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 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?

Use AWS Data Pipeline to transform the data and Amazon RDS to run queries.

Use AWS Glue to catalogue the data and Amazon Athena to run queries.

Use AWS Batch to run ETL on the data and Amazon Aurora to run the queries.

Use AWS Lambda to transform the data and Amazon Kinesis Data Analytics to run queries.

2 / 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.

3 / 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

4 / 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)

5 / 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.

6 / 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.

7 / 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.

8 / 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.

9 / 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.

10 / 30

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?

Listwise deletion

Last observation carried forward

Multiple imputation

Mean substitution

11 / 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.

12 / 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.

13 / 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.

14 / 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.

15 / 30

A Machine Learning Specialist is developing a daily ETL workflow containing multiple ETL jobs. The

workflow consists of the following processes:

• Start the workflow as soon as data is uploaded to Amazon S3.

• When all the datasets are available in Amazon S3, start an ETL job to join the uploaded datasets with

multiple terabyte-sized datasets already stored in Amazon S3.

• Store the results of joining datasets in Amazon S3.

• If one of the jobs fails, send a notification to the Administrator.

Which configuration will meet these requirements?

Use AWS Lambda to trigger an AWS Step Functions workflow to wait for dataset uploads to complete in Amazon S3. Use AWS Glue to join the datasets. Use an Amazon CloudWatch alarm to send an SNS notification to the Administrator in the case of a failure.

Develop the ETL workflow using AWS Lambda to start an Amazon SageMaker notebook instance. Use a lifecycle configuration script to join the datasets and persist the results in Amazon S3. Use an Amazon CloudWatch alarm to send an SNS notification to the Administrator in the case of a failure.

Develop the ETL workflow using AWS Batch to trigger the start of ETL jobs when data is uploaded to Amazon S3. Use AWS Glue to join the datasets in Amazon S3. Use an Amazon CloudWatch alarm to send an SNS notification to the Administrator in the case of a failure.

Use AWS Lambda to chain other Lambda functions to read and join the datasets in Amazon S3 as soon as the data is uploaded to Amazon S3. Use an Amazon CloudWatch alarm to send an SNS notification to the Administrator in the case of a failure.

16 / 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.

17 / 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

18 / 30

A Machine Learning Specialist is creating a new natural language processing application that processes a

dataset comprised of 1 million sentences. The aim is to then run Word2Vec to generate embeddings of the

sentences and enable different types of predictions.

Here is an example from the dataset:

"The quck BROWN FOX jumps over the lazy dog."

Which of the following are the operations the Specialist needs to perform to correctly sanitize and prepare

the data in a repeatable manner? (Choose three.)

Perform part-of-speech tagging and keep the action verb and the nouns only.

Normalize all words by making the sentence lowercase.

Remove stop words using an English stopword dictionary.

Correct the typography on "quck" to "quick."

One-hot encode all words in the sentence.

Tokenize the sentence into words.

19 / 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.

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 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.

22 / 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.

23 / 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.

24 / 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.

25 / 30

A Machine Learning Specialist is packaging a custom ResNet model into a Docker container so the

company can leverage Amazon SageMaker for training. The Specialist is using Amazon EC2 P3 instances

to train the model and needs to properly configure the Docker container to leverage the NVIDIA GPUs.

What does the Specialist need to do?

Bundle the NVIDIA drivers with the Docker image.

Build the Docker container to be NVIDIA-Docker compatible.

Organize the Docker container's file structure to execute on GPU instances.

Set the GPU flag in the Amazon SageMaker CreateTrainingJob request body.

26 / 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

27 / 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

28 / 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

29 / 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.

30 / 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.

Your score is