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Amazon AWS Certified Machine Learning Specialty Exam is a test designed to evaluate an individual's ability to build, deploy, and design machine learning solutions on AWS. AWS Certified Machine Learning - Specialty certification is ideal for individuals who want to pursue a career in the field of machine learning and AI engineering. Having this certification can help professionals distinguish themselves in a highly competitive job market and make them stand out to potential employers.

Amazon MLS-C01 (AWS Certified Machine Learning - Specialty) certification exam is a specialized certification exam designed to test the knowledge, skills, and expertise of candidates in the field of machine learning. AWS Certified Machine Learning - Specialty certification is offered by Amazon Web Services and is designed to validate the candidates' ability to design, build, and deploy machine learning models on AWS.

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Amazon AWS Certified Machine Learning - Specialty Sample Questions (Q246-Q251):

NEW QUESTION # 246
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? (Choose two.)

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

Answer: C,D

NEW QUESTION # 247
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. Change the TensorFlow code to implement a Horovod distributed framework supported by Amazon SageMaker. Parallelize the training to as many machines as needed to achieve the business goals.
B. Do not change the TensorFlow code. Change the machine to one with a more powerful GPU to speed up the training.
C. Switch to using a built-in AWS SageMaker DeepAR model. Parallelize the training to as many machines as needed to achieve the business goals.
D. Move the training to Amazon EMR and distribute the workload to as many machines as needed to achieve the business goals.

Answer: A

NEW QUESTION # 248
A Machine Learning Specialist observes several performance problems with the training portion of a machine learning solution on Amazon SageMaker The solution uses a large training dataset 2 TB in size and is using the SageMaker k-means algorithm The observed issues include the unacceptable length of time it takes before the training job launches and poor I/O throughput while training the model What should the Specialist do to address the performance issues with the current solution?

A. Copy the training dataset to an Amazon EFS volume mounted on the SageMaker instance.
B. Compress the training data into Apache Parquet format.
C. Ensure that the input mode for the training job is set to Pipe.
D. Use the SageMaker batch transform feature

Answer: B

NEW QUESTION # 249
A company is using Amazon Polly to translate plaintext documents to speech for automated company announcements However company acronyms are being mispronounced in the current documents How should a Machine Learning Specialist address this issue for future documents'?

A. Convert current documents to SSML with pronunciation tags
B. Create an appropriate pronunciation lexicon.
C. Output speech marks to guide in pronunciation
D. Use Amazon Lex to preprocess the text files for pronunciation

Answer: A

NEW QUESTION # 250
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. 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.
B. 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.
C. 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.
D. 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.

Answer: D

Explanation:
Explanation
The solution that the agency should consider is to 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.
This solution has the following advantages:
It can handle thousands of video cameras in real time, as Amazon Kinesis Video Streams can scale elastically to support any number of producers and consumers1.
It can leverage the Amazon Rekognition Video API, which is designed and optimized for video analysis, and can detect faces in challenging conditions such as low lighting, occlusions, and different poses2.
It can use a stream processor, which is a feature of Amazon Rekognition Video that allows you to create a persistent application that analyzes streaming video and stores the results in a Kinesis data stream3. The stream processor can compare the detected faces with a collection of known employees, which is a container for persisting faces that you want to search for in the input video stream4. The stream processor can also send notifications to Amazon Simple Notification Service (Amazon SNS) when non-employees are detected, which can trigger downstream actions such as sending alerts or storing the events in Amazon Elasticsearch Service (Amazon ES)3.
References:
1: What Is Amazon Kinesis Video Streams? - Amazon Kinesis Video Streams
2: Detecting and Analyzing Faces - Amazon Rekognition
3: Using Amazon Rekognition Video Stream Processor - Amazon Rekognition
4: Working with Stored Faces - Amazon Rekognition

NEW QUESTION # 251
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