Free Amazon MLS-C01 Exam Questions

Question 1

A retail company wants to combine its customer orders with the product description data from its product catalog. The structure and format of the records in each dataset is different. A data analyst tried to use a spreadsheet to combine the datasets, but the effort resulted in duplicate records and records that were not properly combined. The company needs a solution that it can use to combine similar records from the two datasets and remove any duplicates.

Which solution will meet these requirements?

A : an AWS Lambda function to process the data. Use two arrays to compare equal strings in the fields from the two datasets and remove any duplicates.

B : eate AWS Glue crawlers for reading and populating the AWS Glue Data Catalog. Call the AWS Glue SearchTables API operation to perform a fuzzy-matching search on the two datasets, and cleanse the data accordingly.

C : eate AWS Glue crawlers for reading and populating the AWS Glue Data Catalog. Use the FindMatches transform to cleanse the data.

D : eate an AWS Lake Formation custom transform. Run a transformation for matching products from the Lake Formation console to cleanse the data automatically.

Answer: D

Question 2

A data scientist is building a new model for an ecommerce company. The model will predict how many minutes it will take to deliver a package. During model training, the data scientist needs to evaluate model performance. Which metrics should the data scientist use to meet this requirement? (Select TWO.)

A : InferenceLatency

B : Mean squared error (MSE)

C : Root mean squared error (RMSE)

D : Precision

E : Accuracy

Answer: B,C

Question 3

A gaming company has launched an online game where people can start playing for free but they need to pay if they choose to use certain features The company needs to build an automated system to predict whether or not a new user will become a paid user within 1 year The company has gathered a labeled dataset from 1 million users

The training dataset consists of 1.000 positive samples (from users who ended up paying within 1 year) and 999.000 negative samples (from users who did not use any paid features) Each data sample consists of 200 features including user age, device, location, and play patterns

Using this dataset for training, the Data Science team trained a random forest model that converged with over 99?curacy on the training set However, the prediction results on a test dataset were not satisfactory.

Which of the following approaches should the Data Science team take to mitigate this issue? (Select TWO.)

A : more deep trees to the random forest to enable the model to learn more features.

B : dicate a copy of the samples in the test database in the training dataset

C : nerate more positive samples by duplicating the positive samples and adding a small amount of noise to the duplicated data.

D : ange the cost function so that false negatives have a higher impact on the cost value than false positives

E : ange the cost function so that false positives have a higher impact on the cost value than false negatives

Answer: C,D

Question 4

A machine learning (ML) specialist at a manufacturing company uses Amazon SageMaker DeepAR to forecast input materials and energy requirements for the company. Most of the data in the training dataset is missing values for the target variable. The company stores the training dataset as JSON files.

The ML specialist develop a solution by using Amazon SageMaker DeepAR to account for the missing values in the training dataset.

Which approach will meet these requirements with the LEAST development effort?

A : Impute the missing values by using the linear regression method. Use the entire dataset and the imputed values to train the DeepAR model.

B : Replace the missing values with not a number (NaN). Use the entire dataset and the encoded missing values to train the DeepAR model.

C : Impute the missing values by using a forward fill. Use the entire dataset and the imputed values to train the DeepAR model.

D : Impute the missing values by using the mean value. Use the entire dataset and the imputed values to train the DeepAR model.

Answer: D

Question 5

A Machine Learning Specialist is attempting to build a linear regression model.

Given the displayed residual plot only, what is the MOST likely problem with the model?

A : Linear regression is inappropriate. The residuals do not have constant variance.

B : Linear regression is inappropriate. The underlying data has outliers.

C : Linear regression is appropriate. The residuals have a zero mean.

D : Linear regression is appropriate. The residuals have constant variance.

Answer: D