault tolerance
xa
E
- AWS Step Functions is a serverless orchestration service that can integrate with
01
-C
SageMaker and other AWS services. However, it is more general-purpose and lacks
LA
M
some of the ML-specific features, such as model lineage tracking and hyperparameter
e
th
r
tuning, that are built into SageMaker Pipelines.
fo
ed
Use AWS Lambda functions to manually trigger each step of the ML workflow,
par
re
enabling flexible execution without needing a predefined orchestration tool - AWS
P
lly
Lambda is useful for triggering specific tasks, but manually managing each step of a
Fu
re
complex ML workflow without a comprehensive orchestration tool is not scalable or
A
ou
maintainable. It does not provide the task dependency management, monitoring, and
Y
g
in
versioning required for an end-to-end ML workflow.
ur
ns
References:
-E
)
https://docs.aws.amazon.com/sagemaker/latest/dg/pipelines.html
02
8.
(V
https://aws.amazon.com/managed-workflows-for-apache-airflow/
ps
um
D
01
-C
2.You are tasked with building a predictive model for customer lifetime value (CLV)
LA
M
using Amazon SageMaker. Given the complexity of the model, it’s crucial to optimize
on
az
hyperparameters to achieve the best possible performance. You decide to use
m
A
t
SageMaker’s automatic model tuning (hyperparameter optimization) with Random
es
B
Search strategy to fine-tune the model. You have a large dataset, and the tuning job
involves several hyperparameters, including the learning rate, batch size, and dropout
rate. During the tuning process, you observe that some of the trials are not
converging effectively, and the results are not as expected. You suspect that the
hyperparameter ranges or the strategy you are using may need adjustment.
Which of the following approaches is MOST LIKELY to improve the effectiveness of
the hyperparameter tuning process?
A. Decrease the number of total trials but increase the number of parallel jobs to
speed up the tuning process
B. Switch from the Random Search strategy to the Bayesian Optimization strategy
, and narrow the range of critical hyperparameters
C. Use the Grid Search strategy with a wide range for all hyperparameters and
increase the number of total trials
D. Increase the number of hyperparameters being tuned and widen the range for all
hyperparameters
Answer: B
Explanation:
Correct option:
Switch from the Random Search strategy to the Bayesian Optimization strategy and
narrow the range of critical hyperparameters
When you’re training machine learning models, each dataset and model needs a
different set of hyperparameters, which are a kind of variable. The only way to
determine these is through multiple experiments, where you pick a set of
m
xa
hyperparameters and run them through your model. This is called hyperparameter
E
01
tuning. In essence, you're training your model sequentially with different sets of
-C
LA
hyperparameters. This process can be manual, or you can pick one of several
M
e
th
automated hyperparameter tuning methods.
r
fo
Bayesian Optimization is a technique based on Bayes’ theorem, which describes the
ed
ar
probability of an event occurring related to current knowledge. When this is applied to
p
re
P
hyperparameter optimization, the algorithm builds a probabilistic model from a set of
lly
Fu
hyperparameters that optimizes a specific metric. It uses regression analysis to
re
A
iteratively choose the best set of hyperparameters.
ou
Y
Random Search selects groups of hyperparameters randomly on each iteration. It
g
in
ur
works well when a relatively small number of the hyperparameters primarily determine
ns
-E
the model outcome.
)
02
Bayesian Optimization is more efficient than Random Search for hyperparameter
8.
(V
tuning, especially when dealing with complex models and large hyperparameter
ps
um
spaces. It learns from previous trials to predict the best set of hyperparameters, thus
D
01
focusing the search more effectively. Narrowing the range of critical hyperparameters
-C
LA
can further improve the chances of finding the optimal values, leading to better model
M
on
convergence and performance.
az
m
A
How hyperparameter tuning with Amazon SageMaker works:
t
es
B
