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Amazon AWS Certified Machine Learning - Specialty Sample Questions (Q289-Q294):

NEW QUESTION # 289
An online reseller has a large, multi-column dataset with one column missing 30% of its data. A Machine Learning Specialist believes that certain columns in the dataset could be used to reconstruct the missing data.
Which reconstruction approach should the Specialist use to preserve the integrity of the dataset?

• A. Listwise deletion
• B. Multiple imputation
• C. Last observation carried forward
• D. Mean substitution

Answer: B

Explanation:
Explanation/Reference: https://worldwidescience.org/topicpages/i/imputing+missing+values.html

 

NEW QUESTION # 290
A company sells thousands of products on a public website and wants to automatically identify products with potential durability problems. The company has 1.000 reviews with date, star rating, review text, review summary, and customer email fields, but many reviews are incomplete and have empty fields. Each review has already been labeled with the correct durability result.
A machine learning specialist must train a model to identify reviews expressing concerns over product durability. The first model needs to be trained and ready to review in 2 days.
What is the MOST direct approach to solve this problem within 2 days?

• A. Build a recurrent neural network (RNN) in Amazon SageMaker by using Gluon and Apache MXNet.
• B. Train a custom classifier by using Amazon Comprehend.
• C. Train a built-in BlazingText model using Word2Vec mode in Amazon SageMaker.
• D. Use a built-in seq2seq model in Amazon SageMaker.

Answer: B

Explanation:
The most direct approach to solve this problem within 2 days is to train a custom classifier by using Amazon Comprehend. Amazon Comprehend is a natural language processing (NLP) service that can analyze text and extract insights such as sentiment, entities, topics, and syntax. Amazon Comprehend also provides a custom classification feature that allows users to create and train a custom text classifier using their own labeled data.
The custom classifier can then be used to categorize any text document into one or more custom classes. For this use case, the custom classifier can be trained to identify reviews that express concerns over product durability as a class, and use the star rating, review text, and review summary fields as input features. The custom classifier can be created and trained using the Amazon Comprehend console or API, and does not require any coding or machine learning expertise. The training process is fully managed and scalable, and can handle large and complex datasets. The custom classifier can be trained and ready to review in 2 days or less, depending on the size and quality of the dataset.
The other options are not the most direct approaches because:
Option B: Building a recurrent neural network (RNN) in Amazon SageMaker by using Gluon and Apache MXNet is a more complex and time-consuming approach that requires coding and machine learning skills.
RNNs are a type of deep learning models that can process sequential data, such as text, and learn long-term dependencies between tokens. Gluon is a high-level API for MXNet that simplifies the development of deep learning models. Amazon SageMaker is a fully managed service that provides tools and frameworks for building, training, and deploying machine learning models. However, to use this approach, the machine learning specialist would have to write custom code to preprocess the data, define the RNN architecture, train the model, and evaluate the results. This would likely take more than 2 days and involve more administrative overhead.
Option C: Training a built-in BlazingText model using Word2Vec mode in Amazon SageMaker is not a suitable approach for text classification. BlazingText is a built-in algorithm in Amazon SageMaker that provides highly optimized implementations of the Word2Vec and text classification algorithms. The Word2Vec algorithm is useful for generating word embeddings, which are dense vector representations of words that capture their semantic and syntactic similarities. However, word embeddings alone are not sufficient for text classification, as they do not account for the context and structure of the text documents. To use this approach, the machine learning specialist would have to combine the word embeddings with another classifier model, such as a logistic regression or a neural network, which would add more complexity and time to the solution.
Option D: Using a built-in seq2seq model in Amazon SageMaker is not a relevant approach for text classification. Seq2seq is a built-in algorithm in Amazon SageMaker that provides a sequence-to-sequence framework for neural machine translation based on MXNet. Seq2seq is a supervised learning algorithm that can generate an output sequence of tokens given an input sequence of tokens, such as translating a sentence from one language to another. However, seq2seq is not designed for text classification, which requires assigning a label or a category to a text document, not generating another text sequence. To use this approach, the machine learning specialist would have to modify the seq2seq algorithm to fit the text classification task, which would be challenging and inefficient.
Custom Classification - Amazon Comprehend
Build a Text Classification Model with Amazon Comprehend - AWS Machine Learning Blog Recurrent Neural Networks - Gluon API BlazingText Algorithm - Amazon SageMaker Sequence-to-Sequence Algorithm - Amazon SageMaker

 

NEW QUESTION # 291
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 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.
• 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 R5 instance with the same memory size as the ml.m5.4xlarge instance by using the Reserved Instance option.
• D. 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.

Answer: B

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.
References:
Amazon SageMaker Pricing
Manage Notebook Instances - Amazon SageMaker
Amazon EC2 Pricing - Reserved Instances

 

NEW QUESTION # 292
A machine learning (ML) engineer is integrating a production model with a customer metadata repository for real-time inference. The repository is hosted in Amazon SageMaker Feature Store. The engineer wants to retrieve only the latest version of the customer metadata record for a single customer at a time.
Which solution will meet these requirements?

• A. Create an Amazon Athena query to retrieve the data from the feature table.
• B. Use the SageMaker Feature Store BatchGetRecord API with the record identifier. Filter to find the latest record.
• C. Create an Amazon Athena query to retrieve the data from the feature table. Use the write_time value to find the latest record.
• D. Use the SageMaker Feature Store GetRecord API with the record identifier.

Answer: D

Explanation:
To retrieve only the latest version of a record in real-time for a single entity (like a customer), the correct method is to use the GetRecord API provided by SageMaker Feature Store.
"Use the GetRecord API to retrieve the latest feature values for a specific record identifier from the online store. This operation returns the latest version of the record only." This is optimal for low-latency, single-record lookups, such as real-time inference scenarios. BatchGetRecord is used for multiple records, and Athena queries are for offline/batch analysis and not suited for real-time access.

 

NEW QUESTION # 293
A logistics company needs a forecast model to predict next month's inventory requirements for a single item in
10 warehouses. A machine learning specialist uses Amazon Forecast to develop a forecast model from 3 years of monthly data. There is no missing data. The specialist selects the DeepAR+ algorithm to train a predictor.
The predictor means absolute percentage error (MAPE) is much larger than the MAPE produced by the current human forecasters.
Which changes to the CreatePredictor API call could improve the MAPE? (Choose two.)

• A. Set PerformHPO to true.
• B. Set ForecastFrequency to W for weekly.
• C. Set FeaturizationMethodName to filling.
• D. Set PerformAutoML to true.
• E. Set ForecastHorizon to 4.

Answer: A,D

Explanation:
Explanation
The MAPE of the predictor could be improved by making the following changes to the CreatePredictor API call:
Set PerformAutoML to true. This will allow Amazon Forecast to automatically evaluate different algorithms and choose the one that minimizes the objective function, which is the mean of the weighted losses over the forecast types. By default, these are the p10, p50, and p90 quantile losses1. This option can help find a better algorithm than DeepAR+ for the given data.
Set PerformHPO to true. This will enable hyperparameter optimization (HPO), which is the process of finding the optimal values for the algorithm-specific parameters that affect the quality of the forecasts. HPO can improve the accuracy of the predictor by tuning the hyperparameters based on the training data2.
The other options are not likely to improve the MAPE of the predictor. Setting ForecastHorizon to 4 will reduce the number of time steps that the model predicts, which may not match the business requirement of predicting next month's inventory. Setting ForecastFrequency to W for weekly will change the granularity of the forecasts, which may not be appropriate for the monthly data. Setting FeaturizationMethodName to filling will not have any effect, since there is no missing data in the dataset.
References:
CreatePredictor - Amazon Forecast
HPOConfig - Amazon Forecast

 

NEW QUESTION # 294
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