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 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

2 / 30

A Machine Learning Specialist is configuring Amazon SageMaker so multiple Data Scientists can access

notebooks, train models, and deploy endpoints. To ensure the best operational performance, the Specialist

needs to be able to track how often the Scientists are deploying models, GPU and CPU utilization on the

deployed SageMaker endpoints, and all errors that are generated when an endpoint is invoked.

Which services are integrated with Amazon SageMaker to track this information? (Choose two.)

AWS CloudTrail

AWS Health

AWS Trusted Advisor

Amazon CloudWatch

AWS Config

3 / 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.

4 / 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.

5 / 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.

6 / 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.

7 / 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.

8 / 30

A company is running a machine learning prediction service that generates 100 TB of predictions every

day. A Machine Learning Specialist must generate a visualization of the daily precision-recall curve from

the predictions, and forward a read-only version to the Business team.

Which solution requires the LEAST coding effort?

Run daily Amazon EMR workflow to generate precision-recall data, and save the results in Amazon S3. Give the Business team read-only access to S3.

Generate daily precision-recall data in Amazon QuickSight, and publish the results in a dashboard shared with the Business team.

Run a daily Amazon EMR workflow to generate precision-recall data, and save the results in Amazon S3. Visualize the arrays in Amazon QuickSight, and publish them in a dashboard shared with the Business team.

Generate daily precision-recall data in Amazon ES, and publish the results in a dashboard shared with the Business team.

9 / 30

An employee found a video clip with audio on a company's social media feed. The language used in the

video is Spanish. English is the employee's first language, and they do not understand Spanish. The

employee wants to do a sentiment analysis.

What combination of services is the MOST efficient to accomplish the task?

Amazon Transcribe, Amazon Translate, and Amazon Comprehend

Amazon Transcribe, Amazon Comprehend, and Amazon SageMaker seq2seq

Amazon Transcribe, Amazon Translate, and Amazon SageMaker Neural Topic Model (NTM)

Amazon Transcribe, Amazon Translate and Amazon SageMaker BlazingText

10 / 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.

11 / 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.

12 / 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.

13 / 30

An insurance company is developing a new device for vehicles that uses a camera to observe drivers'

behavior and alert them when they appear distracted. The company created approximately 10,000 training

images in a controlled environment that a Machine Learning Specialist will use to train and evaluate

machine learning models.

During the model evaluation, the Specialist notices that the training error rate diminishes faster as the

number of epochs increases and the model is not accurately inferring on the unseen test images.

Which of the following should be used to resolve this issue? (Choose two.)

Add vanishing gradient to the model.

Perform data augmentation on the training data.

Make the neural network architecture complex.

Use gradient checking in the model.

Add L2 regularization to the model.

14 / 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

15 / 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

16 / 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

17 / 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.

18 / 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.

19 / 30

A Machine Learning Specialist must build out a process to query a dataset on Amazon S3 using Amazon

Athena. The dataset contains more than 800,000 records stored as plaintext CSV files. Each record

contains 200 columns and is approximately 1.5 MB in size. Most queries will span 5 to 10 columns only.

How should the Machine Learning Specialist transform the dataset to minimize query runtime?

Convert the records to Apache Parquet format.

Convert the records to JSON format.

Convert the records to GZIP CSV format.

Convert the records to XML format.

20 / 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.

21 / 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.

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 Machine Learning Specialist is assigned a TensorFlow project using Amazon SageMaker for training,

and needs to continue working for an extended period with no Wi-Fi access.

Which approach should the Specialist use to continue working?

Install Python 3 and boto3 on their laptop and continue the code development using that environment.

Download the TensorFlow Docker container used in Amazon SageMaker from GitHub to their local environment, and use the Amazon SageMaker Python SDK to test the code.

Download TensorFlow from tensorflow.org to emulate the TensorFlow kernel in the SageMaker environment.

Download the SageMaker notebook to their local environment, then install Jupyter Notebooks on their laptop and continue the development in a local notebook.

24 / 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.

25 / 30

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 technique should the Specialist use to understand how different classification thresholds will impact the model's performance?

Receiver operating characteristic (ROC) curve

Misclassification rate

Root Mean Square Error (RMSE)

L1 norm

26 / 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.

27 / 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.

28 / 30

A Data Scientist needs to create a serverless ingestion and analytics solution for high-velocity, real-time

streaming data.

The ingestion process must buffer and convert incoming records from JSON to a query-optimized,

columnar format without data loss. The output datastore must be highly available, and Analysts must be

able to run SQL queries against the data and connect to existing business intelligence dashboards.

Which solution should the Data Scientist build to satisfy the requirements?

Create a schema in the AWS Glue Data Catalog of the incoming data format. Use an Amazon Kinesis Data Firehose delivery stream to stream the data and transform the data to Apache Parquet or ORC format using the AWS Glue Data Catalog before delivering to Amazon S3. Have the Analysts query the data directly from Amazon S3 using Amazon Athena, and connect to BI tools using the Athena Java Database Connectivity (JDBC) connector.

Write each JSON record to a staging location in Amazon S3. Use the S3 Put event to trigger an AWS Lambda function that transforms the data into Apache Parquet or ORC format and writes the data to a processed data location in Amazon S3. Have the Analysts query the data directly from Amazon S3 using Amazon Athena, and connect to BI tools using the Athena Java Database Connectivity (JDBC) connector.

Write each JSON record to a staging location in Amazon S3. Use the S3 Put event to trigger an AWS Lambda function that transforms the data into Apache Parquet or ORC format and inserts it into an Amazon RDS PostgreSQL database. Have the Analysts query and run dashboards from the RDS database.

Use Amazon Kinesis Data Analytics to ingest the streaming data and perform real-time SQL queries to convert the records to Apache Parquet before delivering to Amazon S3. Have the Analysts query the data directly from Amazon S3 using Amazon Athena and connect to BI tools using the Athena Java Database Connectivity (JDBC) connector.

29 / 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 accuracy?

Long short-term memory (LSTM) model with scaled exponential linear unit (SELU)

Logistic regression

Support vector machine (SVM) with non-linear kernel

Single perceptron with tanh activation function

30 / 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.

Your score is