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Amazon AWS Certified Specialty MLS-C01 Practice Test 1-13
QUESTION 1
A Machine Learning Specialist is working for a credit card processing company and receives an unbalanced dataset
containing credit card transactions. It contains 99,000 valid transactions and 1,000 fraudulent transactions The
Specialist is asked to score a model that was run against the dataset The Specialist has been advised that identifying
valid transactions is equally as important as identifying fraudulent transactions What metric is BEST suited to score the
model?
A. Precision
B. Recall
C. Area Under the ROC Curve (AUC)
D. Root Mean Square Error (RMSE)
Correct Answer: A
QUESTION 2
A Machine Learning Specialist kicks off a hyperparameter tuning job for a tree-based ensemble model using Amazon
SageMaker with Area Under the ROC Curve (AUC) as the objective metric This workflow will eventually be deployed in
a
pipeline that retrains and tunes hyperparameters each night to model click-through on data that goes stale every 24
hours
With the goal of decreasing the amount of time it takes to train these models, and ultimately to decrease costs, the
Specialist wants to reconfigure the input hyperparameter range(s)
Which visualization will accomplish this?
A. A histogram showing whether the most important input feature is Gaussian.
B. A scatter plot with points colored by target variable that uses (-Distributed Stochastic Neighbor Embedding (I-SNE) to
visualize the large number of input variables in an easier-to-read dimension.
C. A scatter plot showing (he performance of the objective metric over each training iteration
D. A scatter plot showing the correlation between maximum tree depth and the objective metric.
Correct Answer: B
QUESTION 3
A Data Scientist is developing a machine learning model to predict future patient outcomes based on information
collected about each patient and their treatment plans. The model should output a continuous value as its prediction.
The data
available includes labeled outcomes for a set of 4,000 patients. The study was conducted on a group of individuals over
the age of 65 who have a particular disease that is known to worsen with age.
Initial models have performed poorly. While reviewing the underlying data, the Data Scientist notices that, out of 4,000
patient observations, there are 450 where the patient age has been input as 0. The other features for these
observations
appear normal compared to the rest of the sample population.
How should the Data Scientist correct this issue?
A. Drop all records from the dataset where age has been set to 0.
B. Replace the age field value for records with a value of 0 with the mean or median value from the dataset.
C. Drop the age feature from the dataset and train the model using the rest of the features.
D. Use k-means clustering to handle missing features.
Correct Answer: A
QUESTION 4
A large mobile network operating company is building a machine learning model to predict customers who are likely to
unsubscribe from the service. The company plans to offer an incentive for these customers as the cost of churn is far
greater than the cost of the incentive.
The model produces the following confusion matrix after evaluating on a test dataset of 100 customers:
QUESTION 5
A company\\’s Machine Learning Specialist needs to improve the training speed of a time-series forecasting model using TensorFlow. The training is currently implemented on a single-GPU machine and takes approximately 23 hours to
complete. The training needs to be run daily.
The model accuracy js acceptable, but the company anticipates a continuous increase in the size of the training data
and a need to update the model on an hourly, rather than a daily, basis. The company also wants to minimize coding
effort and infrastructure changes
What should the Machine Learning Specialist do to the training solution to allow it to scale for future demand?
A. Do not change the TensorFlow code. Change the machine to one with a more powerful GPU to speed up the
training.
B. Change the TensorFlow code to implement a Horovod distributed framework supported by Amazon SageMaker.
Parallelize the training to as many machines as needed to achieve the business goals.
C. Switch to using a built-in AWS SageMaker DeepAR model. Parallelize the training to as many machines as needed
to achieve the business goals.
D. Move the training to Amazon EMR and distribute the workload to as many machines as needed to achieve the
business goals.
Correct Answer: B
QUESTION 6
A Machine Learning Specialist has built a model using Amazon SageMaker built-in algorithms and is not getting
expected accurate results The Specialist wants to use hyperparameter optimization to increase the model\\’s accuracy
Which method is the MOST repeatable and requires the LEAST amount of effort to achieve this?
A. Launch multiple training jobs in parallel with different hyperparameters
B. Create an AWS Step Functions workflow that monitors the accuracy in Amazon CloudWatch Logs and relaunches
the training job with a defined list of hyperparameters
C. Create a hyperparameter tuning job and set the accuracy as an objective metric.
D. Create a random walk in the parameter space to iterate through a range of values that should be used for each
individual hyperparameter
Correct Answer: B
QUESTION 7
A Machine Learning Specialist is developing a custom video recommendation model for an application. The dataset
used to train this model is very large with millions of data points and is hosted in an Amazon S3 bucket. The Specialist
wants to avoid loading all of this data onto an Amazon SageMaker notebook instance because it would take hours to
move and will exceed the attached 5 GB Amazon EBS volume on the notebook instance.
Which approach allows the Specialist to use all the data to train the model?
A. Load a smaller subset of the data into the SageMaker notebook and train locally. Confirm that the training code is
executing and the model parameters seem reasonable. Initiate a SageMaker training job using the full dataset from the
S3 bucket using Pipe input mode.
B. Launch an Amazon EC2 instance with an AWS Deep Learning AMI and attach the S3 bucket to the instance. Train
on a small amount of the data to verify the training code and hyperparameters. Go back to Amazon SageMaker and
train using the full dataset
C. Use AWS Glue to train a model using a small subset of the data to confirm that the data will be compatible with
Amazon SageMaker. Initiate a SageMaker training job using the full dataset from the S3 bucket using Pipe input mode.
D. Load a smaller subset of the data into the SageMaker notebook and train locally. Confirm that the training code is
executing and the model parameters seem reasonable. Launch an Amazon EC2 instance with an AWS Deep Learning
AMI and attach the S3 bucket to train the full dataset.
Correct Answer: A
QUESTION 8
A large consumer goods manufacturer has the following products on sale:
1.
34 different toothpaste variants
2.
48 different toothbrush variants
3.
43 different mouthwash variants
The entire sales history of all these products is available in Amazon S3. Currently, the company is using custom-built
autoregressive integrated moving average (ARIMA) models to forecast demand for these products. The company wants
to
predict the demand for a new product that will soon be launched.
Which solution should a Machine Learning Specialist apply?
A. Train a custom ARIMA model to forecast demand for the new product.
B. Train an Amazon SageMaker DeepAR algorithm to forecast demand for the new product
C. Train an Amazon SageMaker k-means clustering algorithm to forecast demand for the new product.
D. Train a custom XGBoost model to forecast demand for the new product
Correct Answer: B
The Amazon SageMaker DeepAR forecasting algorithm is a supervised learning algorithm for forecasting scalar (onedimensional) time series using recurrent neural networks (RNN). Classical forecasting methods, such as autoregressive
integrated moving average (ARIMA) or exponential smoothing (ETS), fit a single model to each individual time series.
They then use that model to extrapolate the time series into the future.
Reference: click here
QUESTION 8
A large consumer goods manufacturer has the following products on sale:
1.
34 different toothpaste variants
2.
48 different toothbrush variants
3.
43 different mouthwash variants
The entire sales history of all these products is available in Amazon S3. Currently, the company is using custom-built
autoregressive integrated moving average (ARIMA) models to forecast demand for these products. The company wants
to
predict the demand for a new product that will soon be launched.
Which solution should a Machine Learning Specialist apply?
A. Train a custom ARIMA model to forecast demand for the new product.
B. Train an Amazon SageMaker DeepAR algorithm to forecast demand for the new product
C. Train an Amazon SageMaker k-means clustering algorithm to forecast demand for the new product.
D. Train a custom XGBoost model to forecast demand for the new product
Correct Answer: B
The Amazon SageMaker DeepAR forecasting algorithm is a supervised learning algorithm for forecasting scalar (onedimensional) time series using recurrent neural networks (RNN). Classical forecasting methods, such as autoregressive
integrated moving average (ARIMA) or exponential smoothing (ETS), fit a single model to each individual time series.
They then use that model to extrapolate the time series into the future.
Reference: click here
QUESTION 9
A Data Scientist needs to migrate an existing on-premises ETL process to the cloud. The current process runs at
regular time intervals and uses PySpark to combine and format multiple large data sources into a single consolidated
output for
downstream processing.
The Data Scientist has been given the following requirements to the cloud solution:
Combine multiple data sources.
Reuse existing PySpark logic.
Run the solution on the existing schedule.
Minimize the number of servers that will need to be managed.
Which architecture should the Data Scientist use to build this solution?
A. Write the raw data to Amazon S3. Schedule an AWS Lambda function to submit a Spark step to a persistent Amazon
EMR cluster based on the existing schedule. Use the existing PySpark logic to run the ETL job on the EMR cluster.
Output the results to a “processed” location in Amazon S3 that is accessible for downstream use.
B. Write the raw data to Amazon S3. Create an AWS Glue ETL job to perform the ETL processing against the input
data. Write the ETL job in PySpark to leverage the existing logic. Create a new AWS Glue trigger to trigger the ETL job
based on the existing schedule. Configure the output target of the ETL job to write to a “processed” location in Amazon
S3 that is accessible for downstream use.
C. Write the raw data to Amazon S3. Schedule an AWS Lambda function to run on the existing schedule and process
the input data from Amazon S3. Write the Lambda logic in Python and implement the existing PySpark logic to perform
the ETL process. Have the Lambda function output the results to a “processed” location in Amazon S3 that is
accessible for downstream use.
D. Use Amazon Kinesis Data Analytics to stream the input data and perform real-time SQL queries against the stream
to carry out the required transformations within the stream. Deliver the output results to a “processed” location in
Amazon S3 that is accessible for downstream use.
Correct Answer: D
QUESTION 10
A Machine Learning Specialist is packaging a custom ResNet model into a Docker container so the company can
leverage Amazon SageMaker for training. The Specialist is using Amazon EC2 P3 instances to train the model and
needs to properly configure the Docker container to leverage the NVIDIA GPUs.
What does the Specialist need to do?
A. Bundle the NVIDIA drivers with the Docker image.
B. Build the Docker container to be NVIDIA-Docker compatible.
C. Organize the Docker container\\’s file structure to execute on GPU instances.
D. Set the GPU flag in the Amazon SageMaker CreateTrainingJob request body
Correct Answer: A
QUESTION 11
A Machine Learning Specialist was given a dataset consisting of unlabeled data The Specialist must create a model that
can help the team classify the data into different buckets What model should be used to complete this work?
A. K-means clustering
B. Random Cut Forest (RCF)
C. XGBoost
D. BlazingText
Correct Answer: A
QUESTION 12
A Machine Learning Specialist is working with a large cybersecurily company that manages security events in real time
for companies around the world The cybersecurity company wants to design a solution that will allow it to use machine
learning to score malicious events as anomalies on the data as it is being ingested The company also wants be able to
save the results in its data lake for later processing and analysis
What is the MOST efficient way to accomplish 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 replication factor of three as the data lake
C. Ingest the data and store it in Amazon S3 Use AWS Batch along with the AWS Deep Learning AMIs to train a kmeans model using TensorFlow on the data in Amazon S3.
D. Ingest the data and store it in Amazon S3. Have an AWS Glue job that is triggered on demand transform the new
data Then use the built-in Random Cut Forest (RCF) model within Amazon SageMaker to detect anomalies in the data
Correct Answer: B
QUESTION 13
A Machine Learning Specialist uploads a dataset to an Amazon S3 bucket protected with server-side encryption using
AWS KMS.
How should the ML Specialist define the Amazon SageMaker notebook instance so it can read the same dataset from
Amazon S3?
A. Define security group(s) to allow all HTTP inbound/outbound traffic and assign those security group(s) to the Amazon
SageMaker notebook instance.
B. ?onfigure the Amazon SageMaker notebook instance to have access to the VPC. Grant permission in the KMS key
policy to the notebook\\’s KMS role.
C. Assign an IAM role to the Amazon SageMaker notebook with S3 read access to the dataset. Grant permission in the
KMS key policy to that role.
D. Assign the same KMS key used to encrypt data in Amazon S3 to the Amazon SageMaker notebook instance.
Correct Answer: D
Reference: click here
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