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To qualify for the Amazon MLS-C01 exam, candidates must have at least one year of experience in developing and deploying machine learning models on the AWS platform. They should have a strong understanding of machine learning algorithms, data preparation, and model optimization techniques. Additionally, candidates should be proficient in Python programming language and have experience with AWS services such as Amazon S3, AWS Lambda, and AWS CloudFormation.
Amazon MLS-C01 Exam consists of 65 multiple-choice and multiple-response questions that must be completed within 170 minutes. MLS-C01 exam is available in English, Japanese, Korean, and Simplified Chinese. Candidates who pass the exam will receive the AWS Certified Machine Learning - Specialty certification, which is valid for three years.
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Amazon AWS Certified Machine Learning - Specialty Sample Questions (Q196-Q201):
NEW QUESTION # 196
A manufacturer of car engines collects data from cars as they are being driven The data collected includes timestamp, engine temperature, rotations per minute (RPM), and other sensor readings The company wants to predict when an engine is going to have a problem so it can notify drivers in advance to get engine maintenance The engine data is loaded into a data lake for training Which is the MOST suitable predictive model that can be deployed into production'?
- A. Add labels over time to indicate which engine faults occur at what time in the future to turn this into a supervised learning problem Use a convolutional neural network (CNN) to train the model to recognize when an engine might need maintenance for a certain fault.
- B. This data is already formulated as a time series Use Amazon SageMaker seq2seq to model the time series.
- C. Add labels over time to indicate which engine faults occur at what time in the future to turn this into a supervised learning problem Use a recurrent neural network (RNN) to train the model to recognize when an engine might need maintenance for a certain fault.
- D. This data requires an unsupervised learning algorithm Use Amazon SageMaker k-means to cluster the data
Answer: C
Explanation:
A recurrent neural network (RNN) is a type of neural network that can process sequential data, such as time series, by maintaining a hidden state that captures the temporal dependencies between the inputs. RNNs are well suited for predicting future events based on past observations, such as forecasting engine failures based on sensor readings. To train an RNN model, the data needs to be labeled with the target variable, which in this case is the type and time of the engine fault. This makes the problem a supervised learning problem, where the goal is to learn a mapping from the input sequence (sensor readings) to the output sequence (engine faults). By using an RNN model, the manufacturer can leverage the temporal information in the data and detect patterns that indicate when an engine might need maintenance for a certain fault.
References:
Recurrent Neural Networks - Amazon SageMaker
Use Amazon SageMaker Built-in Algorithms or Pre-trained Models
Recurrent Neural Network Definition | DeepAI
What are Recurrent Neural Networks? An Ultimate Guide for Newbies!
Lee and Carter go Machine Learning: Recurrent Neural Networks - SSRN
NEW QUESTION # 197
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. Increase the XGBoost scale_pos_weight parameter to adjust the balance of positive and negative weights.
- B. Increase the XGBoost max_depth parameter because the model is currently underfitting the data.
- C. Change the XGBoost eval_metric parameter to optimize based on rmse instead of error.
- 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: A,D
Explanation:
* The XGBoost algorithm is a popular machine learning technique for classification problems. It is based on the idea of boosting, which is to combine many weak learners (decision trees) into a strong learner (ensemble model).
* The XGBoost algorithm can handle imbalanced data by using the scale_pos_weight parameter, which controls the balance of positive and negative weights in the objective function. A typical value to consider is the ratio of negative cases to positive cases in the data. By increasing this parameter, the algorithm will pay more attention to the minority class (positive) and reduce the number of false negatives.
* The XGBoost algorithm can also use different evaluation metrics to optimize the model performance.
The default metric is error, which is the misclassification rate. However, this metric can be misleading for imbalanced data, as it does not account for the different costs of false positives and false negatives.
A better metric to use is AUC, which is the area under the receiver operating characteristic (ROC) curve. The ROC curve plots the true positive rate against the false positive rate for different threshold values. The AUC measures how well the model can distinguish between the two classes, regardless of the threshold. By changing the eval_metric parameter to AUC, the algorithm will try to maximize the AUC score and reduce the number of false negatives.
* Therefore, the combination of steps that should be taken to reduce the number of false negatives are to increase the scale_pos_weight parameter and change the eval_metric parameter to AUC.
References:
* XGBoost Parameters
* XGBoost for Imbalanced Classification
NEW QUESTION # 198
A Machine Learning Specialist has completed a proof of concept for a company using a small data sample and now the Specialist is ready to implement an end-to-end solution in AWS using Amazon SageMaker The historical training data is stored in Amazon RDS Which approach should the Specialist use for training a model using that data?
- A. Write a direct connection to the SQL database within the notebook and pull data in
- B. Move the data to Amazon ElastiCache using AWS DMS and set up a connection within the notebook to pull data in for fast access.
- C. Push the data from Microsoft SQL Server to Amazon S3 using an AWS Data Pipeline and provide the S3 location within the notebook.
- D. Move the data to Amazon DynamoDB and set up a connection to DynamoDB within the notebook to pull data in
Answer: C
Explanation:
Pushing the data from Microsoft SQL Server to Amazon S3 using an AWS Data Pipeline and providing the S3 location within the notebook is the best approach for training a model using the data stored in Amazon RDS. This is because Amazon SageMaker can directly access data from Amazon S3 and train models on it. AWS Data Pipeline is a service that can automate the movement and transformation of data between different AWS services. It can also use Amazon RDS as a data source and Amazon S3 as a data destination. This way, the data can be transferred efficiently and securely without writing any code within the notebook. References:
Amazon SageMaker
AWS Data Pipeline
NEW QUESTION # 199
A finance company needs to forecast the price of a commodity. The company has compiled a dataset of historical daily prices. A data scientist must train various forecasting models on 80% of the dataset and must validate the efficacy of those models on the remaining 20% of the dataset.
What should the data scientist split the dataset into a training dataset and a validation dataset to compare model performance?
- A. Pick a date so that 80% to the data points precede the date Assign that group of data points as the training dataset. Assign all the remaining data points to the validation dataset.
- B. Sample data points randomly without replacement so that 80% of the data points are in the training dataset. Assign all the remaining data points to the validation dataset.
- C. Starting from the earliest date in the dataset. pick eight data points for the training dataset and two data points for the validation dataset. Repeat this stratified sampling until no data points remain.
- D. Pick a date so that 80% of the data points occur after the date. Assign that group of data points as the training dataset. Assign all the remaining data points to the validation dataset.
Answer: A
Explanation:
A Comprehensive Explanation: The best way to split the dataset into a training dataset and a validation dataset is to pick a date so that 80% of the data points precede the date and assign that group of data points as the training dataset. This method preserves the temporal order of the data and ensures that the validation dataset reflects the most recent trends and patterns in the commodity price. This is important for forecasting models that rely on time series analysis and sequential data. The other methods would either introduce bias or lose information by ignoring the temporal structure of the data.
References:
Time Series Forecasting - Amazon SageMaker
Time Series Splitting - scikit-learn
Time Series Forecasting - Towards Data Science
NEW QUESTION # 200
A company supplies wholesale clothing to thousands of retail stores. A data scientist must create a model that predicts the daily sales volume for each item for each store. The data scientist discovers that more than half of the stores have been in business for less than 6 months. Sales data is highly consistent from week to week. Daily data from the database has been aggregated weekly, and weeks with no sales are omitted from the current dataset. Five years (100 MB) of sales data is available in Amazon S3.
Which factors will adversely impact the performance of the forecast model to be developed, and which actions should the data scientist take to mitigate them? (Choose two.)
- A. Detecting seasonality for the majority of stores will be an issue. Request categorical data to relate new stores with similar stores that have more historical data.
- B. Only 100 MB of sales data is available in Amazon S3. Request 10 years of sales data, which would provide 200 MB of training data for the model.
- C. The sales data does not have enough variance. Request external sales data from other industries to improve the model's ability to generalize.
- D. Sales data is aggregated by week. Request daily sales data from the source database to enable building a daily model.
- E. The sales data is missing zero entries for item sales. Request that item sales data from the source database include zero entries to enable building the model.
Answer: D,E
Explanation:
The factors that will adversely impact the performance of the forecast model are:
Sales data is aggregated by week. This will reduce the granularity and resolution of the data, and make it harder to capture the daily patterns and variations in sales volume. The data scientist should request daily sales data from the source database to enable building a daily model, which will be more accurate and useful for the prediction task.
Sales data is missing zero entries for item sales. This will introduce bias and incompleteness in the data, and make it difficult to account for the items that have no demand or are out of stock. The data scientist should request that item sales data from the source database include zero entries to enable building the model, which will be more robust and realistic.
The other options are not valid because:
Detecting seasonality for the majority of stores will not be an issue, as sales data is highly consistent from week to week. Requesting categorical data to relate new stores with similar stores that have more historical data may not improve the model performance significantly, and may introduce unnecessary complexity and noise.
The sales data does not need to have more variance, as it reflects the actual demand and behavior of the customers. Requesting external sales data from other industries will not improve the model's ability to generalize, but may introduce irrelevant and misleading information.
Only 100 MB of sales data is not a problem, as it is sufficient to train a forecast model with Amazon S3 and Amazon Forecast. Requesting 10 years of sales data will not provide much benefit, as it may contain outdated and obsolete information that does not reflect the current market trends and customer preferences.
References:
Amazon Forecast
Forecasting: Principles and Practice
NEW QUESTION # 201
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