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Amazon MLS-C01 Practice testing questions from Youtube


latest updated Amazon MLS-C01 exam questions and answers

A Machine Learning Specialist is using an Amazon SageMaker notebook instance in a private subnet of a corporate
VPC. The ML Specialist has important data stored on the Amazon SageMaker notebook instance\\’s Amazon EBS
volume and needs to take a snapshot of that EBS volume. However, the ML Specialist cannot find the Amazon
SageMaker notebook instance\\’s EBS volume or Amazon EC2 instance within the VPC.
Why is the ML Specialist not seeing the instance visible in the VPC?
A. Amazon SageMaker notebook instances are based on the EC2 instances within the customer account, but they run
outside of VPCs.
B. Amazon SageMaker notebook instances are based on the Amazon ECS service within customer accounts.
C. Amazon SageMaker notebook instances are based on EC2 instances running within AWS service accounts.
D. Amazon SageMaker notebook instances are based on AWS ECS instances running within AWS service accounts.
Correct Answer: C
Reference: https://docs.aws.amazon.com/sagemaker/latest/dg/gs-setup-working-env.html


A company is observing low accuracy while training on the default built-in image classification algorithm in Amazon
SageMaker. The Data Science team wants to use an Inception neural network architecture instead of a ResNet
architecture. Which of the following will accomplish this? (Select TWO.)
A. Customize the built-in image classification algorithm to use Inception and use this for model training.
B. Create a support case with the SageMaker team to change the default image classification algorithm to Inception.
C. Bundle a Docker container with TensorFlow Estimator loaded with an Inception network and use this for model
D. Use custom code in Amazon SageMaker with TensorFlow Estimator to load the model with an Inception network and
use this for model training.
E. Download and apt-get install the inception network code into an Amazon EC2 instance and use this instance as a
Jupyter notebook in Amazon SageMaker.
Correct Answer: AD


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 to 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


An online reseller has a large, multi-column dataset with one column missing 30% of its data A Machine Learning Specialist believes that certain columns in the dataset could be used to reconstruct the missing data Which
reconstruction approach should the Specialist use to preserve the integrity of the dataset?
A. Listwise deletion
B. Last observation carried forward
C. Multiple imputation
D. Mean substitution
Correct Answer: C
Reference: https://worldwidescience.org/topicpages/i/imputing+missing+values.html


A gaming company has launched an online game where people can start playing for free but they need to pay if they
choose to use certain features The company needs to build an automated system to predict whether or not a new user
become a paid user within 1 year The company has gathered a labeled dataset from 1 million users The training dataset
consists of 1.000 positive samples (from users who ended up paying within 1 year) and 999.000 negative samples (from
users who did not use any paid features) Each data sample consists of 200 features including user age, device,
location, and play patterns
Using this dataset for training, the Data Science team trained a random forest model that converged with over 99%
accuracy on the training set However, the prediction results on a test dataset were not satisfactory. Which of the
following approaches should the Data Science team take to mitigate this issue? (Select TWO.)
A. Add more deep trees to the random forest to enable the model to learn more features.
B. indicates a copy of the samples in the test database in the training dataset
C. Generate more positive samples by duplicating the positive samples and adding a small amount of noise to the
duplicated data.
D. Change the cost function so that false negatives have a higher impact on the cost value than false positives
E. Change the cost function so that false positives have a higher impact on the cost value than false negatives
Correct Answer: BD


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
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


A Machine Learning Specialist is working with multiple data sources containing billions of records that need to be joined.
What features engineering and model development approach should the Specialist take with a dataset this large?
A. Use an Amazon SageMaker notebook for both feature engineering and model development
B. Use an Amazon SageMaker notebook for feature engineering and Amazon ML for model development
C. Use Amazon EMR for feature engineering and Amazon SageMaker SDK for model development
D. Use Amazon ML for both feature engineering and model development.
Correct Answer: B


QUESTION 8[2021.1] lead4pass mls-c01 exam questions q8

Considering the graph, what is a reasonable selection for the optimal choice of k?
A. 1
B. 4
C. 7
D. 10
Correct Answer: C


A large consumer goods manufacturer has the following products on sale:
34 different toothpaste variants
48 different toothbrush variants
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
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: https://docs.aws.amazon.com/sagemaker/latest/dg/deepar.html


A manufacturing company has structured and unstructured data stored in an Amazon S3 bucket. A Machine Learning Specialist wants to use SQL to run queries on this data.
Which solution requires the LEAST effort to be able to query this data?
A. Use AWS Data Pipeline to transform the data and Amazon RDS to run queries.
B. Use AWS Glue to catalog the data and Amazon Athena to run queries.
C. Use AWS Batch to run ETL on the data and Amazon Aurora to run the queries.
D. Use AWS Lambda to transform the data and Amazon Kinesis Data Analytics to run queries.
Correct Answer: B


A Machine Learning Specialist is working with a large company to leverage machine learning within its products. The
company wants to group its customers into categories based on which customers will and will not churn within the next
6 months. The company has labeled the data available to the Specialist.
Which machine learning model type should the Specialist use to accomplish this task?
A. Linear regression
B. Classification
C. Clustering
D. Reinforcement learning
Correct Answer: B
The goal of classification is to determine to which class or category a data point (customer in our case) belongs. For
classification problems, data scientists would use historical data with predefined target variables AKA labels
(churner/nonchurner)? answers that need to be predicted? to train an algorithm. With classification, businesses can
answer the following questions:
Will this customer churn or not?
Will a customer renew their subscription?
Will a user downgrade a pricing plan?
Are there any signs of unusual customer behavior?
Reference: https://www.kdnuggets.com/2019/05/churn-prediction-machine-learning.html


A Machine Learning Specialist needs to move and transform data in preparation for training Some of the data needs to
be processed in near-real-time and other data can be moved hourly There are existing Amazon EMR MapReduce jobs
to clean and feature engineering to perform on the data.
Which of the following services can feed data to the MapReduce jobs? (Select TWO )
B. Amazon Kinesis
C. AWS Data Pipeline
D. Amazon Athena
E. Amazon ES
Correct Answer: BD

An e-commerce company needs a customized training model to classify images of its shirts and pants products The company needs a proof of concept in 2 to 3 days with good accuracy Which compute choice should the Machine
Learning Specialist select to train and achieve good accuracy on the model quickly?
A. . m5 4xlarge (general purpose)
B. r5.2xlarge (memory-optimized)
C. p3.2xlarge (GPU accelerated computing)
D. p3 8xlarge (GPU accelerated computing)
Correct Answer: C

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