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

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

2 / 30

When submitting Amazon SageMaker training jobs using one of the built-in algorithms, which common parameters MUST be specified? (Choose three.)

The training channel identifying the location of training data on an Amazon S3 bucket.

The validation channel identifying the location of validation data on an Amazon S3 bucket.

The IAM role that Amazon SageMaker can assume to perform tasks on behalf of the users.

Hyperparameters in a JSON array as documented for the algorithm used.

The Amazon EC2 instance class specifying whether training will be run using CPU or GPU.

The output path specifying where on an Amazon S3 bucket the trained model will persist.

3 / 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.

4 / 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.

5 / 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

6 / 30

A company is observing low accuracy while training on the default built-in image classification algorithm in

Amazon SageMaker. The Data Science team wants to use an Inception neural network architecture

instead of a ResNet architecture.

Which of the following will accomplish this? (Choose two.)

Customize the built-in image classification algorithm to use Inception and use this for model training.

Create a support case with the SageMaker team to change the default image classification algorithm to Inception.

Bundle a Docker container with TensorFlow Estimator loaded with an Inception network and use this for model training.

Use custom code in Amazon SageMaker with TensorFlow Estimator to load the model with an Inception network, and use this for model training.

Download and apt-get install the inception network code into an Amazon EC2 instance and use this instance as a Jupyter notebook in Amazon SageMaker.

7 / 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.

8 / 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.

9 / 30

A retail company intends to use machine learning to categorize new products. A labeled dataset of current

products was provided to the Data Science team. The dataset includes 1,200 products. The labeled

dataset has 15 features for each product such as title dimensions, weight, and price. Each product is

labeled as belonging to one of six categories such as books, games, electronics, and movies.

Which model should be used for categorizing new products using the provided dataset for training?

AnXGBoost model where the objective parameter is set to multi:softmax

A deep convolutional neural network (CNN) with a softmax activation function for the last layer

A regression forest where the number of trees is set equal to the number of product categories

A DeepAR forecasting model based on a recurrent neural network (RNN)

10 / 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

11 / 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.

12 / 30

A Machine Learning Specialist at a company sensitive to security is preparing a dataset for model training.

The dataset is stored in Amazon S3 and contains Personally Identifiable Information (PII).

The dataset:

Must be accessible from a VPC only.

Must not traverse the public internet.

How can these requirements be satisfied?

Create a VPC endpoint and apply a bucket access policy that restricts access to the given VPC endpoint and the VPC.

Create a VPC endpoint and apply a bucket access policy that allows access from the given VPC endpoint and an Amazon EC2 instance.

Create a VPC endpoint and use Network Access Control Lists (NACLs) to allow traffic between only the given VPC endpoint and an Amazon EC2 instance.

Create a VPC endpoint and use security groups to restrict access to the given VPC endpoint and an Amazon EC2 instance

13 / 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

14 / 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.

15 / 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

16 / 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.

17 / 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)

18 / 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.

19 / 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

20 / 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.

21 / 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.

22 / 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

23 / 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.

24 / 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.

25 / 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.

26 / 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.

27 / 30

A Machine Learning Specialist working for an online fashion company wants to build a data ingestion

solution for the company's Amazon S3-based data lake.

The Specialist wants to create a set of ingestion mechanisms that will enable future capabilities comprised

of:

Real-time analytics

Interactive analytics of historical data

Clickstream analytics

Product recommendations

Which services should the Specialist use?

AWS Glue as the data catalog; Amazon Kinesis Data Streams and Amazon Kinesis Data Analytics for real-time data insights; Amazon Kinesis Data Firehose for delivery to Amazon ES for clickstream analytics; Amazon EMR to generate personalized product recommendations

Amazon Athena as the data catalog: Amazon Kinesis Data Streams and Amazon Kinesis Data Analytics for near-real-time data insights; Amazon Kinesis Data Firehose for clickstream analytics; AWS Glue to generate personalized product recommendations

AWS Glue as the data catalog; Amazon Kinesis Data Streams and Amazon Kinesis Data Analytics for historical data insights; Amazon Kinesis Data Firehose for delivery to Amazon ES for clickstream analytics; Amazon EMR to generate personalized product recommendations

Amazon Athena as the data catalog; Amazon Kinesis Data Streams and Amazon Kinesis Data Analytics for historical data insights; Amazon DynamoDB streams for clickstream analytics; AWS Glue to generate personalized product recommendations

28 / 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

29 / 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.

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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