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Amazon MLS-C01 exam is a certification exam offered by Amazon Web Services (AWS) for individuals who want to demonstrate their expertise in machine learning. AWS-Certified-Machine-Learning-Specialty Exam is intended for individuals who have a deep understanding and practical experience in designing and implementing machine learning solutions using AWS services.
Amazon AWS Certified Machine Learning - Specialty Sample Questions (Q207-Q212):NEW QUESTION # 207
A manufacturing company needs to identify returned smartphones that have been damaged by moisture. The company has an automated process that produces 2.000 diagnostic values for each phone. The database contains more than five million phone evaluations. The evaluation process is consistent, and there are no missing values in the data. A machine learning (ML) specialist has trained an Amazon SageMaker linear learner ML model to classify phones as moisture damaged or not moisture damaged by using all available features. The model's F1 score is 0.6.
What changes in model training would MOST likely improve the model's F1 score? (Select TWO.)

• A. Use the SageMaker k-nearest neighbors (k-NN) algorithm. Set a dimension reduction target of less than
  1,000 to train the model.
• B. Use the SageMaker k-means algorithm with k of less than 1.000 to train the model
• C. Continue to use the SageMaker linear learner algorithm. Set the predictor type to regressor.
• D. Continue to use the SageMaker linear learner algorithm. Reduce the number of features with the SageMaker principal component analysis (PCA) algorithm.
• E. Continue to use the SageMaker linear learner algorithm. Reduce the number of features with the scikit- iearn multi-dimensional scaling (MDS) algorithm.
  

Answer: A,D
Explanation:
* Option A is correct because reducing the number of features with the SageMaker PCA algorithm can help remove noise and redundancy from the data, and improve the model's performance. PCA is a dimensionality reduction technique that transforms the original features into a smaller set of linearly uncorrelated features called principal components. The SageMaker linear learner algorithm supports PCA as a built-in feature transformation option.
* Option E is correct because using the SageMaker k-NN algorithm with a dimension reduction target of less than 1,000 can help the model learn from the similarity of the data points, and improve the model's performance. k-NN is a non-parametric algorithm that classifies an input based on the majority vote of its k nearest neighbors in the feature space. The SageMaker k-NN algorithm supports dimension reduction as a built-in feature transformation option.
* Option B is incorrect because using the scikit-learn MDS algorithm to reduce the number of features is not a feasible option, as MDS is a computationally expensive technique that does not scale well to large datasets. MDS is a dimensionality reduction technique that tries to preserve the pairwise distances between the original data points in a lower-dimensional space.
* Option C is incorrect because setting the predictor type to regressor would change the model's objective from classification to regression, which is not suitable for the given problem. A regressor model would output a continuous value instead of a binary label for each phone.
* Option D is incorrect because using the SageMaker k-means algorithm with k of less than 1,000 would not help the model classify the phones, as k-means is a clustering algorithm that groups the data points into k clusters based on their similarity, without using any labels. A clustering model would not output a binary label for each phone.
Amazon SageMaker Linear Learner Algorithm
Amazon SageMaker K-Nearest Neighbors (k-NN) Algorithm
[Principal Component Analysis - Scikit-learn]
[Multidimensional Scaling - Scikit-learn]

NEW QUESTION # 208
A Machine Learning Specialist is designing a scalable data storage solution for Amazon SageMaker. There is an existing TensorFlow-based model implemented as a train.py script that relies on static training data that is currently stored as TFRecords.
Which method of providing training data to Amazon SageMaker would meet the business requirements with the LEAST development overhead?

• A. Use Amazon SageMaker script mode and use train.py unchanged. Point the Amazon SageMaker training invocation to the local path of the data without reformatting the training data.
• B. Prepare the data in the format accepted by Amazon SageMaker. Use AWS Glue or AWS Lambda to reformat and store the data in an Amazon S3 bucket.
• C. Rewrite the train.py script to add a section that converts TFRecords to protobuf and ingests the protobuf data instead of TFRecords.
• D. Use Amazon SageMaker script mode and use train.py unchanged. Put the TFRecord data into an Amazon S3 bucket. Point the Amazon SageMaker training invocation to the S3 bucket without reformatting the training data.
  

Answer: D
Explanation:
https://github.com/aws-samples/a ... e-pipeline/train.py

NEW QUESTION # 209
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. Output speech marks to guide in pronunciation
• B. Use Amazon Lex to preprocess the text files for pronunciation
• C. Convert current documents to SSML with pronunciation tags
• D. Create an appropriate pronunciation lexicon.
  

Answer: D
Explanation:
A pronunciation lexicon is a file that defines how words or phrases should be pronounced by Amazon Polly. A lexicon can help customize the speech output for words that are uncommon, foreign, or have multiple pronunciations. A lexicon must conform to the Pronunciation Lexicon Specification (PLS) standard and can be stored in an AWS region using the Amazon Polly API. To use a lexicon for synthesizing speech, the lexicon name must be specified in the <speak> SSML tag. For example, the following lexicon defines how to pronounce the acronym W3C:
<lexicon version="1.0" xmlns="http://www.w3.org/2005/01/pronunciation-lexicon" alphabet="ipa" xml:lang="en-US"> <lexeme> <grapheme>W3C</grapheme> <alias>World Wide Web Consortium</alias> </lexeme> </lexicon> To use this lexicon, the text input must include the following SSML tag:
<speak version="1.1" xmlns="http://www.w3.org/2001/10/synthesis" xml:lang="en-US"> <voice name="Joanna"> <lexicon name="w3c_lexicon"/> The <say-as interpret-as="characters">W3C</say-as> is an international community that develops open standards to ensure the long-term growth of the Web. </voice> </speak> References:
Customize pronunciation using lexicons in Amazon Polly: A blog post that explains how to use lexicons for creating custom pronunciations.
Managing Lexicons: A documentation page that describes how to store and retrieve lexicons using the Amazon Polly API.

NEW QUESTION # 210
A company is building a demand forecasting model based on machine learning (ML). In the development stage, an ML specialist uses an Amazon SageMaker notebook to perform feature engineering during work hours that consumes low amounts of CPU and memory resources. A data engineer uses the same notebook to perform data preprocessing once a day on average that requires very high memory and completes in only 2 hours. The data preprocessing is not configured to use GPU. All the processes are running well on an ml.m5.
4xlarge notebook instance.
The company receives an AWS Budgets alert that the billing for this month exceeds the allocated budget.
Which solution will result in the MOST cost savings?

• A. Keep the notebook instance type and size the same. Stop the notebook when it is not in use. Run data preprocessing on a P3 instance type with the same memory as the ml.m5.4xlarge instance by using Amazon SageMaker Processing.
• B. Change the notebook instance type to a memory optimized instance with the same vCPU number as the ml.m5.4xlarge instance has. Stop the notebook when it is not in use. Run both data preprocessing and feature engineering development on that instance.
• C. Change the notebook instance type to a smaller general-purpose instance. Stop the notebook when it is not in use. Run data preprocessing on an ml. r5 instance with the same memory size as the ml.m5.
  4xlarge instance by using Amazon SageMaker Processing.
• D. Change the notebook instance type to a smaller general-purpose instance. Stop the notebook when it is not in use. Run data preprocessing on an R5 instance with the same memory size as the ml.m5.4xlarge instance by using the Reserved Instance option.
  

Answer: C
Explanation:
The best solution to reduce the cost of the notebook instance and the data preprocessing job is to change the notebook instance type to a smaller general-purpose instance, stop the notebook when it is not in use, and run data preprocessing on an ml.r5 instance with the same memory size as the ml.m5.4xlarge instance by using Amazon SageMaker Processing. This solution will result in the most cost savings because:
* Changing the notebook instance type to a smaller general-purpose instance will reduce the hourly cost of running the notebook, since the feature engineering development does not require high CPU and memory resources. For example, an ml.t3.medium instance costs $0.0464 per hour, while an ml.m5.
4xlarge instance costs $0.888 per hour1.
* Stopping the notebook when it is not in use will also reduce the cost, since the notebook will only incur charges when it is running. For example, if the notebook is used for 8 hours per day, 5 days per week, then stopping it when it is not in use will save about 76% of the monthly cost compared to leaving it running all the time2.
* Running data preprocessing on an ml.r5 instance with the same memory size as the ml.m5.4xlarge instance by using Amazon SageMaker Processing will reduce the cost of the data preprocessing job, since the ml.r5 instance is optimized for memory-intensive workloads and has a lower cost per GB of memory than the ml.m5 instance. For example, an ml.r5.4xlarge instance has 128 GB of memory and costs $1.008 per hour, while an ml.m5.4xlarge instance has 64 GB of memory and costs $0.888 per hour1. Therefore, the ml.r5.4xlarge instance can process the same amount of data in half the time and at a lower cost than the ml.m5.4xlarge instance. Moreover, using Amazon SageMaker Processing will allow the data preprocessing job to run on a separate, fully managed infrastructure that can be scaled up or down as needed, without affecting the notebook instance.
The other options are not as effective as option C for the following reasons:
* Option A is not optimal because changing the notebook instance type to a memory optimized instance with the same vCPU number as the ml.m5.4xlarge instance has will not reduce the cost of the notebook, since the memory optimized instances have a higher cost per vCPU than the general-purpose instances. For example, an ml.r5.4xlarge instance has 16 vCPUs and costs $1.008 per hour, while an ml.
m5.4xlarge instance has 16 vCPUs and costs $0.888 per hour1. Moreover, running both data preprocessing and feature engineering development on the same instance will not take advantage of the scalability and flexibility of Amazon SageMaker Processing.
* Option B is not suitable because running data preprocessing on a P3 instance type with the same memory as the ml.m5.4xlarge instance by using Amazon SageMaker Processing will not reduce the cost of the data preprocessing job, since the P3 instance type is optimized for GPU-based workloads and has a higher cost per GB of memory than the ml.m5 or ml.r5 instance types. For example, an ml.p3.
2xlarge instance has 61 GB of memory and costs $3.06 per hour, while an ml.m5.4xlarge instance has
64 GB of memory and costs $0.888 per hour1. Moreover, the data preprocessing job does not require GPU, so using a P3 instance type will be wasteful and inefficient.
* Option D is not feasible because running data preprocessing on an R5 instance with the same memory size as the ml.m5.4xlarge instance by using the Reserved Instance option will not reduce the cost of the data preprocessing job, since the Reserved Instance option requires a commitment to a consistent amount of usage for a period of 1 or 3 years3. However, the data preprocessing job only runs once a day on average and completes in only 2 hours, so it does not have a consistent or predictable usage pattern.
Therefore, using the Reserved Instance option will not provide any cost savings and may incur additional charges for unused capacity.
Amazon SageMaker Pricing
Manage Notebook Instances - Amazon SageMaker
Amazon EC2 Pricing - Reserved Instances

NEW QUESTION # 211
A retail company is using Amazon Personalize to provide personalized product recommendations for its customers during a marketing campaign. The company sees a significant increase in sales of recommended items to existing customers immediately after deploying a new solution version, but these sales decrease a short time after deployment. Only historical data from before the marketing campaign is available for training.
How should a data scientist adjust the solution?

• A. Add event type and event value fields to the interactions dataset in Amazon Personalize.
• B. Use the event tracker in Amazon Personalize to include real-time user interactions.
• C. Implement a new solution using the built-in factorization machines (FM) algorithm in Amazon SageMaker.
• D. Add user metadata and use the HRNN-Metadata recipe in Amazon Personalize.
  

Answer: B
Explanation:
The best option is to use the event tracker in Amazon Personalize to include real-time user interactions. This will allow the model to learn from the feedback of the customers during the marketing campaign and adjust the recommendations accordingly. The event tracker can capture click-through, add-to-cart, purchase, and other types of events that indicate the user's preferences. By using the event tracker, the company can improve the relevance and freshness of the recommendations and avoid the decrease in sales.
The other options are not as effective as using the event tracker. Adding user metadata and using the HRNN- Metadata recipe in Amazon Personalize can help capture the user's attributes and preferences, but it will not reflect the changes in user behavior during the marketing campaign. Implementing a new solution using the built-in factorization machines (FM) algorithm in Amazon SageMaker can also provide personalized recommendations, but it will require more time and effort to train and deploy the model. Adding event type and event value fields to the interactions dataset in Amazon Personalize can help capture the importance and context of each interaction, but it will not update the model with the latest user feedback.
Recording events - Amazon Personalize
Using real-time events - Amazon Personalize

NEW QUESTION # 212
......
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