MLS-C01 Sample Questions & Answers

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.)

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?

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?

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

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?

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?

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.)

A Mobile Network Operator is building an analytics platform to analyze and optimize a company's operations using Amazon Athena and Amazon S3.
The source systems send data in .CSV format in real time. The Data Engineering team wants to transform the data to the Apache Parquet format before storing it on Amazon S3.

Which solution takes the LEAST effort to implement?

The displayed graph is from a forecasting model for testing a time series.
Considering the graph only,

which conclusion should a Machine Learning Specialist make about the behavior of the model?

A Machine Learning Specialist prepared the following graph displaying the results of k-means for k = [1..10]: