01 August, 2020

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Total 105 questions Full Exam Access

Question 1

A Machine Learning Specialist is configuring Amazon SageMaker so multiple Data Scientists can access notebooks, train models, and deploy endpoints. To ensure the best operational performance, the Specialist needs to be able to track how often the Scientists are deploying models, GPU and CPU utilization on the deployed SageMaker endpoints, and all errors that are generated when an endpoint is invoked.
Which services are integrated with Amazon SageMaker to track this information? (Select TWO.)

AWS CloudTrail

AWS Health

AWS Trusted Advisor

Amazon CloudWatch

AWS Config

Question 2

An office security agency conducted a successful pilot using 100 cameras installed at key locations within the main office. Images from the cameras were uploaded to Amazon S3 and tagged using Amazon Rekognition, and the results were stored in Amazon ES. The agency is now looking to expand the pilot into a full production system using thousands of video cameras in its office locations globally. The goal is to identify activities performed by non-employees in real time.
Which solution should the agency consider?

Use a proxy server at each local office and for each camera, and stream the RTSP feed to a uniqueAmazon Kinesis Video Streams video strea

On each stream, use Amazon Rekognition Video and createa stream processor to detect faces from a collection of known employees, and alert when non-employees are detected.

Use a proxy server at each local office and for each camera, and stream the RTSP feed to a uniqueAmazon Kinesis Video Streams video strea

On each stream, use Amazon Rekognition Image to detectfaces from a collection of known employees and alert when non-employees are detected.

Install AWS DeepLens cameras and use the DeepLens_Kinesis_Video module to stream video to Amazon Kinesis Video Streams for each camer

On each stream, use Amazon Rekognition Video and create a stream processor to detect faces from a collection on each stream, and alert when nonemployees are detected.

Install AWS DeepLens cameras and use the DeepLens_Kinesis_Video module to stream video to Amazon Kinesis Video Streams for each camer

On each stream, run an AWS Lambda function to capture image fragments and then call Amazon Rekognition Image to detect faces from a collection of known employees, and alert when non-employees are detected.

Question 3

A Machine Learning Specialist built an image classification deep learning model. However the Specialist ran into an overfitting problem in which the training and testing accuracies were 99% and 75%r respectively.
How should the Specialist address this issue and what is the reason behind it?

The learning rate should be increased because the optimization process was trapped at a local minimum.

The dropout rate at the flatten layer should be increased because the model is not generalized enough.

The dimensionality of dense layer next to the flatten layer should be increased because the model is not complex enough.

The epoch number should be increased because the optimization process was terminated before it reached the global minimum.

Question 4

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 do1?

Bundle the NVIDIA drivers with the Docker image

Build the Docker container to be NVIDIA-Docker compatible

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

Set the GPU flag in the Amazon SageMaker Create TrainingJob request body

Question 5

While working on a neural network project, a Machine Learning Specialist discovers thai some features in the data have very high magnitude resulting in this data being weighted more in the cost function What should the Specialist do to ensure better convergence during backpropagation?

Dimensionality reduction

Data normalization

Model regulanzation

Data augmentation for the minority class

Question 6

A Machine Learning Specialist is preparing data for training on Amazon SageMaker The Specialist is transformed into a numpy .array, which appears to be negatively affecting the speed of the training
What should the Specialist do to optimize the data for training on SageMaker'?

Use the SageMaker batch transform feature to transform the training data into a DataFrame

Use AWS Glue to compress the data into the Apache Parquet format

Transform the dataset into the Recordio protobuf format

Use the SageMaker hyperparameter optimization feature to automatically optimize the data

Question 7

A Machine Learning Specialist is building a model that will perform time series forecasting using Amazon SageMaker The Specialist has finished training the model and is now planning to perform load testing on the endpoint so they can configure Auto Scaling for the model variant
Which approach will allow the Specialist to review the latency, memory utilization, and CPU utilization during the load test"?

Review SageMaker logs that have been written to Amazon S3 by leveraging Amazon Athena and Amazon OuickSight to visualize logs as they are being produced

Generate an Amazon CloudWatch dashboard to create a single view for the latency, memory utilization,and CPU utilization metrics that are outputted by Amazon SageMaker

Build custom Amazon CloudWatch Logs and then leverage Amazon ES and Kibana to query and visualize the data as it is generated by Amazon SageMaker

Send Amazon CloudWatch Logs that were generated by Amazon SageMaker lo Amazon ES and use Kibana to query and visualize the log data.

Question 8

A Machine Learning Specialist was given a dataset consisting of unlabeled data The Specialist must create a model that can help the team classify the data into different buckets What model should be used to complete this work?

K-means clustering

Random Cut Forest (RCF)

XGBoost

BlazingText

Question 9

A Data Science team is designing a dataset repository where it will store a large amount of training data commonly used in its machine learning models. As Data Scientists may create an arbitrary number of new datasets every day the solution has to scale automatically and be cost-effective. Also, it must be possible to explore the data using SQL.
Which storage scheme is MOST adapted to this scenario?

Store datasets as files in Amazon S3.

Store datasets as files in an Amazon EBS volume attached to an Amazon EC2 instance.

Store datasets as tables in a multi-node Amazon Redshift cluster.

Store datasets as global tables in Amazon DynamoDB.

Question 10

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?

CSV files

Parquet files

Compressed JSON

RecordIO

Question 11

A Machine Learning Specialist is developing a daily ETL workflow containing multiple ETL jobs The workflow consists of the following processes
* Start the workflow as soon as data is uploaded to Amazon S3
* When all the datasets are available in Amazon S3, start an ETL job to join the uploaded datasets with multiple terabyte-sized datasets already stored in Amazon S3
* Store the results of joining datasets in Amazon S3
* If one of the jobs fails, send a notification to the Administrator Which configuration will meet these requirements?

Use AWS Lambda to trigger an AWS Step Functions workflow to wait for dataset uploads to complete in Amazon S3. Use AWS Glue to join the datasets Use an Amazon CloudWatch alarm to send an SNS notification to the Administrator in the case of a failure

Develop the ETL workflow using AWS Lambda to start an Amazon SageMaker notebook instance Use a lifecycle configuration script to join the datasets and persist the results in Amazon S3 Use an Amazon CloudWatch alarm to send an SNS notification to the Administrator in the case of a failure

Develop the ETL workflow using AWS Batch to trigger the start of ETL jobs when data is uploaded to Amazon S3 Use AWS Glue to join the datasets in Amazon S3 Use an Amazon CloudWatch alarm to send an SNS notification to the Administrator in the case of a failure

Use AWS Lambda to chain other Lambda functions to read and join the datasets in Amazon S3 as soon as the data is uploaded to Amazon S3 Use an Amazon CloudWatch alarm to send an SNS notification to the Administrator in the case of a failure

Question 12

A Machine Learning Specialist has built a model using Amazon SageMaker built-in algorithms and is not getting expected accurate results The Specialist wants to use hyperparameter optimization to increase the model's accuracy
Which method is the MOST repeatable and requires the LEAST amount of effort to achieve this?

Launch multiple training jobs in parallel with different hyperparameters

Create an AWS Step Functions workflow that monitors the accuracy in Amazon CloudWatch Logs and relaunches the training job with a defined list of hyperparameters

Create a hyperparameter tuning job and set the accuracy as an objective metric.

Create a random walk in the parameter space to iterate through a range of values that should be used for each individual hyperparameter

Question 13

A manufacturing company has structured and unstructured data stored in an Amazon S3 bucket. A Machine Learning Specialist wants to use SQL to run queries on this data.
Which solution requires the LEAST effort to be able to query this data?

Use AWS Data Pipeline to transform the data and Amazon RDS to run queries.

Use AWS Glue to catalogue the data and Amazon Athena to run queries.

Use AWS Batch to run ETL on the data and Amazon Aurora to run the queries.

Use AWS Lambda to transform the data and Amazon Kinesis Data Analytics to run queries.

Question 14

A Data Engineer needs to build a model using a dataset containing customer credit card information.
How can the Data Engineer ensure the data remains encrypted and the credit card information is secure? Use a custom encryption algorithm to encrypt the data and store the data on an Amazon SageMaker instance in a VPC. Use the SageMaker DeepAR algorithm to randomize the credit card numbers.

Use an IAM policy to encrypt the data on the Amazon S3 bucket and Amazon Kinesis to automatically discard credit card numbers and insert fake credit card numbers.

Use an Amazon SageMaker launch configuration to encrypt the data once it is copied to the SageMaker instance in a VP

Use the SageMaker principal component analysis (PCA) algorithm to reduce the length of the credit card numbers.

Use AWS KMS to encrypt the data on Amazon S3

Question 15

A Machine Learning Specialist wants to determine the appropriate SageMakerVariant Invocations Per Instance setting for an endpoint automatic scaling configuration. The Specialist has performed a load test on a single instance and determined that peak requests per second (RPS) without service degradation is about 20 RPS As this is the first deployment, the Specialist intends to set the invocation safety factor to 0 5
Based on the stated parameters and given that the invocations per instance setting is measured on a per-minute basis, what should the Specialist set as the sageMakervariantinvocationsPerinstance setting?

600

2,400

Question 16

A Machine Learning Specialist must build out a process to query a dataset on Amazon S3 using Amazon Athena The dataset contains more than 800.000 records stored as plaintext CSV files Each record contains 200 columns and is approximately 1 5 MB in size Most queries will span 5 to 10 columns only
How should the Machine Learning Specialist transform the dataset to minimize query runtime?

Convert the records to Apache Parquet format

Convert the records to JSON format

Convert the records to GZIP CSV format

Convert the records to XML format

Question 17

A Machine Learning Specialist is building a prediction model for a large number of features using linear models, such as linear regression and logistic regression During exploratory data analysis the Specialist observes that many features are highly correlated with each other This may make the model unstable
What should be done to reduce the impact of having such a large number of features?

Perform one-hot encoding on highly correlated features

Use matrix multiplication on highly correlated features.

Create a new feature space using principal component analysis (PCA)

Apply the Pearson correlation coefficient

Question 18

A Machine Learning Specialist is working with a large cybersecurily company that manages security events in real time for companies around the world The cybersecurity company wants to design a solution that will allow it to use machine learning to score malicious events as anomalies on the data as it is being ingested The company also wants be able to save the results in its data lake for later processing and analysis
What is the MOST efficient way to accomplish these tasks'?

Ingest the data using Amazon Kinesis Data Firehose, and use Amazon Kinesis Data Analytics Random Cut Forest (RCF) for anomaly detection Then use Kinesis Data Firehose to stream the results to Amazon S3

Ingest the data into Apache Spark Streaming using Amazon EM

and use Spark MLlib with k-means to perform anomaly detection Then store the results in an Apache Hadoop Distributed File System (HDFS) using Amazon EMR with a replication factor of three as the data lake

Ingest the data and store it in Amazon S3 Use AWS Batch along with the AWS Deep Learning AMIs to train a k-means model using TensorFlow on the data in Amazon S3.

Ingest the data and store it in Amazon S3. Have an AWS Glue job that is triggered on demand transform the new data Then use the built-in Random Cut Forest (RCF) model within Amazon SageMaker to detect anomalies in the data

Question 19

For the given confusion matrix, what is the recall and precision of the model?
MLS-C01 dumps exhibit

Recall = 0.92 Precision = 0.84

Recall = 0.84 Precision = 0.8

Recall = 0.92 Precision = 0.8

Recall = 0.8 Precision = 0.92

Question 20

An Machine Learning Specialist discover the following statistics while experimenting on a model.
MLS-C01 dumps exhibit

What can the Specialist from the experiments?

The model In Experiment 1 had a high variance error lhat was reduced in Experiment 3 by regularization Experiment 2 shows that there is minimal bias error in Experiment 1

The model in Experiment 1 had a high bias error that was reduced in Experiment 3 by regularization Experiment 2 shows that there is minimal variance error in Experiment 1

The model in Experiment 1 had a high bias error and a high variance error that were reduced in Experiment 3 by regularization Experiment 2 shows thai high bias cannot be reduced by increasing layers and neurons in the model

The model in Experiment 1 had a high random noise error that was reduced in Expenment 3 by regularization Expenment 2 shows that random noise cannot be reduced by increasing layers and neurons in the model

Question 21

An e-commerce company needs a customized training model to classify images of its shirts and pants products The company needs a proof of concept in 2 to 3 days with good accuracy Which compute choice should the Machine Learning Specialist select to train and achieve good accuracy on the model quickly?

. m5 4xlarge (general purpose)

r5.2xlarge (memory optimized)

p3.2xlarge (GPU accelerated computing)

p3 8xlarge (GPU accelerated computing)

Question 22

A Machine Learning Specialist is designing a system for improving sales for a company. The objective is to use the large amount of information the company has on users' behavior and product preferences to predict which products users would like based on the users' similarity to other users.
What should the Specialist do to meet this objective?

Build a content-based filtering recommendation engine with Apache Spark ML on Amazon EMR.

Build a collaborative filtering recommendation engine with Apache Spark ML on Amazon EMR.

Build a model-based filtering recommendation engine with Apache Spark ML on Amazon EMR.

Build a combinative filtering recommendation engine with Apache Spark ML on Amazon EMR.

Question 23

A Machine Learning Specialist is working with a large company to leverage machine learning within its products. The company wants to group its customers into categories based on which customers will and will not churn within the next 6 months. The company has labeled the data available to the Specialist.
Which machine learning model type should the Specialist use to accomplish this task?

Linear regression

Classification

Clustering

Reinforcement learning

Question 24

An insurance company is developing a new device for vehicles that uses a camera to observe drivers' behavior and alert them when they appear distracted The company created approximately 10,000 training images in a controlled environment that a Machine Learning Specialist will use to train and evaluate machine learning models
During the model evaluation the Specialist notices that the training error rate diminishes faster as the number of epochs increases and the model is not accurately inferring on the unseen test images
Which of the following should be used to resolve this issue? (Select TWO)

Add vanishing gradient to the model

Perform data augmentation on the training data

Make the neural network architecture complex.

Use gradient checking in the model

Add L2 regularization to the model

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Total 105 questions Full Exam Access