Free Amazon MLS-C01 Exam Questions

Question 1

A data scientist receives a collection of insurance claim records. Each record includes a claim ID. the final outcome of the insurance claim, and the date of the final outcome.

The final outcome of each claim is a selection from among 200 outcome categories. Some claim records include only partial information. However, incomplete claim records include only 3 or 4 outcome categories from among the 200 available outcome categories. The collection includes hundreds of records for each outcome category. The records are from the previous 3 years.

The data scientist must create a solution to predict the number of claims that will be in each outcome category every month, several months in advance.

Which solution will meet these requirements?

A : Perform classification every month by using supervised learning of the 200 outcome categories based on claim contents.

B : Perform reinforcement learning by using claim IDs and dates. Instruct the insurance agents who submit the claim records to estimate the expected number of claims in each outcome category every month.

C : Perform forecasting by using claim IDs and dates to identify the expected number of claims in each outcome category every month.

D : Perform classification by using supervised learning of the outcome categories for which partial information on claim contents is provided. Perform forecasting by using claim IDs and dates for all other outcome categories.

Answer: C

Question 2

Amazon Connect has recently been tolled out across a company as a contact call center The solution has been configured to store voice call recordings on Amazon S3

The content of the voice calls are being analyzed for the incidents being discussed by the call operators Amazon Transcribe is being used to convert the audio to text, and the output is stored on Amazon S3

Which approach will provide the information required for further analysis?

A : Amazon Comprehend with the transcribed files to build the key topics

B : Amazon Translate with the transcribed files to train and build a model for the key topics

C : Use the AWS Deep Learning AMI with Gluon Semantic Segmentation on the transcribed files to train and build a model for the key topics

D : the Amazon SageMaker k-Nearest-Neighbors (kNN) algorithm on the transcribed files to generate a word embeddings dictionary for the key topics

Answer: B

Question 3

A machine learning specialist is running an Amazon SageMaker endpoint using the built-in object detection algorithm on a P3 instance for real-time predictions in a company's production application. When evaluating the model's resource utilization, the specialist notices that the model is using only a fraction of the GPU.

Which architecture changes would ensure that provisioned resources are being utilized effectively?

A : deploy the model as a batch transform job on an M5 instance.

B : deploy the model on an M5 instance. Attach Amazon Elastic Inference to the instance.

C : deploy the model on a P3dn instance.

D : ploy the model onto an Amazon Elastic Container Service (Amazon ECS) cluster using a P3 instance.

Answer: D

Question 4

A machine learning (ML) specialist must develop a classification model for a financial services company. A domain expert provides the dataset, which is tabular with 10,000 rows and 1,020 features. During exploratory data analysis, the specialist finds no missing values and a small percentage of duplicate rows. There are correlation scores of > 0.9 for 200 feature pairs. The mean value of each feature is similar to its 50th percentile.

Which feature engineering strategy should the ML specialist use with Amazon SageMaker?

A : ply dimensionality reduction by using the principal component analysis (PCA) algorithm.

B : op the features with low correlation scores by using a Jupyter notebook.

C : ply anomaly detection by using the Random Cut Forest (RCF) algorithm.

D : ncatenate the features with high correlation scores by using a Jupyter notebook.

Answer: C

Question 5

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