Question 1

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?

Options :

A : Create one-hot word encoding vectors.

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

C : Create word embedding factors that store edit distance with every other word.

D : Download word embedding-s pre-trained on a large corpus.

Answer: D

Question 2

An agricultural company is interested in using machine learning to detect specific types of weeds in a 100-acre grassland field. Currently, the company uses tractor-mounted cameras to capture multiple images of the field as 10 Ã— 10 grids. The company also has a large training dataset that consists of annotated images of popular weed classes like broadleaf and non-broadleaf docks. The company wants to build a weed detection model that will detect specific types of weeds and the location of each type within the field. Once the model is ready, it will be hosted on Amazon SageMaker endpoints. The model will perform real-time inferencing using the images captured by the cameras. Which approach should a Machine Learning Specialist take to obtain accurate predictions?

Options :

A :

Prepare the images in RecordIO format and upload them to Amazon S3. Use Amazon SageMaker to train, test, and validate the model using an image classification algorithm to categorize images into various weed classes.

B :

Prepare the images in Apache Parquet format and upload them to Amazon S3. Use Amazon SageMaker to train, test, and validate the model using an object-detection single-shot multibox detector (SSD) algorithm.

C :

Prepare the images in RecordIO format and upload them to Amazon S3. Use Amazon SageMaker to train, test, and validate the model using an object-detection single-shot multibox detector (SSD) algorithm.

D :

Prepare the images in Apache Parquet format and upload them to Amazon S3. Use Amazon SageMaker to train, test, and validate the model using an image classification algorithm to categorize images into various weed classes.

Answer: C

Question 3

A data engineer wants to perform exploratory data analysis (EDA) on a petabyte of data. The data engineer does not want to manage compute resources and wants to pay only for queries that are run. The data engineer must write the analysis by using Python from a Jupyter notebook.

Which solution will meet these requirements?

Options :

A : Use Apache Spark from within Amazon Athena.

B : Use Apache Spark from within Amazon SageMaker.

C : Use Apache Spark from within an Amazon EMR cluster.

D : Use Apache Spark through an integration with Amazon Redshift.

Answer: B

Question 4

A machine learning specialist needs to analyze comments on a news website with users across the globe. The specialist must find the most discussed topics in the comments that are in either English or Spanish. What steps could be used to accomplish this task? (Choose two.)

Options :

A : Use an Amazon SageMaker BlazingText algorithm to find the topics independently from language. Proceed with the analysis.

B : Use an Amazon SageMaker seq2seq algorithm to translate from Spanish to English, if necessary. Use a SageMaker Latent Dirichlet Allocation (LDA) algorithm to find the topics.

C : Use Amazon Translate to translate from Spanish to English, if necessary. Use Amazon Comprehend topic modeling to find the topics.

D : Use Amazon Translate to translate from Spanish to English, if necessary. Use Amazon Lex to extract topics form the content.

E : Use Amazon Translate to translate from Spanish to English, if necessary. Use Amazon SageMaker Neural Topic Model (NTM) to find the topics.

Answer: C,E

Question 5

A trucking company is collecting live image data from its fleet of trucks across the globe. The data is growing

rapidly and approximately 100 GB of new data is generated every day. The company wants to explore

machine learning uses cases while ensuring the data is only accessible to specific IAM users.

Which storage option provides the most processing flexibility and will allow access control with IAM?

Options :

A :

Use a database, such as Amazon DynamoDB, to store the images, and set the IAM policies to restrict

access to only the desired IAM users.

B :

Use an Amazon S3-backed data lake to store the raw images, and set up the permissions using bucket

policies.

C :

Setup up Amazon EMR with Hadoop Distributed File System (HDFS) to store the files, and restrict access

to the EMR instances using IAM policies.

D :

Configure Amazon EFS with IAM policies to make the data available to Amazon EC2 instances owned by

the IAM users.

Answer: B

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