The Secret Of Amazon-Web-Services MLS-C01 Braindumps

NEW QUESTION 1
Amazon Connect has recently been tolled out across a company as a contact call center The solution has been configured to store voice call recordings on Amazon S3
The content of the voice calls are being analyzed for the incidents being discussed by the call operators Amazon Transcribe is being used to convert the audio to text, and the output is stored on Amazon S3
Which approach will provide the information required for further analysis?

• A. Use Amazon Comprehend with the transcribed files to build the key topics
• B. Use Amazon Translate with the transcribed files to train and build a model for the key topics
• C. Use the AWS Deep Learning AMI with Gluon Semantic Segmentation on the transcribed files to train and build a model for the key topics
• D. Use the Amazon SageMaker k-Nearest-Neighbors (kNN) algorithm on the transcribed files to generate a word embeddings dictionary for the key topics

Answer: B

NEW QUESTION 2
A web-based company wants to improve its conversion rate on its landing page Using a large historical dataset of customer visits, the company has repeatedly trained a multi-class deep learning network algorithm on Amazon SageMaker However there is an overfitting problem training data shows 90% accuracy in predictions, while test data shows 70% accuracy only
The company needs to boost the generalization of its model before deploying it into production to maximize conversions of visits to purchases
Which action is recommended to provide the HIGHEST accuracy model for the company's test and validation data?

• A. Increase the randomization of training data in the mini-batches used in training.
• B. Allocate a higher proportion of the overall data to the training dataset
• C. Apply L1 or L2 regularization and dropouts to the training.
• D. Reduce the number of layers and units (or neurons) from the deep learning network.

Answer: A

NEW QUESTION 3
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 4
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. AWSDMS
• B. Amazon Kinesis Data Streams
• C. Amazon Kinesis Data Firehose
• D. Amazon Kinesis Data Analytics

Answer: C

NEW QUESTION 5
A Machine Learning Specialist is implementing a full Bayesian network on a dataset that describes public transit in New York City. One of the random variables is discrete, and represents the number of minutes New Yorkers wait for a bus given that the buses cycle every 10 minutes, with a mean of 3 minutes.
Which prior probability distribution should the ML Specialist use for this variable?

• A. Poisson distribution ,
• B. Uniform distribution
• C. Normal distribution
• D. Binomial distribution

Answer: D

NEW QUESTION 6
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

Answer: C

NEW 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"?

• 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 8
An Amazon SageMaker notebook instance is launched into Amazon VPC The SageMaker notebook references data contained in an Amazon S3 bucket in another account The bucket is encrypted using SSE-KMS The instance returns an access denied error when trying to access data in Amazon S3.
Which of the following are required to access the bucket and avoid the access denied error? (Select THREE )

• A. An AWS KMS key policy that allows access to the customer master key (CMK)
• B. A SageMaker notebook security group that allows access to Amazon S3
• C. An 1AM role that allows access to the specific S3 bucket
• D. A permissive S3 bucket policy
• E. An S3 bucket owner that matches the notebook owner
• F. A SegaMaker notebook subnet ACL that allow traffic to Amazon S3.

Answer: ACF

NEW QUESTION 9
A Machine Learning Specialist is creating a new natural language processing application that processes a dataset comprised of 1 million sentences The aim is to then run Word2Vec to generate embeddings of the sentences and enable different types of predictions
Here is an example from the dataset
"The quck BROWN FOX jumps over the lazy dog "
Which of the following are the operations the Specialist needs to perform to correctly sanitize and prepare the data in a repeatable manner? (Select THREE)

• A. Perform part-of-speech tagging and keep the action verb and the nouns only
• B. Normalize all words by making the sentence lowercase
• C. Remove stop words using an English stopword dictionary.
• D. Correct the typography on "quck" to "quick."
• E. One-hot encode all words in the sentence
• F. Tokenize the sentence into words.

Answer: ABD

NEW QUESTION 10
Which of the following metrics should a Machine Learning Specialist generally use to compare/evaluate machine learning classification models against each other?

• A. Recall
• B. Misclassification rate
• C. Mean absolute percentage error (MAPE)
• D. Area Under the ROC Curve (AUC)

Answer: A

NEW QUESTION 11
A Machine Learning Specialist working for an online fashion company wants to build a data ingestion solution for the company's Amazon S3-based data lake.
The Specialist wants to create a set of ingestion mechanisms that will enable future capabilities comprised of:
• Real-time analytics
• Interactive analytics of historical data
• Clickstream analytics
• Product recommendations
Which services should the Specialist use?

• A. AWS Glue as the data dialog; Amazon Kinesis Data Streams and Amazon Kinesis Data Analytics for real-time data insights; Amazon Kinesis Data Firehose for delivery to Amazon ES for clickstream analytics; Amazon EMR to generate personalized product recommendations
• B. Amazon Athena as the data catalog; Amazon Kinesis Data Streams and Amazon Kinesis Data Analytics for near-realtime data insights; Amazon Kinesis Data Firehose for clickstream analytics; AWS Glue to generate personalized product recommendations
• C. AWS Glue as the data catalog; Amazon Kinesis Data Streams and Amazon Kinesis Data Analytics for historical data insights; Amazon Kinesis Data Firehose for delivery to Amazon ES for clickstream analytics; Amazon EMR to generate personalized product recommendations
• D. Amazon Athena as the data catalog; Amazon Kinesis Data Streams and Amazon Kinesis Data Analytics for historical data insights; Amazon DynamoDB streams for clickstream analytics; AWS Glue to generate personalized product recommendations

Answer: A

NEW QUESTION 12
A company is running an Amazon SageMaker training job that will access data stored in its Amazon S3 bucket A compliance policy requires that the data never be transmitted across the internet How should the company set up the job?

• A. Launch the notebook instances in a public subnet and access the data through the public S3 endpoint
• B. Launch the notebook instances in a private subnet and access the data through a NAT gateway
• C. Launch the notebook instances in a public subnet and access the data through a NAT gateway
• D. Launch the notebook instances in a private subnet and access the data through an S3 VPC endpoint.

Answer: D

NEW QUESTION 13
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 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
For the given confusion matrix, what is the recall and precision of the model?

• A. Recall = 0.92 Precision = 0.84
• B. Recall = 0.84 Precision = 0.8
• C. Recall = 0.92 Precision = 0.8
• D. Recall = 0.8 Precision = 0.92

Answer: A

NEW QUESTION 16
A Data Scientist wants to gain real-time insights into a data stream of GZIP files. Which solution would allow the use of SQL to query the stream with the LEAST latency?

• A. Amazon Kinesis Data Analytics with an AWS Lambda function to transform the data.
• B. AWS Glue with a custom ETL script to transform the data.
• C. An Amazon Kinesis Client Library to transform the data and save it to an Amazon ES cluster.
• D. Amazon Kinesis Data Firehose to transform the data and put it into an Amazon S3 bucket.

Answer: A

NEW QUESTION 17
A Machine Learning Specialist is building a logistic regression model that will predict whether or not a person will order a pizza. The Specialist is trying to build the optimal model with an ideal classification threshold.
What model evaluation technique should the Specialist use to understand how different classification thresholds will impact the model's performance?

• A. Receiver operating characteristic (ROC) curve
• B. Misclassification rate
• C. Root Mean Square Error (RM&)
• D. L1 norm

Answer: A

NEW QUESTION 18
A bank's Machine Learning team is developing an approach for credit card fraud detection The company has a large dataset of historical data labeled as fraudulent The goal is to build a model to take the information from new transactions and predict whether each transaction is fraudulent or not
Which built-in Amazon SageMaker machine learning algorithm should be used for modeling this problem?

• A. Seq2seq
• B. XGBoost
• C. K-means
• D. Random Cut Forest (RCF)

Answer: C

NEW QUESTION 19
A Data Scientist is developing a machine learning model to classify whether a financial transaction is fraudulent. The labeled data available for training consists of 100,000 non-fraudulent observations and 1,000 fraudulent observations.
The Data Scientist applies the XGBoost algorithm to the data, resulting in the following confusion matrix when the trained model is applied to a previously unseen validation dataset. The accuracy of the model is 99.1%, but the Data Scientist has been asked to reduce the number of false negatives.

Which combination of steps should the Data Scientist take to reduce the number of false positive predictions by the model? (Select TWO.)

• A. Change the XGBoost eval_metric parameter to optimize based on rmse instead of error.
• B. Increase the XGBoost scale_pos_weight parameter to adjust the balance of positive and negative weights.
• C. Increase the XGBoost max_depth parameter because the model is currently underfitting the data.
• D. Change the XGBoost evaljnetric parameter to optimize based on AUC instead of error.
• E. Decrease the XGBoost max_depth parameter because the model is currently overfitting the data.

Answer: DE

NEW QUESTION 20
A company has raw user and transaction data stored in AmazonS3 a MySQL database, and Amazon RedShift A Data Scientist needs to perform an analysis by joining the three datasets from Amazon S3, MySQL, and Amazon RedShift, and then calculating the average-of a few selected columns from the joined data
Which AWS service should the Data Scientist use?

• A. Amazon Athena
• B. Amazon Redshift Spectrum
• C. AWS Glue
• D. Amazon QuickSight

Answer: A

NEW 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?

• A. . m5 4xlarge (general purpose)
• B. r5.2xlarge (memory optimized)
• C. p3.2xlarge (GPU accelerated computing)
• D. p3 8xlarge (GPU accelerated computing)

Answer: C

NEW QUESTION 22
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 do?

• 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 CreateTrainingJob request body

Answer: A

NEW QUESTION 23
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:
* Must be accessible from a VPC only.
* 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.

Answer: B

NEW QUESTION 24
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