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

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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: 330 Q&A's Shared By: cezar
Question 28

A social media company wants to develop a machine learning (ML) model to detect Inappropriate or offensive content in images. The company has collected a large dataset of labeled images and plans to use the built-in Amazon SageMaker image classification algorithm to train the model. The company also intends to use SageMaker pipe mode to speed up the training.

...company splits the dataset into training, validation, and testing datasets. The company stores the training and validation images in folders that are named Training and Validation, respectively. The folder ...ain subfolders that correspond to the names of the dataset classes. The company resizes the images to the same sue and generates two input manifest files named training.1st and validation.1st, for the ..ing dataset and the validation dataset. respectively. Finally, the company creates two separate Amazon S3 buckets for uploads of the training dataset and the validation dataset.

...h additional data preparation steps should the company take before uploading the files to Amazon S3?

Options:

A.

Generate two Apache Parquet files, training.parquet and validation.parquet. by reading the images into a Pandas data frame and storing the data frame as a Parquet file. Upload the Parquet files to the training S3 bucket

B.

Compress the training and validation directories by using the Snappy compression library Upload the manifest and compressed files to the training S3 bucket

C.

Compress the training and validation directories by using the gzip compression library. Upload the manifest and compressed files to the training S3 bucket.

D.

Generate two RecordIO files, training rec and validation.rec. from the manifest files by using the im2rec Apache MXNet utility tool. Upload the RecordlO files to the training S3 bucket.

Discussion
Question 29

A machine learning (ML) developer for an online retailer recently uploaded a sales dataset into Amazon SageMaker Studio. The ML developer wants to obtain importance scores for each feature of the dataset. The ML developer will use the importance scores to feature engineer the dataset.

Which solution will meet this requirement with the LEAST development effort?

Options:

A.

Use SageMaker Data Wrangler to perform a Gini importance score analysis.

B.

Use a SageMaker notebook instance to perform principal component analysis (PCA).

C.

Use a SageMaker notebook instance to perform a singular value decomposition analysis.

D.

Use the multicollinearity feature to perform a lasso feature selection to perform an importance scores analysis.

Discussion
Question 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?

Options:

A.

Decision tree

B.

Linear support vector machine (SVM)

C.

Naive Bayesian classifier

D.

Single Perceptron with sigmoidal activation function

Discussion
Question 31

A company builds computer-vision models that use deep learning for the autonomous vehicle industry. A machine learning (ML) specialist uses an Amazon EC2 instance that has a CPU: GPU ratio of 12:1 to train the models.

The ML specialist examines the instance metric logs and notices that the GPU is idle half of the time The ML specialist must reduce training costs without increasing the duration of the training jobs.

Which solution will meet these requirements?

Options:

A.

Switch to an instance type that has only CPUs.

B.

Use a heterogeneous cluster that has two different instances groups.

C.

Use memory-optimized EC2 Spot Instances for the training jobs.

D.

Switch to an instance type that has a CPU GPU ratio of 6:1.

Discussion
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