AWS-Certified-Machine-Learning-Specialty Amazon Exam Questions and Free Practice Test

Question 7

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?

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

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

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

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

Correct Answer:A

Question 8

A machine learning specialist stores IoT soil sensor data in Amazon DynamoDB table and stores weather event data as JSON files in Amazon S3. The dataset in DynamoDB is 10 GB in size and the dataset in Amazon S3 is 5 GB in size. The specialist wants to train a model on this data to help predict soil moisture levels as a function of weather events using Amazon SageMaker.
Which solution will accomplish the necessary transformation to train the Amazon SageMaker model with the LEAST amount of administrative overhead?

A. Launch an Amazon EMR cluste

B. Create an Apache Hive external table for the DynamoDB table and S3 dat

C. Join the Hive tables and write the results out to Amazon S3.

D. Crawl the data using AWS Glue crawler

E. Write an AWS Glue ETL job that merges the two tables and writes the output to an Amazon Redshift cluster.

F. Enable Amazon DynamoDB Streams on the sensor tabl

G. Write an AWS Lambda function that consumes the stream and appends the results to the existing weather files in Amazon S3.

H. Crawl the data using AWS Glue crawler

I. Write an AWS Glue ETL job that merges the two tables and writes the output in CSV format to Amazon S3.

Correct Answer:C

Question 9

A company supplies wholesale clothing to thousands of retail stores. A data scientist must create a model that predicts the daily sales volume for each item for each store. The data scientist discovers that more than half of the stores have been in business for less than 6 months. Sales data is highly consistent from week to week. Daily data from the database has been aggregated weekly, and weeks with no sales are omitted from the current dataset. Five years (100 MB) of sales data is available in Amazon S3.
Which factors will adversely impact the performance of the forecast model to be developed, and which actions should the data scientist take to mitigate them? (Choose two.)

A. Detecting seasonality for the majority of stores will be an issu

B. Request categorical data to relate new stores with similar stores that have more historical data.

C. The sales data does not have enough varianc

D. Request external sales data from other industries to improve the model's ability to generalize.

E. Sales data is aggregated by wee

F. Request daily sales data from the source database to enable building a daily model.

G. The sales data is missing zero entries for item sale

H. Request that item sales data from the source database include zero entries to enable building the model.

I. Only 100 MB of sales data is available in Amazon S3. Request 10 years of sales data, which would provide 200 MB of training data for the model.

Correct Answer:AB

Question 10

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'?

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 EM

C. 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

D. 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.

E. 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:A

Question 11

A manufacturer is operating a large number of factories with a complex supply chain relationship where unexpected downtime of a machine can cause production to stop at several factories. A data scientist wants to analyze sensor data from the factories to identify equipment in need of preemptive maintenance and then dispatch a service team to prevent unplanned downtime. The sensor readings from a single machine can include up to 200 data points including temperatures, voltages, vibrations, RPMs, and pressure readings.
To collect this sensor data, the manufacturer deployed Wi-Fi and LANs across the factories. Even though many factory locations do not have reliable or high-speed internet connectivity, the manufacturer would like to maintain near-real-time inference capabilities.
Which deployment architecture for the model will address these business requirements?

A. Deploy the model in Amazon SageMake

B. Run sensor data through this model to predict which machinesneed maintenance.

C. Deploy the model on AWS IoT Greengrass in each factor

D. Run sensor data through this model to infer which machines need maintenance.

E. Deploy the model to an Amazon SageMaker batch transformation jo

F. Generate inferences in a daily batch report to identify machines that need maintenance.

G. Deploy the model in Amazon SageMaker and use an IoT rule to write data to an Amazon DynamoDB table.Consume a DynamoDB stream from the table with an AWS Lambda function to invoke the endpoint.

Correct Answer:B
https://aws.amazon.com/blogs/iot/industrial-iot-from-condition-based-monitoring-to-predictive-quality-to-digitiz https://aws.amazon.com/blogs/iot/using-aws-iot-for-predictive-maintenance/

Question 12

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?

A. Store datasets as files in Amazon S3.

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

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

D. Store datasets as global tables in Amazon DynamoDB.

Correct Answer:A

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