MLS-C01 sample questions

Question 1

A Machine Learning Specialist previously trained a logistic regression model using scikit-learn on a local machine, and the Specialist now wants to deploy it to production for inference only.

What steps should be taken to ensure Amazon SageMaker can host a model that was trained locally?

Accepted Answer

Build the Docker image with the inference code. Configure Docker Hub and upload the image to Amazon ECR.

Answer

Serialize the trained model so the format is compressed for deployment. Build the image and upload it to Docker Hub.

Answer

Serialize the trained model so the format is compressed for deployment. Tag the Docker image with the registry hostname and upload it to Amazon S3.

Answer

Build the Docker image with the inference code. Tag the Docker image with the registry hostname and upload it to Amazon ECR.

Question 2

A large consumer goods manufacturer has the following products on sale: • 34 different toothpaste variants • 48 different toothbrush variants • 43 different mouthwash variants
The entire sales history of all these products is available in Amazon S3. Currently, the company is using custom-built autoregressive integrated moving average (ARIMA) models to forecast demand for these products. The company wants to predict the demand for a new product that will soon be launched.

Which solution should a Machine Learning Specialist apply?

Accepted Answer

Train an Amazon SageMaker DeepAR algorithm to forecast demand for the new product.

Answer

Train an Amazon SageMaker k-means clustering algorithm to forecast demand for the new product.

Answer

Train a custom ARIMA model to forecast demand for the new product.

Answer

Train a custom XGBoost model to forecast demand for the new product.

Question 3

A company wants to classify user behavior as either fraudulent or normal. Based on internal research, a Machine Learning Specialist would like to build a binary classifier based on two features: age of account and transaction month. The class distribution for these features is illustrated in the figure provided.

Based on this information, which model would have the HIGHEST accuracy?

Accepted Answer

Support vector machine (SVM) with non-linear kernel

Answer

Single perceptron with tanh activation function

Answer

Long short-term memory (LSTM) model with scaled exponential linear unit (SELL!)

Answer

Logistic regression

Question 4

A Data Scientist is developing a machine learning model to classify whether a financial transaction is fraudulent. The labeled data available fortraining 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.
Predicted 0 1 Actual 0 99.9661 34 1 8771123

Which combination of steps should the Data Scientist take to reduce the number of false positive predictions by the model? (Choose two.)

Accepted Answer

D Change the XGBoost evaljnetric parameter to optimize based on AUC instead of error.

Accepted Answer

E Decrease the XGBoost max_depth parameter because the model is currently overfitting the data.

Answer

c Increase the XGBoost max_depth parameter because the model is currently underfitting the data.

Answer

B Increase the XGBoost scale_pos_weight parameter to adjust the balance of positive and negative weights.

Answer

Change the XGBoost eval_metric parameter to optimize based on rmse instead of error.

Question 5

A Machine Learning Specialist is assigned to a Fraud Detection team and must tune an XGBoost model, which is working appropriately for test data. However, with unknown data, it is not working as expected. The existing parameters are provided as follows.
param = { 'eta': 0.05, # the training step for each iteration 'silent': 1, # logging mode - quiet 'n_estimators':2000, 'max_depth' :30, 'min_child_weight' : 3, 'gamma': 0, 'subsample': 0.8, 'objective': 'multi:softprob', # error evaluation for multiclass training 'num_class': 201} # the number of classes that exist in this dataset num_round = 6 0 # the number of training iterations

Which parameter tuning guidelines should the Specialist follow to avoid overfitting?

Accepted Answer

Lower the max_depth parameter value.

Answer

Update the objective to binaryzlogistic.

Answer

Lower the min_child_weight parameter value.

Answer

Increase the max_depth parameter value.

Question 6

A Machine Learning Specialist is deciding between building a naive Bayesian model or a full Bayesian network for a classification problem. The Specialist computes the Pearson correlation coefficients between each feature and finds that their absolute values range between 0.1 to 0.95.

Which model describes the underlying data in this situation?

Accepted Answer

a naive Bayesian model, since some of the features are statistically dependent.

Answer

a full Bayesian network, since some of the features are statistically dependent.

Answer

A full Bayesian network, since the features are all conditionally independent.

Answer

A naive Bayesian model, since the features are all conditionally independent.

Question 7

Which of the following metrics should a Machine Learning Specialist generally use to compare/evaluate machine learning classification models against each other?

Accepted Answer

Area Under the ROC Curve (AUC)

Answer

Mean absolute percentage error (MAPE)

Answer

Misclassification rate

Answer

Recall

Question 8

A Machine Learning Specialist is designing a system for improving sales for a company. The objective is to use the large amount of information the company has on users’ behavior and product preferences to predict which products users would like based on the users’ similarity to other users.

What should the Specialist do to meet this objective?

Accepted Answer

Build a collaborative filtering recommendation engine with Apache Spark ML on Amazon EMR.

Answer

Build a content-based filtering recommendation engine with Apache Spark ML on Amazon EMR

Answer

Build a model-based filtering recommendation engine with Apache Spark ML on Amazon EMR

Answer

Build a combinative filtering recommendation engine with Apache Spark ML on Amazon EMR

Question 9

A Machine Learning Specialist trained a regression model, but the first iteration needs optimizing. The Specialist needs to understand whether the model is more frequently overestimating or underestimating the target.

What option can the Specialist use to determine whether it is overestimating or underestimating the target value?

Accepted Answer

Residual plots

Answer

Root Mean Square Error (RMSE)

Answer

Area under the curve

Answer

Confusion matrix

Question 10

A large company has developed a Bl application that generates reports and dashboards using data collected from various operational metrics. The company wants to provide executives with an enhanced experience so they can use natural language to get data from the reports. The company wants the executives to be able ask questions using written and spoken interfaces.

Which combination of services can be used to build this conversational interface? (Choose three.)

Accepted Answer

Amazon Comprehend

Accepted Answer

Amazon Connect

Accepted Answer

Amazon Transcribe

Answer

Amazon Polly

Answer

Amazon Lex

Answer

A Alexa for Business

Free MLS-C01 sample questions

A Machine Learning Specialist previously trained a logistic regression model using scikit-learn on a local machine, and the Specialist now wants to deploy it to production for inference only. What steps should be taken to ensure Amazon SageMaker can host a model that was trained locally?

Which of the following metrics should a Machine Learning Specialist generally use to compare/evaluate machine learning classification models against each other?

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