Amazon Amazon Certifications MLS-C01 Questions & Answers

• Question 361:

  A Machine Learning Specialist is building a model to predict future employment rates based on a wide range of economic factors While exploring the data, the Specialist notices that the magnitude of the input features vary greatly The Specialist does not want variables with a larger magnitude to dominate the model

  What should the Specialist do to prepare the data for model training'?

  A. Apply quantile binning to group the data into categorical bins to keep any relationships in the data by replacing the magnitude with distribution

  B. Apply the Cartesian product transformation to create new combinations of fields that are independent of the magnitude

  C. Apply normalization to ensure each field will have a mean of 0 and a variance of 1 to remove any significant magnitude

  D. Apply the orthogonal sparse Diagram (OSB) transformation to apply a fixed-size sliding window to generate new features of a similar magnitude.

  Correct Answer: C

  Reference: https://docs.aws.amazon.com/machine-learning/latest/dg/data-transformations-reference.html

• Question 362:

  While reviewing the histogram for residuals on regression evaluation data a Machine Learning Specialist notices that the residuals do not form a zero-centered bell shape as shown. What does this mean?

  

  A. The model might have prediction errors over a range of target values.

  B. The dataset cannot be accurately represented using the regression model

  C. There are too many variables in the model

  D. The model is predicting its target values perfectly.

  Correct Answer: D

• Question 363:

  A company is observing low accuracy while training on the default built-in image classification algorithm in Amazon SageMaker. The Data Science team wants to use an Inception neural network architecture instead of a ResNet architecture. Which of the following will accomplish this? (Select TWO.)

  A. Customize the built-in image classification algorithm to use Inception and use this for model training.

  B. Create a support case with the SageMaker team to change the default image classification algorithm to Inception.

  C. Bundle a Docker container with TensorFlow Estimator loaded with an Inception network and use this for model training.

  D. Use custom code in Amazon SageMaker with TensorFlow Estimator to load the model with an Inception network and use this for model training.

  E. Download and apt-get install the inception network code into an Amazon EC2 instance and use this instance as a Jupyter notebook in Amazon SageMaker.

  Correct Answer: CD

• Question 364:

  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.

  Correct Answer: A

  Kinesis Data Analytics can use lamda to convert GZIP and can run SQL on the converted data. https://aws.amazon.com/about-aws/whats-new/2017/10/amazon-kinesis-analytics-can-now-pre-process-data-prior-to-running-sql-queries/

• Question 365:

  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.

  Correct Answer: B

  https://keras.io/layers/core/#dropout

  Overfitting occurs when a model is too complex and memorizes the training data instead of learning the underlying pattern. As a result, the model performs well on the training data but poorly on new, unseen data. Increasing the dropout rate, a regularization technique, can help combat overfitting by randomly dropping out some neurons during training, which prevents the model from relying too heavily on any single feature.

• Question 366:

  A retail company intends to use machine learning to categorize new products A labeled dataset of current products was provided to the Data Science team The dataset includes 1 200 products The labeled dataset has 15 features for each product such as title dimensions, weight, and price Each product is labeled as belonging to one of six categories such as books, games, electronics, and movies.

  Which model should be used for categorizing new products using the provided dataset for training?

  A. An XGBoost model where the objective parameter is set to multi: softmax

  B. A deep convolutional neural network (CNN) with a softmax activation function for the last layer

  C. A regression forest where the number of trees is set equal to the number of product categories

  D. A DeepAR forecasting model based on a recurrent neural network (RNN)

  Correct Answer: A

  The XGBoost algorithm is a popular and effective technique for multi-class classification. The objective parameter can be set to multi:softmax, which uses a softmax objective function for multi-class classification. This will train the model to

  predict the probability of each product belonging to each category, and the most probable category will be chosen as the final prediction.

  A deep convolutional neural network (CNN) (B) is a powerful technique commonly used for image recognition tasks. However, it is less appropriate for tabular data like the dataset provided.

  https://medium.com/@gabrielziegler3/multiclass-multilabel-classification-with-xgboost-66195e4d9f2d

• Question 367:

  A Machine Learning Specialist deployed a model that provides product recommendations on a company's website Initially, the model was performing very well and resulted in customers buying more products on average However within the past few months the Specialist has noticed that the effect of product recommendations has diminished and customers are starting to return to their original habits of spending less The Specialist is unsure of what happened, as the model has not changed from its initial deployment over a year ago Which method should the Specialist try to improve model performance?

  A. The model needs to be completely re-engineered because it is unable to handle product inventory changes

  B. The model's hyperparameters should be periodically updated to prevent drift

  C. The model should be periodically retrained from scratch using the original data while adding a regularization term to handle product inventory changes

  D. The model should be periodically retrained using the original training data plus new data as product inventory changes

  Correct Answer: D

• Question 368:

  A Marketing Manager at a pet insurance company plans to launch a targeted marketing campaign on social media to acquire new customers Currently, the company has the following data in Amazon Aurora

  1.

  Profiles for all past and existing customers

  2.

  Profiles for all past and existing insured pets

  3.

  Policy-level information

  4.

  Premiums received

  5.

  Claims paid

  What steps should be taken to implement a machine learning model to identify potential new customers on social media?

  A. Use regression on customer profile data to understand key characteristics of consumer segments Find similar profiles on social media.

  B. Use clustering on customer profile data to understand key characteristics of consumer segments Find similar profiles on social media.

  C. Use a recommendation engine on customer profile data to understand key characteristics of consumer segments. Find similar profiles on social media

  D. Use a decision tree classifier engine on customer profile data to understand key characteristics of consumer segments. Find similar profiles on social media

  Correct Answer: B

  All of the questions in the preceding examples rely on having example data that includes answers. There are times that you don't need, or can't get, example data with answers. This is true for problems whose answers identify groups. For example:

  "I want to group current and prospective customers into 10 groups based on their attributes. How should I group them? " You might choose to send the mailing to customers in the group that has the highest percentage of current customers. That is, prospective customers that most resemble current customers based on the same set of attributes. For this type of question, Amazon SageMaker provides the K-Means Algorithm.

  https://docs.aws.amazon.com/sagemaker/latest/dg/algos.html

  Clustering algorithms are unsupervised. In unsupervised learning, labels that might be associated with the objects in the training dataset aren't used. https://docs.aws.amazon.com/sagemaker/latest/dg/algo-kmeans-tech-notes.html

• Question 369:

  An agency collects census information within a country to determine healthcare and social program needs by province and city. The census form collects responses for approximately 500 questions from each citizen Which combination of algorithms would provide the appropriate insights? (Select TWO )

  A. The factorization machines (FM) algorithm

  B. The Latent Dirichlet Allocation (LDA) algorithm

  C. The principal component analysis (PCA) algorithm

  D. The k-means algorithm

  E. The Random Cut Forest (RCF) algorithm

  Correct Answer: CD

  https://aws.amazon.com/blogs/machine-learning/analyze-us-census-data-for-population-segmentation-using-amazon-sagemaker/ The PCA and K-means algorithms are useful in collection of data using census form.

  C: Use PCA for reducing number of variables. Each citizen's response should have answer for 500 questions, so it should have 500 variables

  D: Use K-means clustering

  A: Factorization Machines Algorithm is usually used for tasks dealing with high dimensional sparse datasets

  B: The Latent Dirichlet Allocation (LDA) algorithm should be used for task dealing topic modeling in NLP

  E: Random Cut Forest should be used for detecting anormal in data

• Question 370:

  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)

  Correct Answer: D

  https://docs.aws.amazon.com/zh_tw/machine-learning/latest/dg/cross-validation.html

  Area Under the ROC Curve (AUC) is a commonly used metric to compare and evaluate machine learning classification models against each other. The AUC measures the model's ability to distinguish between positive and negative classes, and its performance across different classification thresholds. The AUC ranges from 0 to 1, with a score of 1 representing a perfect classifier and a score of 0.5 representing a classifier that is no better than random.

  While recall is an important evaluation metric for classification models, it alone is not sufficient to compare and evaluate different models against each other. Recall measures the proportion of actual positive cases that are correctly identified as positive, but does not take into account the false positive rate.