Amazon MLS-C01 AWS ML Specialty Exam Questions Instant Access

Question 1

[Data Engineering]

A developer at a retail company is creating a daily demand forecasting model. The company stores the historical hourly demand data in an Amazon S3 bucket. However, the historical data does not include demand data for some hours.

The developer wants to verify that an autoregressive integrated moving average (ARIMA) approach will be a suitable model for the use case.

How should the developer verify the suitability of an ARIMA approach?

AUse Amazon SageMaker Data Wrangler. Import the data from Amazon S3. Impute hourly missing data. Perform a Seasonal Trend decomposition.

BUse Amazon SageMaker Autopilot. Create a new experiment that specifies the S3 data location. Choose ARIMA as the machine learning (ML) problem. Check the model performance.

CUse Amazon SageMaker Data Wrangler. Import the data from Amazon S3. Resample data by using the aggregate daily total. Perform a Seasonal Trend decomposition.

DUse Amazon SageMaker Autopilot. Create a new experiment that specifies the S3 data location. Impute missing hourly values. Choose ARIMA as the machine learning (ML) problem. Check the model performance.

Answer : A

Question 2

[Modeling]

A car company is developing a machine learning solution to detect whether a car is present in an image. The image dataset consists of one million images. Each image in the dataset is 200 pixels in height by 200 pixels in width. Each image is labeled as either having a car or not having a car.

Which architecture is MOST likely to produce a model that detects whether a car is present in an image with the highest accuracy?

AUse a deep convolutional neural network (CNN) classifier with the images as input. Include a linear output layer that outputs the probability that an image contains a car.

BUse a deep convolutional neural network (CNN) classifier with the images as input. Include a softmax output layer that outputs the probability that an image contains a car.

CUse a deep multilayer perceptron (MLP) classifier with the images as input. Include a linear output layer that outputs the probability that an image contains a car.

DUse a deep multilayer perceptron (MLP) classifier with the images as input. Include a softmax output layer that outputs the probability that an image contains a car.

Answer : A

Question 3

[Modeling]

A wildlife research company has a set of images of lions and cheetahs. The company created a dataset of the images. The company labeled each image with a binary label that indicates whether an image contains a lion or cheetah. The company wants to train a model to identify whether new images contain a lion or cheetah.

.... Dh Amazon SageMaker algorithm will meet this requirement?

AXGBoost

BImage Classification - TensorFlow

CObject Detection - TensorFlow

DSemantic segmentation - MXNet

Answer : B

Question 4

[Data Engineering]

A data scientist receives a new dataset in .csv format and stores the dataset in Amazon S3. The data scientist will use this dataset to train a machine learning (ML) model.

The data scientist first needs to identify any potential data quality issues in the dataset. The data scientist must identify values that are missing or values that are not valid. The data scientist must also identify the number of outliers in the dataset.

Which solution will meet these requirements with the LEAST operational effort?)

ACreate an AWS Glue job to transform the data from .csv format to Apache Parquet format. Use an AWS Glue crawler and Amazon Athena with appropriate SQL queries to retrieve the required information.

BLeave the dataset in .csv format. Use an AWS Glue crawler and Amazon Athena with appropriate SQL queries to retrieve the required information.

CCreate an AWS Glue job to transform the data from .csv format to Apache Parquet format. Import the data into Amazon SageMaker Data Wrangler. Use the Data Quality and Insights Report to retrieve the required information.

DLeave the dataset in .csv format. Import the data into Amazon SageMaker Data Wrangler. Use the Data Quality and Insights Report to retrieve the required information.

Answer : D

Question 5

[Modeling]

For the given confusion matrix, what is the recall and precision of the model?

ARecall = 0.92 Precision = 0.84

BRecall = 0.84 Precision = 0.8

CRecall = 0.92 Precision = 0.8

DRecall = 0.8 Precision = 0.92

Answer : C

Question 6

[Modeling]

An ecommerce company wants to train a large image classification model with 10.000 classes. The company runs multiple model training iterations and needs to minimize operational overhead and cost. The company also needs to avoid loss of work and model retraining.

Which solution will meet these requirements?

ACreate the training jobs as AWS Batch jobs that use Amazon EC2 Spot Instances in a managed compute environment.

BUse Amazon EC2 Spot Instances to run the training jobs. Use a Spot Instance interruption notice to save a snapshot of the model to Amazon S3 before an instance is terminated.

CUse AWS Lambda to run the training jobs. Save model weights to Amazon S3.

DUse managed spot training in Amazon SageMaker. Launch the training jobs with checkpointing enabled.

Answer : D

Question 7

[Exploratory Data Analysis]

A machine learning (ML) specialist must develop a classification model for a financial services company. A domain expert provides the dataset, which is tabular with 10,000 rows and 1,020 features. During exploratory data analysis, the specialist finds no missing values and a small percentage of duplicate rows. There are correlation scores of > 0.9 for 200 feature pairs. The mean value of each feature is similar to its 50th percentile.

Which feature engineering strategy should the ML specialist use with Amazon SageMaker?

AApply dimensionality reduction by using the principal component analysis (PCA) algorithm.

BDrop the features with low correlation scores by using a Jupyter notebook.

CApply anomaly detection by using the Random Cut Forest (RCF) algorithm.

DConcatenate the features with high correlation scores by using a Jupyter notebook.

Answer : A

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