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 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.

2 / 30

A Machine Learning Specialist trained a regression model, but the first iteration needs optimizing. The

Specialist needs to understand whether the model is more frequently overestimating or underestimating

the target.

What option can the Specialist use to determine whether it is overestimating or underestimating the target

value?

Root Mean Square Error (RMSE)

Residual plots

Area under the curve

Confusion matrix

3 / 30

A retail chain has been ingesting purchasing records from its network of 20,000 stores to Amazon S3 using

Amazon Kinesis Data Firehose. To support training an improved machine learning model, training records

will require new but simple transformations, and some attributes will be combined. The model needs to be

retrained daily.

Given the large number of stores and the legacy data ingestion, which change will require the LEAST

amount of development effort?

Require that the stores to switch to capturing their data locally on AWS Storage Gateway for loading into Amazon S3, then use AWS Glue to do the transformation.

Deploy an Amazon EMR cluster running Apache Spark with the transformation logic, and have the cluster run each day on the accumulating records in Amazon S3, outputting new/transformed records to Amazon S3.

Spin up a fleet of Amazon EC2 instances with the transformation logic, have them transform the data records accumulating on Amazon S3, and output the transformed records to Amazon S3.

Insert an Amazon Kinesis Data Analytics stream downstream of the Kinesis Data Firehose stream that transforms raw record attributes into simple transformed values using SQL.

4 / 30

Machine Learning Specialist is training a model to identify the make and model of vehicles in images. The

Specialist wants to use transfer learning and an existing model trained on images of general objects. The

Specialist collated a large custom dataset of pictures containing different vehicle makes and models.

What should the Specialist do to initialize the model to re-train it with the custom data?

Initialize the model with random weights in all layers including the last fully connected layer.

Initialize the model with pre-trained weights in all layers and replace the last fully connected layer.

Initialize the model with random weights in all layers and replace the last fully connected layer.

Initialize the model with pre-trained weights in all layers including the last fully connected layer.

5 / 30

A company wants to classify user behavior as either fraudulent or normal. Based on internal research, a

Machine Learning Specialist would like to build a binary classifier based on two features: age of account

and transaction month. The class distribution for these features is illustrated in the figure provided.

Based on this information, which model would have the HIGHEST recall with respect to the fraudulent class?

Decision tree

Linear support vector machine (SVM)

Naive Bayesian classifier

Single Perceptron with sigmoidal activation function

6 / 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.

7 / 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.

8 / 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

9 / 30

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?

The model is 86% accurate and the cost incurred by the company as a result of false negatives is less than the false positives.

The precision of the model is 86%, which is less than the accuracy of the model.

The model is 86% accurate and the cost incurred by the company as a result of false positives is less than the false negatives.

The precision of the model is 86%, which is greater than the accuracy of the model.

10 / 30

A Data Scientist wants to gain real-time insights into a data stream of GZIP files.

Which solution would allow the use of SQL to query the stream with the LEAST latency?

Amazon Kinesis Data Analytics with an AWS Lambda function to transform the data.

AWS Glue with a custom ETL script to transform the data.

An Amazon Kinesis Client Library to transform the data and save it to an Amazon ES cluster.

Amazon Kinesis Data Firehose to transform the data and put it into an Amazon S3 bucket.

11 / 30

The displayed graph is from a forecasting model for testing a time series.

Considering the graph only, which conclusion should a Machine Learning Specialist make about the

behavior of the model?

The model predicts both the trend and the seasonality well

The model predicts the trend well, but not the seasonality.

The model predicts the seasonality well, but not the trend.

The model does not predict the trend or the seasonality well.

12 / 30

An agency collects census information within a country to determine healthcare and social program needs

by province and city. The census form collects responses for approximately 500 questions from each

citizen.

Which combination of algorithms would provide the appropriate insights? (Select TWO.)

The factorization machines (FM) algorithm

The Latent Dirichlet Allocation (LDA) algorithm

The principal component analysis (PCA) algorithm

The k-means algorithm

The Random Cut Forest (RCF) algorithm

13 / 30

Which of the following metrics should a Machine Learning Specialist generally use to compare/evaluate machine learning classification models against each other?

Recall

Misclassification rate

Mean absolute percentage error (MAPE)

Area Under the ROC Curve (AUC)

14 / 30

A financial services company is building a robust serverless data lake on Amazon S3. The data lake

should be flexible and meet the following requirements:

Support querying old and new data on Amazon S3 through Amazon Athena and Amazon Redshift

Spectrum.

Support event-driven ETL pipelines

Provide a quick and easy way to understand metadata

Which approach meets these requirements?

Use an AWS Glue crawler to crawl S3 data, an AWS Lambda function to trigger an AWS Glue ETL job, and an AWS Glue Data catalog to search and discover metadata.

Use an AWS Glue crawler to crawl S3 data, an AWS Lambda function to trigger an AWS Batch job, and an external Apache Hive metastore to search and discover metadata.

Use an AWS Glue crawler to crawl S3 data, an Amazon CloudWatch alarm to trigger an AWS Batch job, and an AWS Glue Data Catalog to search and discover metadata.

Use an AWS Glue crawler to crawl S3 data, an Amazon CloudWatch alarm to trigger an AWS Glue ETL job, and an external Apache Hive metastore to search and discover metadata.

15 / 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.

16 / 30

A manufacturing company has a large set of labeled historical sales data. The manufacturer would like to

predict how many units of a particular part should be produced each quarter.

Which machine learning approach should be used to solve this problem?

Logistic regression

Random Cut Forest (RCF)

Principal component analysis (PCA)

Linear regression

17 / 30

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?

Drop all records from the dataset where age has been set to 0.

Replace the age field value for records with a value of 0 with the mean or median value from the dataset

Drop the age feature from the dataset and train the model using the rest of the features.

Use k-means clustering to handle missing features

18 / 30

Machine Learning Specialist is building a model to predict future employment rates based on a wide range

of economic factors. While exploring the data, the Specialist notices that the magnitude of the input

features vary greatly. The Specialist does not want variables with a larger magnitude to dominate the

model.

What should the Specialist do to prepare the data for model training?

Apply quantile binning to group the data into categorical bins to keep any relationships in the data by replacing the magnitude with distribution.

Apply the Cartesian product transformation to create new combinations of fields that are independent of the magnitude.

Apply normalization to ensure each field will have a mean of 0 and a variance of 1 to remove any significant magnitude.

Apply the orthogonal sparse bigram (OSB) transformation to apply a fixed-size sliding window to generate new features of a similar magnitude.

19 / 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

20 / 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.

21 / 30

An interactive online dictionary wants to add a widget that displays words used in similar contexts. A

Machine Learning Specialist is asked to provide word features for the downstream nearest neighbor model

powering the widget.

What should the Specialist do to meet these requirements?

Create one-hot word encoding vectors.

Produce a set of synonyms for every word using Amazon Mechanical Turk.

Create word embedding vectors that store edit distance with every other word.

Download word embeddings pre-trained on a large corpus.

22 / 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

23 / 30

A Data Engineer needs to build a model using a dataset containing customer credit card information

How can the Data Engineer ensure the data remains encrypted and the credit card information is secure?

Use a custom encryption algorithm to encrypt the data and store the data on an Amazon SageMaker instance in a VPC. Use the SageMaker DeepAR algorithm to randomize the credit card numbers.

Use an IAM policy to encrypt the data on the Amazon S3 bucket and Amazon Kinesis to automatically discard credit card numbers and insert fake credit card numbers.

Use an Amazon SageMaker launch configuration to encrypt the data once it is copied to the SageMaker instance in a VPC. Use the SageMaker principal component analysis (PCA) algorithm to reduce the length of the credit card numbers.

Use AWS KMS to encrypt the data on Amazon S3 and Amazon SageMaker, and redact the credit card numbers from the customer data with AWS Glue.

24 / 30

A Machine Learning Specialist built an image classification deep learning model. However, the Specialist

ran into an overfitting problem in which the training and testing accuracies were 99% and 75%,

respectively.

How should the Specialist address this issue and what is the reason behind it?

The learning rate should be increased because the optimization process was trapped at a local minimum.

The dropout rate at the flatten layer should be increased because the model is not generalized enough.

The dimensionality of dense layer next to the flatten layer should be increased because the model is not complex enough.

The epoch number should be increased because the optimization process was terminated before it reached the global minimum.

25 / 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.

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 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

28 / 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.

29 / 30

A company is using Amazon Polly to translate plaintext documents to speech for automated company

announcements. However, company acronyms are being mispronounced in the current documents.

How should a Machine Learning Specialist address this issue for future documents?

Convert current documents to SSML with pronunciation tags.

Create an appropriate pronunciation lexicon.

Output speech marks to guide in pronunciation.

Use Amazon Lex to preprocess the text files for pronunciation

30 / 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.

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