1
AWS Machine Learning Specialist Exam
Questions with 100% Correct Answer
2025 Top Rated A+
A data scientist conducts data exploration and analysis
using an Amazon SageMaker notebook instance. This
involves installing some Python packages on the notebook
instance that are not natively accessible on Amazon
SageMaker.How can a machine learning professional
guarantee that the data scientist's essential packages are
automatically accessible on the notebook instance?
A. Install AWS Systems Manager Agent on the underlying
Amazon EC2 instance and use Systems Manager Automation
to execute the package installation commands.
B. Create a Jupyter notebook file (.ipynb) with cells
containing the package installation commands to execute
and place the file under the /etc/init directory of each Amazon
SageMaker notebook instance.
C. Use the conda package manager from within the Jupyter
notebook console to apply the necessary conda packages to
the default kernel of the notebook.
D. Create an Amazon SageMaker lifecycle configuration with
package installation commands and assign the lifecycle
configuration to the notebook instance.
D
I would select D. See AWS documentation:
https://docs.aws.amazon.com/sagemaker/latest/dg/nbi-add-
external.html
A web-based business wishes to increase conversions on its
landing page. The business developed a multi-class deep
,2
learning network algorithm using Amazon SageMaker
regularly using a big historical dataset of client visits.
However, there is an overfitting issue: training data indicates
a prediction accuracy of 90%, whereas test data indicates
only a prediction accuracy of 70%. The organization has to
increase the generalizability of its model prior to putting it in
production in order to optimize visit-to-purchase
conversions. Which activity is advised to ensure that the
company's test and validation data is modeled with the
HIGHEST degree of accuracy possible?
A. Increase the randomization of training data in the mini-
batches used in training
B. Allocate a higher proportion of the overall data to the
training dataset
C. Apply L1 or L2 regularization and dropouts to the training
D. Reduce the number of layers and units (or neurons) from
the deep learning network
D
Because we have 'deep neural network'. And there are two ways
to reduce overfitting of a neural network: 1) Change network
complexity by changing the network structure (number of
weights). 2) Change network complexity by changing the network
parameters (values of weights).
A business evaluates the risk variables associated with a
specific energy sector using a long short-term memory
(LSTM) model. The program analyzes multi-page text
documents and categorizes each phrase as either posing a
danger or posing no risk. The model is underperforming,
despite the Data Scientist's extensive experimentation with
several network architectures and tuning of the associated
hyperparameters. Which technique will result in the
MAXIMUM increase in performance?
,3
A. Initialize the words by term frequency-inverse document
frequency (TF-IDF) vectors pretrained on a large collection of
news articles related to the energy sector.
B. Use gated recurrent units (GRUs) instead of LSTM and run
the training process until the validation loss stops
decreasing.
C. Reduce the learning rate and run the training process until
the training loss stops decreasing.
D. Initialize the words by word2vec embeddings pretrained
on a large collection of news articles related to the energy
sector.
D
C is not the best the answer because the question states that
tuning parameters doesn't help a lot. Transfer learning would be
better solution.
A big mobile network operator is developing a machine
learning algorithm to forecast which consumers are likely to
cancel their service subscription. The corporation intends to
give an incentive to retain these clients, since the cost of
churn is far more than the incentive's cost.After testing on a
test dataset of 100 consumers, the model generates the
following confusion matrix:
n= 100. Predicted Churn.
Yes. No.
Actual Yes. 10 4
Actual No. 10 76
Why is this a feasible model for
manufacturing, based on the model assessment results?
A. The model is 86% accurate and the cost incurred by the
company as a result of false negatives is less than the false
, 4
positives.
B. The precision of the model is 86%, which is less than the
accuracy of the model.
C. The model is 86% accurate and the cost incurred by the
company as a result of false positives is less than the false
negatives.
D. The precision of the model is 86%, which is greater than
the accuracy of the model.
A or D
On a company's social media page, an employee saw a video
clip with audio. The video is in Spanish. The employee's
primary language is English, and he or she does not
comprehend Spanish. The employee requests that a
sentiment analysis be performed.Which service combination
is the MOST EFFECTIVE in completing the task?
A. Amazon Transcribe, Amazon Translate, and Amazon
Comprehend
B. Amazon Transcribe, Amazon Comprehend, and Amazon
SageMaker seq2seq
C. Amazon Transcribe, Amazon Translate, and Amazon
SageMaker Neural Topic Model (NTM)
D. Amazon Transcribe, Amazon Translate and Amazon
SageMaker BlazingText
A
A business analyzes camera photos of the tops of objects
placed on shop shelves to identify which things have been
taken and which remain. The organization now has a total of
1,000 hand-labeled photos encompassing ten separate
things after many hours of data tagging. The training was
ineffective.Which machine learning technique best meets the
long-term goals of the business?
A. Convert the images to grayscale and retrain the model
AWS Machine Learning Specialist Exam
Questions with 100% Correct Answer
2025 Top Rated A+
A data scientist conducts data exploration and analysis
using an Amazon SageMaker notebook instance. This
involves installing some Python packages on the notebook
instance that are not natively accessible on Amazon
SageMaker.How can a machine learning professional
guarantee that the data scientist's essential packages are
automatically accessible on the notebook instance?
A. Install AWS Systems Manager Agent on the underlying
Amazon EC2 instance and use Systems Manager Automation
to execute the package installation commands.
B. Create a Jupyter notebook file (.ipynb) with cells
containing the package installation commands to execute
and place the file under the /etc/init directory of each Amazon
SageMaker notebook instance.
C. Use the conda package manager from within the Jupyter
notebook console to apply the necessary conda packages to
the default kernel of the notebook.
D. Create an Amazon SageMaker lifecycle configuration with
package installation commands and assign the lifecycle
configuration to the notebook instance.
D
I would select D. See AWS documentation:
https://docs.aws.amazon.com/sagemaker/latest/dg/nbi-add-
external.html
A web-based business wishes to increase conversions on its
landing page. The business developed a multi-class deep
,2
learning network algorithm using Amazon SageMaker
regularly using a big historical dataset of client visits.
However, there is an overfitting issue: training data indicates
a prediction accuracy of 90%, whereas test data indicates
only a prediction accuracy of 70%. The organization has to
increase the generalizability of its model prior to putting it in
production in order to optimize visit-to-purchase
conversions. Which activity is advised to ensure that the
company's test and validation data is modeled with the
HIGHEST degree of accuracy possible?
A. Increase the randomization of training data in the mini-
batches used in training
B. Allocate a higher proportion of the overall data to the
training dataset
C. Apply L1 or L2 regularization and dropouts to the training
D. Reduce the number of layers and units (or neurons) from
the deep learning network
D
Because we have 'deep neural network'. And there are two ways
to reduce overfitting of a neural network: 1) Change network
complexity by changing the network structure (number of
weights). 2) Change network complexity by changing the network
parameters (values of weights).
A business evaluates the risk variables associated with a
specific energy sector using a long short-term memory
(LSTM) model. The program analyzes multi-page text
documents and categorizes each phrase as either posing a
danger or posing no risk. The model is underperforming,
despite the Data Scientist's extensive experimentation with
several network architectures and tuning of the associated
hyperparameters. Which technique will result in the
MAXIMUM increase in performance?
,3
A. Initialize the words by term frequency-inverse document
frequency (TF-IDF) vectors pretrained on a large collection of
news articles related to the energy sector.
B. Use gated recurrent units (GRUs) instead of LSTM and run
the training process until the validation loss stops
decreasing.
C. Reduce the learning rate and run the training process until
the training loss stops decreasing.
D. Initialize the words by word2vec embeddings pretrained
on a large collection of news articles related to the energy
sector.
D
C is not the best the answer because the question states that
tuning parameters doesn't help a lot. Transfer learning would be
better solution.
A big mobile network operator is developing a machine
learning algorithm to forecast which consumers are likely to
cancel their service subscription. The corporation intends to
give an incentive to retain these clients, since the cost of
churn is far more than the incentive's cost.After testing on a
test dataset of 100 consumers, the model generates the
following confusion matrix:
n= 100. Predicted Churn.
Yes. No.
Actual Yes. 10 4
Actual No. 10 76
Why is this a feasible model for
manufacturing, based on the model assessment results?
A. The model is 86% accurate and the cost incurred by the
company as a result of false negatives is less than the false
, 4
positives.
B. The precision of the model is 86%, which is less than the
accuracy of the model.
C. The model is 86% accurate and the cost incurred by the
company as a result of false positives is less than the false
negatives.
D. The precision of the model is 86%, which is greater than
the accuracy of the model.
A or D
On a company's social media page, an employee saw a video
clip with audio. The video is in Spanish. The employee's
primary language is English, and he or she does not
comprehend Spanish. The employee requests that a
sentiment analysis be performed.Which service combination
is the MOST EFFECTIVE in completing the task?
A. Amazon Transcribe, Amazon Translate, and Amazon
Comprehend
B. Amazon Transcribe, Amazon Comprehend, and Amazon
SageMaker seq2seq
C. Amazon Transcribe, Amazon Translate, and Amazon
SageMaker Neural Topic Model (NTM)
D. Amazon Transcribe, Amazon Translate and Amazon
SageMaker BlazingText
A
A business analyzes camera photos of the tops of objects
placed on shop shelves to identify which things have been
taken and which remain. The organization now has a total of
1,000 hand-labeled photos encompassing ten separate
things after many hours of data tagging. The training was
ineffective.Which machine learning technique best meets the
long-term goals of the business?
A. Convert the images to grayscale and retrain the model
