Amazon MLS-C01 AWS ML Specialty Exam Practice Test Instant Access

Question 1

[Data Engineering]

During mini-batch training of a neural network for a classification problem, a Data Scientist notices that training accuracy oscillates What is the MOST likely cause of this issue?

AThe class distribution in the dataset is imbalanced

BDataset shuffling is disabled

CThe batch size is too big

DThe learning rate is very high

Answer : D

Question 2

[Modeling]

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?

ABundle the NVIDIA drivers with the Docker image.

BBuild the Docker container to be NVIDIA-Docker compatible.

COrganize the Docker container's file structure to execute on GPU instances.

DSet the GPU flag in the Amazon SageMaker CreateTrainingJob request body

Answer : B

Question 3

[Modeling]

A university wants to develop a targeted recruitment strategy to increase new student enrollment. A data scientist gathers information about the academic performance history of students. The data scientist wants to use the data to build student profiles. The university will use the profiles to direct resources to recruit students who are likely to enroll in the university.

Which combination of steps should the data scientist take to predict whether a particular student applicant is likely to enroll in the university? (Select TWO)

AUse Amazon SageMaker Ground Truth to sort the data into two groups named 'enrolled' or 'not enrolled.'

BUse a forecasting algorithm to run predictions.

CUse a regression algorithm to run predictions.

DUse a classification algorithm to run predictions

EUse the built-in Amazon SageMaker k-means algorithm to cluster the data into two groups named 'enrolled' or 'not enrolled.'

Answer : A, D

Question 4

[Modeling]

A financial company is trying to detect credit card fraud. The company observed that, on average, 2% of credit card transactions were fraudulent. A data scientist trained a classifier on a year's worth of credit card transactions dat

a. The model needs to identify the fraudulent transactions (positives) from the regular ones (negatives). The company's goal is to accurately capture as many positives as possible.

Which metrics should the data scientist use to optimize the model? (Choose two.)

ASpecificity

BFalse positive rate

CAccuracy

DArea under the precision-recall curve

ETrue positive rate

Answer : D, E

Question 5

[Data Engineering]

A monitoring service generates 1 TB of scale metrics record data every minute A Research team performs queries on this data using Amazon Athena The queries run slowly due to the large volume of data, and the team requires better performance

How should the records be stored in Amazon S3 to improve query performance?

ACSV files

BParquet files

CCompressed JSON

DRecordIO

Answer : B

Question 6

[Data Engineering]

A company has a podcast platform that has thousands of users. The company implemented an algorithm to detect low podcast engagement based on a 10-minute running window of user events such as listening to. pausing, and closing the podcast. A machine learning (ML) specialist is designing the ingestion process for these events. The ML specialist needs to transform the data to prepare the data for inference.

How should the ML specialist design the transformation step to meet these requirements with the LEAST operational effort?

AUse an Amazon Managed Streaming for Apache Kafka (Amazon MSK) cluster to ingest event data. Use Amazon Managed Service for Apache Flink (previously known as Amazon Kinesis Data Analytics) to transform the most recent 10 minutes of data before inference.

BUse Amazon Kinesis Data Streams to ingest event data. Store the data in Amazon S3 by using Amazon Data Firehose. Use AWS Lambda to transform the most recent 10 minutes of data before inference.

CUse Amazon Kinesis Data Streams to ingest event data. Use Amazon Managed Service for Apache Flink (previously known as Amazon Kinesis Data Analytics) to transform the most recent 10 minutes of data before inference.

DUse an Amazon Managed Streaming for Apache Kafka (Amazon MSK) cluster to ingest event data. Use AWS Lambda to transform the most recent 10 minutes of data before inference.

Answer : C

Question 7

[Data Engineering]

A company offers an online shopping service to its customers. The company wants to enhance the site's security by requesting additional information when customers access the site from locations that are different from their normal location. The company wants to update the process to call a machine learning (ML) model to determine when additional information should be requested.

The company has several terabytes of data from its existing ecommerce web servers containing the source IP addresses for each request made to the web server. For authenticated requests, the records also contain the login name of the requesting user.

Which approach should an ML specialist take to implement the new security feature in the web application?

AUse Amazon SageMaker Ground Truth to label each record as either a successful or failed access attempt. Use Amazon SageMaker to train a binary classification model using the factorization machines (FM) algorithm.

BUse Amazon SageMaker to train a model using the IP Insights algorithm. Schedule updates and retraining of the model using new log data nightly.

CUse Amazon SageMaker Ground Truth to label each record as either a successful or failed access attempt. Use Amazon SageMaker to train a binary classification model using the IP Insights algorithm.

DUse Amazon SageMaker to train a model using the Object2Vec algorithm. Schedule updates and retraining of the model using new log data nightly.

Answer : B

Amazon AWS Certified Machine Learning - Specialty MLS-C01 AWS ML Specialty Exam Practice Test