What Tested MLS-C01 Test Questions Is

NEW QUESTION 1
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?

• A. Launch multiple training jobs in parallel with different hyperparameters
• B. 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
• C. Create a hyperparameter tuning job and set the accuracy as an objective metric.
• D. Create a random walk in the parameter space to iterate through a range of values that should be used for each individual hyperparameter

Answer: B

NEW QUESTION 2
A Machine Learning Specialist is using an Amazon SageMaker notebook instance in a private subnet of a corporate VPC. The ML Specialist has important data stored on the Amazon SageMaker notebook instance's Amazon EBS volume, and needs to take a snapshot of that EBS volume. However the ML Specialist cannot find the Amazon SageMaker notebook instance's EBS volume or Amazon EC2 instance within the VPC.
Why is the ML Specialist not seeing the instance visible in the VPC?

• A. Amazon SageMaker notebook instances are based on the EC2 instances within the customer account, but they run outside of VPCs.
• B. Amazon SageMaker notebook instances are based on the Amazon ECS service within customer accounts.
• C. Amazon SageMaker notebook instances are based on EC2 instances running within AWS service accounts.
• D. Amazon SageMaker notebook instances are based on AWS ECS instances running within AWS service accounts.

Answer: C

NEW QUESTION 3
A Machine Learning Specialist is required to build a supervised image-recognition model to identify a cat. The ML Specialist performs some tests and records the following results for a neural network-based image classifier:
Total number of images available = 1,000 Test set images = 100 (constant test set)
The ML Specialist notices that, in over 75% of the misclassified images, the cats were held upside down by their owners.
Which techniques can be used by the ML Specialist to improve this specific test error?

• A. Increase the training data by adding variation in rotation for training images.
• B. Increase the number of epochs for model training.
• C. Increase the number of layers for the neural network.
• D. Increase the dropout rate for the second-to-last layer.

Answer: B

NEW QUESTION 4
A company's Machine Learning Specialist needs to improve the training speed of a time-series forecasting model using TensorFlow. The training is currently implemented on a single-GPU machine and takes approximately 23 hours to complete. The training needs to be run daily.
The model accuracy js acceptable, but the company anticipates a continuous increase in the size of the training data and a need to update the model on an hourly, rather than a daily, basis. The company also wants to minimize coding effort and infrastructure changes
What should the Machine Learning Specialist do to the training solution to allow it to scale for future demand?

• A. Do not change the TensorFlow cod
• B. Change the machine to one with a more powerful GPU to speed up the training.
• C. Change the TensorFlow code to implement a Horovod distributed framework supported by Amazon SageMake
• D. Parallelize the training to as many machines as needed to achieve the business goals.
• E. Switch to using a built-in AWS SageMaker DeepAR mode
• F. Parallelize the training to as many machines as needed to achieve the business goals.
• G. Move the training to Amazon EMR and distribute the workload to as many machines as needed to achieve the business goals.

Answer: B

NEW QUESTION 5
A Machine Learning Specialist is using Amazon SageMaker to host a model for a highly available customer-facing application .
The Specialist has trained a new version of the model, validated it with historical data, and now wants to deploy it to production To limit any risk of a negative customer experience, the Specialist wants to be able to monitor the model and roll it back, if needed
What is the SIMPLEST approach with the LEAST risk to deploy the model and roll it back, if needed?

• A. Create a SageMaker endpoint and configuration for the new model versio
• B. Redirect production traffic to the new endpoint by updating the client configuratio
• C. Revert traffic to the last version if the model does not perform as expected.
• D. Create a SageMaker endpoint and configuration for the new model versio
• E. Redirect production traffic to the new endpoint by using a load balancer Revert traffic to the last version if the model does not perform as expected.
• F. Update the existing SageMaker endpoint to use a new configuration that is weighted to send 5% of the traffic to the new varian
• G. Revert traffic to the last version by resetting the weights if the model does not perform as expected.
• H. Update the existing SageMaker endpoint to use a new configuration that is weighted to send 100% of the traffic to the new variant Revert traffic to the last version by resetting the weights if the model does not perform as expected.

Answer: A

NEW QUESTION 6
Given the following confusion matrix for a movie classification model, what is the true class frequency for Romance and the predicted class frequency for Adventure?

• A. The true class frequency for Romance is 77.56% and the predicted class frequency for Adventure is 20 85%
• B. The true class frequency for Romance is 57.92% and the predicted class frequency for Adventure is 1312%
• C. The true class frequency for Romance is 0 78 and the predicted class frequency for Adventure is (0 47 - 0.32).
• D. The true class frequency for Romance is 77.56% * 0.78 and the predicted class frequency for Adventure is 20 85% ' 0.32

Answer: A

NEW QUESTION 7
An employee found a video clip with audio on a company's social media feed. The language used in the video is Spanish. English is the employee's first language, and they do not understand Spanish. The employee wants to do a sentiment analysis.
What combination of services is the MOST efficient to accomplish the task?

• A. Amazon Transcribe, Amazon Translate, and Amazon Comprehend
• B. Amazon Transcribe, Amazon Comprehend, and Amazon SageMaker seq2seq
• C. Amazon Transcribe, Amazon Translate, and Amazon SageMaker Neural Topic Model (NTM)
• D. Amazon Transcribe, Amazon Translate, and Amazon SageMaker BlazingText

Answer: C

NEW QUESTION 8
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?

• A. CSV files
• B. Parquet files
• C. Compressed JSON
• D. RecordIO

Answer: B

NEW QUESTION 9
A Machine Learning Specialist prepared the following graph displaying the results of k-means for k = [1:10]

Considering the graph, what is a reasonable selection for the optimal choice of k?

• A. 1
• B. 4
• C. 7
• D. 10

Answer: C

NEW 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

Answer: B

NEW QUESTION 11
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?

• A. The class distribution in the dataset is imbalanced
• B. Dataset shuffling is disabled
• C. The batch size is too big
• D. The learning rate is very high

Answer: D

NEW QUESTION 12
A Machine Learning Specialist is building a convolutional neural network (CNN) that will classify 10 types of animals. The Specialist has built a series of layers in a neural network that will take an input image of an animal, pass it through a series of convolutional and pooling layers, and then finally pass it through a dense and fully connected layer with 10 nodes The Specialist would like to get an output from the neural network that is a probability distribution of how likely it is that the input image belongs to each of the 10 classes
Which function will produce the desired output?

• A. Dropout
• B. Smooth L1 loss
• C. Softmax
• D. Rectified linear units (ReLU)

Answer: D

NEW 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?

• A. Use AWS Data Pipeline to transform the data and Amazon RDS to run queries.
• B. Use AWS Glue to catalogue the data and Amazon Athena to run queries.
• C. Use AWS Batch to run ETL on the data and Amazon Aurora to run the queries.
• D. Use AWS Lambda to transform the data and Amazon Kinesis Data Analytics to run queries.

Answer: B

NEW QUESTION 14
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.

Answer: D

NEW QUESTION 15
A large consumer goods manufacturer has the following products on sale
• 34 different toothpaste variants
• 48 different toothbrush variants
• 43 different mouthwash variants
The entire sales history of all these products is available in Amazon S3 Currently, the company is using custom-built autoregressive integrated moving average (ARIMA) models to forecast demand for these products The company wants to predict the demand for a new product that will soon be launched
Which solution should a Machine Learning Specialist apply?

• A. Train a custom ARIMA model to forecast demand for the new product.
• B. Train an Amazon SageMaker DeepAR algorithm to forecast demand for the new product
• C. Train an Amazon SageMaker k-means clustering algorithm to forecast demand for the new product.
• D. Train a custom XGBoost model to forecast demand for the new product

Answer: B

Explanation:
The Amazon SageMaker DeepAR forecasting algorithm is a supervised learning algorithm for forecasting scalar (one-dimensional) time series using recurrent neural networks (RNN). Classical forecasting methods, such as autoregressive integrated moving average (ARIMA) or exponential smoothing (ETS), fit a single model to each individual time series. They then use that model to extrapolate the time series into the future.

NEW QUESTION 16
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?

• A. Linear regression
• B. Classification
• C. Clustering
• D. Reinforcement learning

Answer: B

Explanation:
The goal of classification is to determine to which class or category a data point (customer in our case) belongs to. For classification problems, data scientists would use historical data with predefined target variables AKA labels (churner/non-churner) – answers that need to be predicted – to train an algorithm. With classification,
businesses can answer the following questions:
Will this customer churn or not?
Will a customer renew their subscription?
Will a user downgrade a pricing plan?
Are there any signs of unusual customer behavior?

NEW QUESTION 17
A Machine Learning Specialist is developing recommendation engine for a photography blog Given a picture, the recommendation engine should show a picture that captures similar objects The Specialist would like to create a numerical representation feature to perform nearest-neighbor searches
What actions would allow the Specialist to get relevant numerical representations?

• A. Reduce image resolution and use reduced resolution pixel values as features
• B. Use Amazon Mechanical Turk to label image content and create a one-hot representation indicating the presence of specific labels
• C. Run images through a neural network pie-trained on ImageNet, and collect the feature vectors from the penultimate layer
• D. Average colors by channel to obtain three-dimensional representations of images.

Answer: A

NEW QUESTION 18
A city wants to monitor its air quality to address the consequences of air pollution A Machine Learning Specialist needs to forecast the air quality in parts per million of contaminates for the next 2 days in the city As this is a prototype, only daily data from the last year is available
Which model is MOST likely to provide the best results in Amazon SageMaker?

• A. Use the Amazon SageMaker k-Nearest-Neighbors (kNN) algorithm on the single time series consisting of the full year of data with a predictor_type of regressor.
• B. Use Amazon SageMaker Random Cut Forest (RCF) on the single time series consisting of the full year of data.
• C. Use the Amazon SageMaker Linear Learner algorithm on the single time series consisting of the full yearof data with a predictor_type of regressor.
• D. Use the Amazon SageMaker Linear Learner algorithm on the single time series consisting of the full yearof data with a predictor_type of classifier.

Answer: C

NEW QUESTION 19
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.

Answer: B

NEW QUESTION 20
A Machine Learning Specialist needs to move and transform data in preparation for training Some of the data needs to be processed in near-real time and other data can be moved hourly There are existing Amazon EMR MapReduce jobs to clean and feature engineering to perform on the data
Which of the following services can feed data to the MapReduce jobs? (Select TWO )

• A. AWSDMS
• B. Amazon Kinesis
• C. AWS Data Pipeline
• D. Amazon Athena
• E. Amazon ES

Answer: BD

NEW QUESTION 21
A Machine Learning Specialist is using Apache Spark for pre-processing training data As part of the Spark pipeline, the Specialist wants to use Amazon SageMaker for training a model and hosting it Which of the following would the Specialist do to integrate the Spark application with SageMaker? (Select THREE )

• A. Download the AWS SDK for the Spark environment
• B. Install the SageMaker Spark library in the Spark environment.
• C. Use the appropriate estimator from the SageMaker Spark Library to train a model.
• D. Compress the training data into a ZIP file and upload it to a pre-defined Amazon S3 bucket.
• E. Use the sageMakerMode
• F. transform method to get inferences from the model hosted in SageMaker
• G. Convert the DataFrame object to a CSV file, and use the CSV file as input for obtaining inferences from SageMaker.

Answer: DEF

NEW QUESTION 22
An Machine Learning Specialist discover the following statistics while experimenting on a model.

What can the Specialist from the experiments?

• A. 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
• B. 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
• C. 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
• D. 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

Answer: C

NEW QUESTION 23
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