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Amazon Web Services Updated MLS-C01 Exam Questions and Answers by baxter

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Amazon Web Services MLS-C01 Exam Overview :

Exam Name: AWS Certified Machine Learning - Specialty
Exam Code: MLS-C01 Dumps
Vendor: Amazon Web Services Certification: AWS Certified Specialty
Questions: 281 Q&A's Shared By: baxter
Question 12

A Machine Learning Specialist is given a structured dataset on the shopping habits of a company’s customer

base. The dataset contains thousands of columns of data and hundreds of numerical columns for each

customer. The Specialist wants to identify whether there are natural groupings for these columns across all

customers and visualize the results as quickly as possible.

What approach should the Specialist take to accomplish these tasks?

Options:

A.

Embed the numerical features using the t-distributed stochastic neighbor embedding (t-SNE) algorithm and

create a scatter plot.

B.

Run k-means using the Euclidean distance measure for different values of k and create an elbow plot.

C.

Embed the numerical features using the t-distributed stochastic neighbor embedding (t-SNE) algorithm and

create a line graph.

D.

Run k-means using the Euclidean distance measure for different values of k and create box plots for each numerical column within each cluster.

Discussion
Question 13

A Machine Learning Specialist is designing a scalable data storage solution for Amazon SageMaker. There is an existing TensorFlow-based model implemented as a train.py script that relies on static training data that is currently stored as TFRecords.

Which method of providing training data to Amazon SageMaker would meet the business requirements with the LEAST development overhead?

Options:

A.

Use Amazon SageMaker script mode and use train.py unchanged. Point the Amazon SageMaker training invocation to the local path of the data without reformatting the training data.

B.

Use Amazon SageMaker script mode and use train.py unchanged. Put the TFRecord data into an Amazon S3 bucket. Point the Amazon SageMaker training invocation to the S3 bucket without reformatting the training data.

C.

Rewrite the train.py script to add a section that converts TFRecords to protobuf and ingests the protobuf data instead of TFRecords.

D.

Prepare the data in the format accepted by Amazon SageMaker. Use AWS Glue or AWS Lambda to reformat and store the data in an Amazon S3 bucket.

Discussion
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Question 14

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.

Discussion
Question 15

An ecommerce company sends a weekly email newsletter to all of its customers. Management has hired a team of writers to create additional targeted content. A data scientist needs to identify five customer segments based on age, income, and location. The customers’ current segmentation is unknown. The data scientist previously built an XGBoost model to predict the likelihood of a customer responding to an email based on age, income, and location.

Why does the XGBoost model NOT meet the current requirements, and how can this be fixed?

Options:

A.

The XGBoost model provides a true/false binary output. Apply principal component analysis (PCA) with five feature dimensions to predict a segment.

B.

The XGBoost model provides a true/false binary output. Increase the number of classes the XGBoost model predicts to five classes to predict a segment.

C.

The XGBoost model is a supervised machine learning algorithm. Train a k-Nearest-Neighbors (kNN) model with K = 5 on the same dataset to predict a segment.

D.

The XGBoost model is a supervised machine learning algorithm. Train a k-means model with K = 5 on the same dataset to predict a segment.

Discussion
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