AWS Certified Machine Exam 2 with
complete solutions 2024/2025
A firm operates a vast number of factories and maintains a complicated supply
chain connection in which an unexpected breakdown of a machine might result in
the suspension of operations at multiple plants. A data scientist wishes to
examine factory sensor data in order to detect equipment in need of preventative
maintenance and then deploy a repair crew to avoid unscheduled downtime. A
single machine's sensor data may include up to 200 data points, including
temperatures, voltages, vibrations, RPMs, and pressure measurements.The firm
put Wi-Fi and LANs across the plants to capture this sensor data. Despite the fact
that many industrial sites lack stable or high-speed internet access, the
manufacturer want to retain near-real-time inference capabilities.Which model
deployment architecture will satisfy these business requirements?
A. Deploy the model in Amazon SageMaker. Run sensor data through this model
to predict wh - ANSWER-B. Deploy the model on AWS IoT Greengrass in each
factory. Run sensor data through this model to infer which machines need
maintenance.
A Machine Learning Specialist is employed by a multinational cybersecurity firm
that handles real-time security events for businesses worldwide. The
cybersecurity firm wants to develop a system that would enable it to employ
machine learning to classify dangerous events as anomalies in data as it is
consumed. Additionally, the corporation wishes to save the findings in its data
lake for subsequent processing and analysis.Which method is the MOST
EFFECTIVE for completing these tasks?
A. Ingest the data using Amazon Kinesis Data Firehose, and use Amazon Kinesis
Data Analytics Random Cut Forest (RCF) for anomaly detection. Then use Kinesis
Data Firehose to stream the results to Amazon S3.
B. Ingest the data into Apache Spark Streaming using Amazon EMR, and use
Spark MLlib with k-means to perform anomaly detection. Then store the results in
,an Apache Hadoop Distributed File System (HDFS) using Amazon EMR with a
replicati - ANSWER-A. Ingest the data using Amazon Kinesis Data Firehose, and
use Amazon Kinesis Data Analytics Random Cut Forest (RCF) for anomaly
detection. Then use Kinesis Data Firehose to stream the results to Amazon S3.
A real estate firm wishes to develop a machine learning model capable of
forecasting home values using a historical dataset. 32 features are included in the
dataset.
Which model is most appropriate for the business requirement?
A. Logistic regression
B. Linear regression
C. K-means
D. Principal component analysis (PCA) - ANSWER-B. Linear regression
A machine learning specialist created a deep learning model for picture
categorization. The Specialist, on the other hand, encountered an overfitting
issue, with training and testing accuracies of 99 percent and 75%,
respectively.How should the Specialist approach this situation and what is the
underlying cause?
A. The learning rate should be increased because the optimization process was
trapped at a local minimum.
B. The dropout rate at the flatten layer should be increased because the model is
not generalized enough.
C. The dimensionality of dense layer next to the flatten layer should be increased
because the model is not complex enough.
D. The epoch number should be increased because the optimization process was
terminated before it reached the global minimum. - ANSWER-B. The dropout rate
at the flatten layer should be increased because the model is not generalized
enough.
A business want to categorize user behavior as fraudulent or normal. A machine
learning expert will develop a binary classifier based on two features: the
account's age, represented by x, and the month of the transaction, denoted by y.
The distributions of the classes are shown in the accompanying image. Positive
classes are shown in red, whereas negative classes are depicted in black.Which
model would be the most precise?
A. Linear support vector machine (SVM)
B. Decision tree
C. Support vector machine (SVM) with a radial basis function kernel
D. Single perceptron with a Tanh activation function - ANSWER-C. Support vector
machine (SVM) with a radial basis function kernel
, A Machine Learning Specialist is required to work for an online shop that want to
do analytics on each client visit using a machine learning pipeline.The data must
be ingested at a rate of up to 100 transactions per second using Amazon Kinesis
Data Streams, and the JSON data blob must be 100 KB in size.What is the
MINIMUM number of shards that the Specialist should employ in Kinesis Data
Streams to effectively ingest this data?
A. 1 shards
B. 10 shards
C. 100 shards
D. 1,000 shards - ANSWER-B. 10 shards
A machine learning professional is running an Amazon SageMaker endpoint on a
P3 instance and using the built-in object identification algorithm to make real-
time predictions in a production application. When the expert examines the
model's resource consumption, he or she sees that the model is only using a
portion of the GPU.Which architectural improvements would maximize the use of
provided resources?
A. Redeploy the model as a batch transform job on an M5 instance.
B. Redeploy the model on an M5 instance. Attach Amazon Elastic Inference to the
instance.
C. Redeploy the model on a P3dn instance.
D. Deploy the model onto an Amazon Elastic Container Service (Amazon ECS)
cluster using a P3 instance. - ANSWER-B. Redeploy the model on an M5 instance.
Attach Amazon Elastic Inference to the instance.
A financial services firm wants to make Amazon SageMaker its primary data
science environment. On sensitive financial data, the company's data scientists
run machine learning (ML) models. The organization is concerned about data
egress and desires the services of a machine learning engineer to safeguard the
environment.Which methods does the machine learning engineer have at his
disposal to manage data egress from SageMaker? (Select three.)
A. Connect to SageMaker by using a VPC interface endpoint powered by AWS
PrivateLink.
B. Use SCPs to restrict access to SageMaker.
C. Disable root access on the SageMaker notebook instances.
D. Enable network isolation for training jobs and models.
E. Restrict notebook presigned URLs to specific IPs used by the company.
F. Protect data with encryption at rest and in transit. Use AWS Key Management
Service (AWS KMS) to manage encryption keys. - ANSWER-A. Connect to
SageMaker by using a VPC interface endpoint powered by AWS PrivateLink.
complete solutions 2024/2025
A firm operates a vast number of factories and maintains a complicated supply
chain connection in which an unexpected breakdown of a machine might result in
the suspension of operations at multiple plants. A data scientist wishes to
examine factory sensor data in order to detect equipment in need of preventative
maintenance and then deploy a repair crew to avoid unscheduled downtime. A
single machine's sensor data may include up to 200 data points, including
temperatures, voltages, vibrations, RPMs, and pressure measurements.The firm
put Wi-Fi and LANs across the plants to capture this sensor data. Despite the fact
that many industrial sites lack stable or high-speed internet access, the
manufacturer want to retain near-real-time inference capabilities.Which model
deployment architecture will satisfy these business requirements?
A. Deploy the model in Amazon SageMaker. Run sensor data through this model
to predict wh - ANSWER-B. Deploy the model on AWS IoT Greengrass in each
factory. Run sensor data through this model to infer which machines need
maintenance.
A Machine Learning Specialist is employed by a multinational cybersecurity firm
that handles real-time security events for businesses worldwide. The
cybersecurity firm wants to develop a system that would enable it to employ
machine learning to classify dangerous events as anomalies in data as it is
consumed. Additionally, the corporation wishes to save the findings in its data
lake for subsequent processing and analysis.Which method is the MOST
EFFECTIVE for completing these tasks?
A. Ingest the data using Amazon Kinesis Data Firehose, and use Amazon Kinesis
Data Analytics Random Cut Forest (RCF) for anomaly detection. Then use Kinesis
Data Firehose to stream the results to Amazon S3.
B. Ingest the data into Apache Spark Streaming using Amazon EMR, and use
Spark MLlib with k-means to perform anomaly detection. Then store the results in
,an Apache Hadoop Distributed File System (HDFS) using Amazon EMR with a
replicati - ANSWER-A. Ingest the data using Amazon Kinesis Data Firehose, and
use Amazon Kinesis Data Analytics Random Cut Forest (RCF) for anomaly
detection. Then use Kinesis Data Firehose to stream the results to Amazon S3.
A real estate firm wishes to develop a machine learning model capable of
forecasting home values using a historical dataset. 32 features are included in the
dataset.
Which model is most appropriate for the business requirement?
A. Logistic regression
B. Linear regression
C. K-means
D. Principal component analysis (PCA) - ANSWER-B. Linear regression
A machine learning specialist created a deep learning model for picture
categorization. The Specialist, on the other hand, encountered an overfitting
issue, with training and testing accuracies of 99 percent and 75%,
respectively.How should the Specialist approach this situation and what is the
underlying cause?
A. The learning rate should be increased because the optimization process was
trapped at a local minimum.
B. The dropout rate at the flatten layer should be increased because the model is
not generalized enough.
C. The dimensionality of dense layer next to the flatten layer should be increased
because the model is not complex enough.
D. The epoch number should be increased because the optimization process was
terminated before it reached the global minimum. - ANSWER-B. The dropout rate
at the flatten layer should be increased because the model is not generalized
enough.
A business want to categorize user behavior as fraudulent or normal. A machine
learning expert will develop a binary classifier based on two features: the
account's age, represented by x, and the month of the transaction, denoted by y.
The distributions of the classes are shown in the accompanying image. Positive
classes are shown in red, whereas negative classes are depicted in black.Which
model would be the most precise?
A. Linear support vector machine (SVM)
B. Decision tree
C. Support vector machine (SVM) with a radial basis function kernel
D. Single perceptron with a Tanh activation function - ANSWER-C. Support vector
machine (SVM) with a radial basis function kernel
, A Machine Learning Specialist is required to work for an online shop that want to
do analytics on each client visit using a machine learning pipeline.The data must
be ingested at a rate of up to 100 transactions per second using Amazon Kinesis
Data Streams, and the JSON data blob must be 100 KB in size.What is the
MINIMUM number of shards that the Specialist should employ in Kinesis Data
Streams to effectively ingest this data?
A. 1 shards
B. 10 shards
C. 100 shards
D. 1,000 shards - ANSWER-B. 10 shards
A machine learning professional is running an Amazon SageMaker endpoint on a
P3 instance and using the built-in object identification algorithm to make real-
time predictions in a production application. When the expert examines the
model's resource consumption, he or she sees that the model is only using a
portion of the GPU.Which architectural improvements would maximize the use of
provided resources?
A. Redeploy the model as a batch transform job on an M5 instance.
B. Redeploy the model on an M5 instance. Attach Amazon Elastic Inference to the
instance.
C. Redeploy the model on a P3dn instance.
D. Deploy the model onto an Amazon Elastic Container Service (Amazon ECS)
cluster using a P3 instance. - ANSWER-B. Redeploy the model on an M5 instance.
Attach Amazon Elastic Inference to the instance.
A financial services firm wants to make Amazon SageMaker its primary data
science environment. On sensitive financial data, the company's data scientists
run machine learning (ML) models. The organization is concerned about data
egress and desires the services of a machine learning engineer to safeguard the
environment.Which methods does the machine learning engineer have at his
disposal to manage data egress from SageMaker? (Select three.)
A. Connect to SageMaker by using a VPC interface endpoint powered by AWS
PrivateLink.
B. Use SCPs to restrict access to SageMaker.
C. Disable root access on the SageMaker notebook instances.
D. Enable network isolation for training jobs and models.
E. Restrict notebook presigned URLs to specific IPs used by the company.
F. Protect data with encryption at rest and in transit. Use AWS Key Management
Service (AWS KMS) to manage encryption keys. - ANSWER-A. Connect to
SageMaker by using a VPC interface endpoint powered by AWS PrivateLink.
