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Latest Amazon AWS Certified Specialty MLS-C01 exam practice test
QUESTION 1
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 2
For the given confusion matrix, what is the recall and precision of the model?
A. Recall = 0.92 Precision = 0.84
B. Recall = 0.84 Precision = 0.8
C. Recall = 0.92 Precision = 0.8
D. Recall = 0.8 Precision = 0.92
Correct Answer: A
QUESTION 3
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:
Based on the model evaluation results, why is this a viable model for production?
A. The model is 86% accurate and the cost incurred by the company as a result of false negatives is less than the false
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.
Correct Answer: B
QUESTION 4
A city wants to monitor its air quality to address the consequences of air pollution A Machine Learning Specialist needs
to forecast the air quality in parts per million of contaminates for the next 2 days in the city As this is a prototype, only
daily data from the last year is available
Which model is MOST likely to provide the best results in Amazon SageMaker?
A. Use the Amazon SageMaker k-Nearest-Neighbors (kNN) algorithm on the single time series consisting of the full year
of data with a predictor_type of the regressor.
B. Use Amazon SageMaker Random Cut Forest (RCF) on the single time series consisting of the full year of data.
C. Use the Amazon SageMaker Linear Learner algorithm on the single time series consisting of the full year of data with
a predictor_type of the regressor.
D. Use the Amazon SageMaker Linear Learner algorithm on the single time series consisting of the full year of data with a predictor_type of the classifier.
Correct Answer: C
Reference: https://aws.amazon.com/blogs/machine-learning/build-a-model-to-predict-the-impact-of-weather-on-urbanair-quality-using-amazon-sagemaker/?ref=Welcome.AI
QUESTION 5
A Machine Learning Specialist is building a logistic regression model that will predict whether or not a person will order a
pizza. The Specialist is trying to build the optimal model with an ideal classification threshold. What model evaluation
of the technique should the Specialist use to understand how different classification thresholds will impact the model\\’s
performance?
A. Receiver operating characteristic (ROC) curve
B. Misclassification rate
C. Root Mean Square Error (RMand)
D. L1 norm
Correct Answer: A
Reference: https://docs.aws.amazon.com/machine-learning/latest/dg/binary-model-insights.html
QUESTION 6
Given the following confusion matrix for a movie classification model, what is the true class frequency for Romance and
the predicted class frequency for Adventure?
A. The true class frequency for Romance is 77.56% and the predicted class frequency for Adventure is 20 85%
B. The true class frequency for Romance is 57.92% and the predicted class frequency for Adventure is 1312%
C. The true class frequency for Romance is 0 78 and the predicted class frequency for Adventure is (0 47 – 0.32).
D. The true class frequency for Romance is 77.56% * 0.78 and the predicted class frequency for Adventure is 20 85% \\’
0.32
Correct Answer: A
QUESTION 7
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 8
While working on a neural network project, a Machine Learning Specialist discovers that some features in the data have
very high magnitude resulting in this data being weighted more in the cost function What should the Specialist do to
ensure better convergence during backpropagation?
A. Dimensionality reduction
B. Data normalization
C. Model regulanzation
D. Data augmentation for the minority class
Correct Answer: D
QUESTION 9
A Machine Learning Specialist works for a credit card processing company and needs to predict which transactions may
be fraudulent in near-real-time. Specifically, the Specialist must train a model that returns the probability that a given
transaction may be fraudulent
How should the Specialist frame this business problem\\’?
A. Streaming classification
B. Binary classification
C. multi-category classification
D. Regression classification
Correct Answer: A
QUESTION 10
A Data Science team within a large company uses Amazon SageMaker notebooks to access data stored in Amazon S3
buckets. The IT Security team is concerned that internet-enabled notebook instances create a security vulnerability
where malicious code running on the instances could compromise data privacy. The company mandates that all
instances stay within a secured VPC with no internet access, and data communication traffic must stay within the AWS
network.
How should the Data Science team configure the notebook instance placement to meet these requirements?
A. Associate the Amazon SageMaker notebook with a private subnet in a VPC. Place the Amazon SageMaker endpoint
and S3 buckets within the same VPC.
B. Associate the Amazon SageMaker notebook with a private subnet in a VPC. Use 1AM policies to grant access to
Amazon S3 and Amazon SageMaker.
C. Associate the Amazon SageMaker notebook with a private subnet in a VPC. Ensure the VPC has S3 VPC endpoints
and Amazon SageMaker VPC endpoints attached to it.
D. Associate the Amazon SageMaker notebook with a private subnet in a VPC. Ensure the VPC has a NAT gateway
and an associated security group allowing only outbound connections to Amazon S3 and Amazon SageMaker
Correct Answer: D
QUESTION 11
A Machine Learning Specialist is designing a system for improving sales for a company. The objective is to use the
large amount of information the company has on users\\’ behavior and product preferences to predict which products
users would like based on the users\\’ similarity to other users.
What should the Specialist do to meet this objective?
A. Build a content-based filtering recommendation engine with Apache Spark ML on Amazon EMR.
B. Build a collaborative filtering recommendation engine with Apache Spark ML on Amazon EMR.
C. Build a model-based filtering recommendation engine with Apache Spark ML on Amazon EMR.
D. Build a combinative filtering recommendation engine with Apache Spark ML on Amazon EMR.
Correct Answer: B
Many developers want to implement the famous Amazon model that was used to power the “People who bought this
also bought these items” feature on Amazon.com. This model is based on a method called Collaborative Filtering. It
takes items such as movies, books, and products that were rated highly by a set of users and recommending them to
other users who also gave them high ratings. This method works well in domains where explicit ratings or implicit user
actions can be gathered and analyzed.
Reference: https://aws.amazon.com/blogs/big-data/building-a-recommendation-engine-with-spark-ml-on-amazon-emrusing-zeppelin/
QUESTION 12
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
will
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
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. Configure 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: https://docs.aws.amazon.com/sagemaker/latest/dg/encryption-at-rest.html
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