Amazon MLS-C01 AWS ML Specialty Exam Questions Instant Access

Question 1

[Modeling]

A company is building a new supervised classification model in an AWS environment. The company's data science team notices that the dataset has a large quantity of variables Ail the variables are numeric. The model accuracy for training and validation is low. The model's processing time is affected by high latency The data science team needs to increase the accuracy of the model and decrease the processing.

How it should the data science team do to meet these requirements?

ACreate new features and interaction variables.

BUse a principal component analysis (PCA) model.

CApply normalization on the feature set.

DUse a multiple correspondence analysis (MCA) model

Answer : B

Question 2

[Modeling]

A company is using Amazon Polly to translate plaintext documents to speech for automated company announcements However company acronyms are being mispronounced in the current documents How should a Machine Learning Specialist address this issue for future documents?

AConvert current documents to SSML with pronunciation tags

BCreate an appropriate pronunciation lexicon.

COutput speech marks to guide in pronunciation

DUse Amazon Lex to preprocess the text files for pronunciation

Answer : B

Question 3

[Data Engineering]

A data scientist is working on a forecast problem by using a dataset that consists of .csv files that are stored in Amazon S3. The files contain a timestamp variable in the following format:

March 1st, 2020, 08:14pm -

There is a hypothesis about seasonal differences in the dependent variable. This number could be higher or lower for weekdays because some days and hours present varying values, so the day of the week, month, or hour could be an important factor. As a result, the data scientist needs to transform the timestamp into weekdays, month, and day as three separate variables to conduct an analysis.

Which solution requires the LEAST operational overhead to create a new dataset with the added features?

ACreate an Amazon EMR cluster. Develop PySpark code that can read the timestamp variable as a string, transform and create the new variables, and save the dataset as a new file in Amazon S3.

BCreate a processing job in Amazon SageMaker. Develop Python code that can read the timestamp variable as a string, transform and create the new variables, and save the dataset as a new file in Amazon S3.

CCreate a new flow in Amazon SageMaker Data Wrangler. Import the S3 file, use the Featurize date/time transform to generate the new variables, and save the dataset as a new file in Amazon S3.

DCreate an AWS Glue job. Develop code that can read the timestamp variable as a string, transform and create the new variables, and save the dataset as a new file in Amazon S3.

Answer : C

Question 4

[Modeling]

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.)

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

BUse 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.

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

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

EUse 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

[Modeling]

A global bank requires a solution to predict whether customers will leave the bank and choose another bank. The bank is using a dataset to train a model to predict customer loss. The training dataset has 1,000 rows. The training dataset includes 100 instances of customers who left the bank.

A machine learning (ML) specialist is using Amazon SageMaker Data Wrangler to train a churn prediction model by using a SageMaker training job. After training, the ML specialist notices that the model returns only false results. The ML specialist must correct the model so that it returns more accurate predictions.

Which solution will meet these requirements?

AApply anomaly detection to remove outliers from the training dataset before training.

BApply Synthetic Minority Oversampling Technique (SMOTE) to the training dataset before training.

CApply normalization to the features of the training dataset before training.

DApply undersampling to the training dataset before training.

Answer : B

Question 6

[Data Engineering]

A bank has collected customer data for 10 years in CSV format. The bank stores the data in an on-premises server. A data science team wants to use Amazon SageMaker to build and train a machine learning (ML) model to predict churn probability. The team will use the historical dat

a. The data scientists want to perform data transformations quickly and to generate data insights before the team builds a model for production.

Which solution will meet these requirements with the LEAST development effort?

AUpload the data into the SageMaker Data Wrangler console directly. Perform data transformations and generate insights within Data Wrangler.

BUpload the data into an Amazon S3 bucket. Allow SageMaker to access the data that is in the bucket. Import the data from the S3 bucket into SageMaker Data Wrangler. Perform data transformations and generate insights within Data Wrangler.

CUpload the data into the SageMaker Data Wrangler console directly. Allow SageMaker and Amazon QuickSight to access the data that is in an Amazon S3 bucket. Perform data transformations in Data Wrangler and save the transformed data into a second S3 bucket. Use QuickSight to generate data insights.

DUpload the data into an Amazon S3 bucket. Allow SageMaker to access the data that is in the bucket. Import the data from the bucket into SageMaker Data Wrangler. Perform data transformations in Data Wrangler. Save the data into a second S3 bucket. Use a SageMaker Studio notebook to generate data insights.

Answer : B

Question 7

[Data Engineering]

A data scientist is using an Amazon SageMaker notebook instance and needs to securely access data stored in a specific Amazon S3 bucket.

How should the data scientist accomplish this?

AAdd an S3 bucket policy allowing GetObject, PutObject, and ListBucket permissions to the Amazon SageMaker notebook ARN as principal.

BEncrypt the objects in the S3 bucket with a custom AWS Key Management Service (AWS KMS) key that only the notebook owner has access to.

CAttach the policy to the IAM role associated with the notebook that allows GetObject, PutObject, and ListBucket operations to the specific S3 bucket.

DUse a script in a lifecycle configuration to configure the AWS CLI on the instance with an access key ID and secret.

Answer : C

Amazon AWS Certified Machine Learning - Specialty MLS-C01 AWS ML Specialty Exam Questions