AWS Certified Machine Learning - Specialty (MLS-C01)

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This quiz randomly generates 30 questions as asked in AWS Certified Machine Learning - Specialty (MLS-C01)

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This quiz randomly generates 30 questions (in 60 mins) as asked in AWS Certified Machine Learning - Specialty (MLS-C01). The real MLS-C01 test has 65 questions and a total time of 180 minutes. Of these, 15 questions are underlined, and only 50 questions are scored. This test randomly generates 30 questions from our question bank. For best results, practice multiple times until you achieve 100% accuracy.

1 / 30

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?

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 regressor.

Use Amazon SageMaker Random Cut Forest (RCF) on the single time series consisting of the full year of data.

Use the Amazon SageMaker Linear Learner algorithm on the single time series consisting of the full year of data with a predictor_type of regressor.

Use the Amazon SageMaker Linear Learner algorithm on the single time series consisting of the full year of data with a predictor_type of classifier.

2 / 30

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?

Amazon SageMaker notebook instances are based on the EC2 instances within the customer account, but they run outside of VPCs.

Amazon SageMaker notebook instances are based on the Amazon ECS service within customer accounts.

Amazon SageMaker notebook instances are based on EC2 instances running within AWS service accounts.

Amazon SageMaker notebook instances are based on AWS ECS instances running within AWS service accounts.

3 / 30

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 histogram showing whether the most important input feature is Gaussian.

A scatter plot with points colored by target variable that uses t-Distributed Stochastic Neighbor Embedding (t-SNE) to visualize the large number of input variables in an easier-to-read dimension.

A scatter plot showing the performance of the objective metric over each training iteration.

A scatter plot showing the correlation between maximum tree depth and the objective metric.

4 / 30

A Machine Learning Specialist is building a prediction model for a large number of features using linear

models, such as linear regression and logistic regression. During exploratory data analysis, the Specialist

observes that many features are highly correlated with each other. This may make the model unstable.

What should be done to reduce the impact of having such a large number of features?

Perform one-hot encoding on highly correlated features.

Use matrix multiplication on highly correlated features.

Create a new feature space using principal component analysis (PCA)

Apply the Pearson correlation coefficient.

5 / 30

A Machine Learning Specialist needs to be able to ingest streaming data and store it in Apache Parquet

files for exploration and analysis.

Which of the following services would both ingest and store this data in the correct format?

AWS DMS

Amazon Kinesis Data Streams

Amazon Kinesis Data Firehose

Amazon Kinesis Data Analytics

6 / 30

A Data Science team is designing a dataset repository where it will store a large amount of training data

commonly used in its machine learning models. As Data Scientists may create an arbitrary number of new

datasets every day, the solution has to scale automatically and be cost-effective. Also, it must be possible

to explore the data using SQL.

Which storage scheme is MOST adapted to this scenario?

Store datasets as files in Amazon S3.

Store datasets as files in an Amazon EBS volume attached to an Amazon EC2 instance.

Store datasets as tables in a multi-node Amazon Redshift cluster.

Store datasets as global tables in Amazon DynamoDB.

7 / 30

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?

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.

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

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.

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.

8 / 30

An office security agency conducted a successful pilot using 100 cameras installed at key locations within

the main office. Images from the cameras were uploaded to Amazon S3 and tagged using Amazon

Rekognition, and the results were stored in Amazon ES. The agency is now looking to expand the pilot into

a full production system using thousands of video cameras in its office locations globally. The goal is to

identify activities performed by non-employees in real time

Which solution should the agency consider?

Use a proxy server at each local office and for each camera, and stream the RTSP feed to a unique Amazon Kinesis Video Streams video stream. On each stream, use Amazon Rekognition Video and create a stream processor to detect faces from a collection of known employees, and alert when non- employees are detected.

Use a proxy server at each local office and for each camera, and stream the RTSP feed to a unique Amazon Kinesis Video Streams video stream. On each stream, use Amazon Rekognition Image to detect faces from a collection of known employees and alert when non-employees are detected.

Install AWS DeepLens cameras and use the DeepLens_Kinesis_Video module to stream video to Amazon Kinesis Video Streams for each camera. On each stream, use Amazon Rekognition Video and create a stream processor to detect faces from a collection on each stream, and alert when non- employees are detected.

Install AWS DeepLens cameras and use the DeepLens_Kinesis_Video module to stream video to Amazon Kinesis Video Streams for each camera. On each stream, run an AWS Lambda function to capture image fragments and then call Amazon Rekognition Image to detect faces from a collection of known employees, and alert when non-employees are detected.

9 / 30

A Machine Learning Specialist is preparing data for training on Amazon SageMaker. The Specialist is

using one of the SageMaker built-in algorithms for the training. The dataset is stored in .CSV format and is

transformed into a numpy.array, which appears to be negatively affecting the speed of the training.

What should the Specialist do to optimize the data for training on SageMaker?

Use the SageMaker batch transform feature to transform the training data into a DataFrame.

Use AWS Glue to compress the data into the Apache Parquet format.

Transform the dataset into the RecordIO protobuf format.

Use the SageMaker hyperparameter optimization feature to automatically optimize the data.

10 / 30

A monitoring service generates 1 TB of scale metrics record data every minute. A Research team performs

queries on this data using Amazon Athena. The queries run slowly due to the large volume of data, and the

team requires better performance. How should the records be stored in Amazon S3 to improve query performance?

CSV files

Parquet files

Compressed JSON

RecordlO

11 / 30

A Machine Learning Specialist is building a convolutional neural network (CNN) that will classify 10 types

of animals. The Specialist has built a series of layers in a neural network that will take an input image of an

animal, pass it through a series of convolutional and pooling layers, and then finally pass it through a

dense and fully connected layer with 10 nodes. The Specialist would like to get an output from the neural

network that is a probability distribution of how likely it is that the input image belongs to each of the 10

classes.

Which function will produce the desired output?

Dropout

Smooth L1 loss

Softmax

Rectified linear units (ReLU)

12 / 30

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?

Train a custom ARIMA model to forecast demand for the new product.

Train an Amazon SageMaker DeepAR algorithm to forecast demand for the new product.

Train an Amazon SageMaker k-means clustering algorithm to forecast demand for the new product.

Train a custom XGBoost model to forecast demand for the new product.

13 / 30

A Data Scientist is working on an application that performs sentiment analysis. The validation accuracy is

poor, and the Data Scientist thinks that the cause may be a rich vocabulary and a low average frequency

of words in the dataset.

Which tool should be used to improve the validation accuracy?

Amazon Comprehend syntax analysis and entity detection

Amazon SageMaker BlazingText cbow mode

Natural Language Toolkit (NLTK) stemming and stop word removal

Scikit-leam term frequency-inverse document frequency (TF-IDF) vectorizer

14 / 30

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? (Choose

two.)

Add more deep trees to the random forest to enable the model to learn more features.

Include a copy of the samples in the test dataset in the training dataset.

Generate more positive samples by duplicating the positive samples and adding a small amount of noise to the duplicated data.

Change the cost function so that false negatives have a higher impact on the cost value than false positives.

Change the cost function so that false positives have a higher impact on the cost value than false negatives.

15 / 30

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 is 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?

Do not change the TensorFlow code. Change the machine to one with a more powerful GPU to speed up the training.

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.

Switch to using a built-in AWS SageMaker DeepAR model. Parallelize the training to as many machines as needed to achieve the business goals.

Move the training to Amazon EMR and distribute the workload to as many machines as needed to achieve the business goals.

16 / 30

A Machine Learning team uses Amazon SageMaker to train an Apache MXNet handwritten digit classifier

model using a research dataset. The team wants to receive a notification when the model is overfitting.

Auditors want to view the Amazon SageMaker log activity report to ensure there are no unauthorized API

calls.

What should the Machine Learning team do to address the requirements with the least amount of code and

fewest steps?

Implement an AWS Lambda function to log Amazon SageMaker API calls to Amazon S3. Add code to push a custom metric to Amazon CloudWatch. Create an alarm in CloudWatch with Amazon SNS to receive a notification when the model is overfitting.

Use AWS CloudTrail to log Amazon SageMaker API calls to Amazon S3. Add code to push a custom metric to Amazon CloudWatch. Create an alarm in CloudWatch with Amazon SNS to receive a notification when the model is overfitting.

Implement an AWS Lambda function to log Amazon SageMaker API calls to AWS CloudTrail. Add code to push a custom metric to Amazon CloudWatch. Create an alarm in CloudWatch with Amazon SNS to receive a notification when the model is overfitting.

Use AWS CloudTrail to log Amazon SageMaker API calls to Amazon S3. Set up Amazon SNS to receive a notification when the model is overfitting

17 / 30

A Machine Learning Specialist has created a deep learning neural network model that performs well on the

training data but performs poorly on the test data.

Which of the following methods should the Specialist consider using to correct this? (Choose three.)

Decrease regularization.

Increase regularization.

Increase dropout.

Decrease dropout.

Increase feature combinations.

Decrease feature combinations.

18 / 30

A Machine Learning Specialist deployed a model that provides product recommendations on a company's

website. Initially, the model was performing very well and resulted in customers buying more products on

average. However, within the past few months, the Specialist has noticed that the effect of product

recommendations has diminished and customers are starting to return to their original habits of spending

less. The Specialist is unsure of what happened, as the model has not changed from its initial deployment

over a year ago.

Which method should the Specialist try to improve model performance?

The model needs to be completely re-engineered because it is unable to handle product inventory changes.

The model's hyperparameters should be periodically updated to prevent drift.

The model should be periodically retrained from scratch using the original data while adding a regularization term to handle product inventory changes

The model should be periodically retrained using the original training data plus new data as product inventory changes.

19 / 30

A Data Scientist is developing a machine learning model to classify whether a financial transaction is

fraudulent. The labeled data available for training consists of 100,000 non-fraudulent observations and

1,000 fraudulent observations.

The Data Scientist applies the XGBoost algorithm to the data, resulting in the following confusion matrix

when the trained model is applied to a previously unseen validation dataset. The accuracy of the model is

99.1%, but the Data Scientist has been asked to reduce the number of false negatives.

Which combination of steps should the Data Scientist take to reduce the number of false positive

predictions by the model? (Choose two.)

Change the XGBoost eval_metric parameter to optimize based on rmse instead of error.

Increase the XGBoost scale_pos_weight parameter to adjust the balance of positive and negative weights.

Increase the XGBoost max_depth parameter because the model is currently underfitting the data.

Change the XGBoost eval_metric parameter to optimize based on AUC instead of error.

Decrease the XGBoost max_depth parameter because the model is currently overfitting the data.

20 / 30

A Machine Learning Specialist is required to build a supervised image-recognition model to identify a cat.

The ML Specialist performs some tests and records the following results for a neural network-based image

classifier:

Total number of images available = 1,000

Test set images = 100 (constant test set)

The ML Specialist notices that, in over 75% of the misclassified images, the cats were held upside down by

their owners.

Which techniques can be used by the ML Specialist to improve this specific test error?

Increase the training data by adding variation in rotation for training images.

Increase the number of epochs for model training

Increase the number of layers for the neural network.

Increase the dropout rate for the second-to-last layer.

21 / 30

A Machine Learning Specialist is implementing a full Bayesian network on a dataset that describes public

transit in New York City. One of the random variables is discrete, and represents the number of minutes

New Yorkers wait for a bus given that the buses cycle every 10 minutes, with a mean of 3 minutes.

Which prior probability distribution should the ML Specialist use for this variable?

Poisson distribution

Uniform distribution

Normal distribution

Binomial distribution

22 / 30

A Marketing Manager at a pet insurance company plans to launch a targeted marketing campaign on

social media to acquire new customers. Currently, the company has the following data in Amazon Aurora:

Profiles for all past and existing customers

Profiles for all past and existing insured pets

Policy-level information

Premiums received

Claims paid

What steps should be taken to implement a machine learning model to identify potential new customers on

social media?

Use regression on customer profile data to understand key characteristics of consumer segments. Find similar profiles on social media

Use clustering on customer profile data to understand key characteristics of consumer segments. Find similar profiles on social media

Use a recommendation engine on customer profile data to understand key characteristics of consumer segments. Find similar profiles on social media.

Use a decision tree classifier engine on customer profile data to understand key characteristics of consumer segments. Find similar profiles on social media.

23 / 30

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?

Associate the Amazon SageMaker notebook with a private subnet in a VPC. Place the Amazon SageMaker endpoint and S3 buckets within the same VPC.

Associate the Amazon SageMaker notebook with a private subnet in a VPC. Use IAM policies to grant access to Amazon S3 and Amazon SageMaker.

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.

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.

24 / 30

A Machine Learning Specialist receives customer data for an online shopping website. The data includes

demographics, past visits, and locality information. The Specialist must develop a machine learning

approach to identify the customer shopping patterns, preferences, and trends to enhance the website-for

better service and smart recommendations.

Which solution should the Specialist recommend?

Latent Dirichlet Allocation (LDA) for the given collection of discrete data to identify patterns in the customer database.

A neural network with a minimum of three layers and random initial weights to identify patterns in the customer database.

Collaborative filtering based on user interactions and correlations to identify patterns in the customer database.

Random Cut Forest (RCF) over random subsamples to identify patterns in the customer database.

25 / 30

A Machine Learning Specialist is working with a large cybersecurity 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?

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.

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.

Ingest the data and store it in Amazon S3. Use AWS Batch along with the AWS Deep Learning AMIs to train a k-means model using TensorFlow on the data in Amazon S3.

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.

26 / 30

A Machine Learning Specialist has completed a proof of concept for a company using a small data sample,

and now the Specialist is ready to implement an end-to-end solution in AWS using Amazon SageMaker.

The historical training data is stored in Amazon RDS.

Which approach should the Specialist use for training a model using that data?

Write a direct connection to the SQL database within the notebook and pull data in

Push the data from Microsoft SQL Server to Amazon S3 using an AWS Data Pipeline and provide the S3 location within the notebook.

Move the data to Amazon DynamoDB and set up a connection to DynamoDB within the notebook to pull data in.

Move the data to Amazon ElastiCache using AWS DMS and set up a connection within the notebook to pull data in for fast access.

27 / 30

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?

Build a content-based filtering recommendation engine with Apache Spark ML on Amazon EMR

Build a collaborative filtering recommendation engine with Apache Spark ML on Amazon EMR

Build a model-based filtering recommendation engine with Apache Spark ML on Amazon EMR

Build a combinative filtering recommendation engine with Apache Spark ML on Amazon EMR

28 / 30

During mini-batch training of a neural network for a classification problem, a Data Scientist notices that

training accuracy oscillates.

What is the MOST likely cause of this issue?

The class distribution in the dataset is imbalanced.

Dataset shuffling is disabled.

The batch size is too big.

The learning rate is very high.

29 / 30

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?

Linear regression

Classification

Clustering

Reinforcement learning

30 / 30

A Mobile Network Operator is building an analytics platform to analyze and optimize a company's

operations using Amazon Athena and Amazon S3. The source systems send data in .CSV format in real time. The Data Engineering team wants to transform the data to the Apache Parquet format before storing it on Amazon S3. Which solution takes the LEAST effort to implement?

Ingest .CSV data using Apache Kafka Streams on Amazon EC2 instances and use Kafka Connect S3 to serialize data as Parquet

Ingest .CSV data from Amazon Kinesis Data Streams and use Amazon Glue to convert data into Parquet.

Ingest .CSV data using Apache Spark Structured Streaming in an Amazon EMR cluster and use Apache Spark to convert data into Parquet.

Ingest .CSV data from Amazon Kinesis Data Streams and use Amazon Kinesis Data Firehose to convert data into Parquet.

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