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QUESTION 1
IT leadership wants Jo transition a company\\’s existing machine learning data storage environment to AWS as a
temporary ad hoc solution The company currently uses a custom software process that heavily leverages SOL as a
query language and exclusively stores generated csv documents for machine learning
The ideal state for the company would be a solution that allows it to continue to use the current workforce of SQL
experts The solution must also support the storage of csv and JSON files, and be able to query over semi-structured
data The following are high priorities for the company:
1.
Solution simplicity
2.
Fast development time
3.
Low cost
4.
High flexibility
What technologies meet the company\\’s requirements?
A. Amazon S3 and Amazon Athena
B. Amazon Redshift and AWS Glue
C. Amazon DynamoDB and DynamoDB Accelerator (DAX)
D. Amazon RDS and Amazon ES
Correct Answer: B
QUESTION 2
While reviewing the histogram for residuals on regression evaluation data a Machine Learning Specialist notices that the
residuals do not form a zero-centered bell shape as shown What does this mean?
A. The model might have prediction errors over a range of target values.
B. The dataset cannot be accurately represented using the regression model
C. There are too many variables in the model
D. The model is predicting its target values perfectly.
Correct Answer: D
QUESTION 3
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?
A. Use a proxy server at each local office and for each camera, and stream the RTSP feed to a unique Amazon Kinesis
Video Streams video stream. On each stream, use Amazon Rekognition Video and create a stream processor to detect
faces from a collection of known employees, and alert when non-employees are detected.
B. Use a proxy server at each local office and for each camera, and stream the RTSP feed to a unique Amazon Kinesis
Video Streams video stream. On each stream, use Amazon Rekognition Image to detect faces from a collection of
known employees and alert when non-employees are detected.
C. Install AWS DeepLens cameras and use the DeepLens_Kinesis_Video module to stream video to Amazon Kinesis
Video Streams for each camera. 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.
D. Install AWS DeepLens cameras and use the DeepLens_Kinesis_Video module to stream video to Amazon Kinesis
Video Streams for each camera. 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.
Correct Answer: D
Reference: https://aws.amazon.com/blogs/machine-learning/video-analytics-in-the-cloud-and-at-the-edge-with-awsdeeplens-and-kinesis-video-streams/
QUESTION 4
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?
A. 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
B. 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
C. 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
D. Send Amazon CloudWatch Logs that were generated by Amazon SageMaker lo Amazon ES and use Kibana to
query and visualize the log data.
Correct Answer: B
Reference: https://docs.aws.amazon.com/sagemaker/latest/dg/monitoring-cloudwatch.html
QUESTION 5
A Machine Learning Specialist is working with a large cybersecurity 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 to 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\\’?
A. 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
B. Ingest the data into Apache Spark Streaming using Amazon EMR. 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
C. Ingest the data and store it in Amazon S3 Use AWS Batch along with the AWS Deep Learning AMIs to train a kmeans model using TensorFlow on the data in Amazon S3.
D. 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
Correct Answer: B
QUESTION 6
A Machine Learning Specialist at a company sensitive to security is preparing a dataset for model training. The dataset
is stored in Amazon S3 and contains Personally Identifiable Information (Pll). The dataset:
1.
Must be accessible from a VPC only.
2.
Must not traverse the public internet. How can these requirements be satisfied?
A. Create a VPC endpoint and apply a bucket access policy that restricts access to the given VPC endpoint and the
VPC.
B. Create a VPC endpoint and apply a bucket access policy that allows access from the given VPC endpoint and an
Amazon EC2 instance.
C. Create a VPC endpoint and use Network Access Control Lists (NACLs) to allow traffic between only the given VPC
endpoint and an Amazon EC2 instance.
D. Create a VPC endpoint and use security groups to restrict access to the given VPC endpoint and an Amazon EC2
instance.
Correct Answer: B
Reference: https://docs.aws.amazon.com/AmazonS3/latest/dev/example-bucket-policies-vpc-endpoint.html
QUESTION 7
A Machine Learning Specialist needs to create a data repository to hold a large amount of time-based training data for a
new model. In the source system, new files are added every hour Throughout a single 24-hour period, the volume of
hourly updates will change significantly. The Specialist always wants to train on the last 24 hours of the data
Which type of data repository is the MOST cost-effective solution?
A. An Amazon EBS-backed Amazon EC2 instance with hourly directories
B. An Amazon RDS database with hourly table partitions
C. An Amazon S3 data lake with hourly object prefixes
D. An Amazon EMR cluster with hourly hive partitions on Amazon EBS volumes
Correct Answer: C
QUESTION 8
A manufacturer of car engines collects data from cars as they are being driven The data collected includes a timestamp,
engine temperature, rotations per minute (RPM), and other sensor readings The company wants to predict when an engine is going to have a problem so it can notify drivers in advance to get engine maintenance The engine data is
loaded into a data lake for training
Which is the MOST suitable predictive model that can be deployed into production\\’?
A. Add labels over time to indicate which engine faults occur at what time in the future to turn this into a supervised
learning problem Use a recurrent neural network (RNN) to train the model to recognize when an engine might need
maintenance for a certain fault.
B. This data requires an unsupervised learning algorithm Use Amazon SageMaker k-means to cluster the data
C. Add labels over time to indicate which engine faults occur at what time in the future to turn this into a supervised
learning problem Use a convolutional neural network (CNN) to train the model to recognize when an engine might need
maintenance for a certain fault.
D. This data is already formulated as a time series Use Amazon SageMaker seq2seq to model the time series.
Correct Answer: B
QUESTION 9
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?
A. Bundle the NVIDIA drivers with the Docker image
B. Build the Docker container to be NVIDIA-Docker compatible
C. Organize the Docker container\\’s file structure to execute on GPU instances.
D. Set the GPU flag in the Amazon SageMaker Create TrainingJob request body
Correct Answer: A
QUESTION 10
A manufacturing company asks its Machine Learning Specialist to develop a model that classifies defective parts into
one of eight defect types. The company has provided roughly 100000 images per defect type for training During the injial training of the image classification model the Specialist notices that the validation accuracy is 80%, while the
training accuracy is 90% It is known that human-level performance for this type of image classification is around 90%
What should the Specialist consider to fix this issue1?
A. A longer training time
B. Making the network larger
C. Using a different optimizer
D. Using some form of regularization
Correct Answer: D
QUESTION 11
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?
A. Convert the records to Apache Parquet format
B. Convert the records to JSON format
C. Convert the records to GZIP CSV format
D. Convert the records to XML format
Correct Answer: A
Using compressions will reduce the amount of data scanned by Amazon Athena, and also reduce your S3 bucket
storage. It\\’s a Win-Win for your AWS bill. Supported formats: GZIP, LZO, SNAPPY (Parquet), and ZLIB. Reference:
https://www.cloudforecast.io/blog/using-parquet-on-athena-to-save-money-on-aws/
QUESTION 12
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?
A. K-means clustering
B. Random Cut Forest (RCF)
C. XGBoost
D. BlazingText
Correct Answer: A
QUESTION 13
A Machine Learning Specialist needs to be able to ingest streaming data and store it in Apache Parquet files for
exploration and analysis. Which of the following services would both ingest and store this data in the correct format?
A. AWS DMS
B. Amazon Kinesis Data Streams
C. Amazon Kinesis Data Firehose
D. Amazon Kinesis Data Analytics
Correct Answer: C
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