Free Amazon MLS-C01 Exam Questions

Question 1

A company has an ecommerce website with a product recommendation engine built in TensorFlow. The recommendation engine endpoint is hosted by Amazon SageMaker. Three compute-optimized instances support the expected peak load of the website.

Response times on the product recommendation page are increasing at the beginning of each month. Some users are encountering errors. The website receives the majority of its traffic between 8 AM and 6 PM on weekdays in a single time zone.

Which of the following options are the MOST effective in solving the issue while keeping costs to a minimum? (Choose two.)

A : nfigure the endpoint to use Amazon Elastic Inference (EI) accelerators.

B : eate a new endpoint configuration with two production variants.

C : nfigure the endpoint to automatically scale with the InvocationsPerInstance metric.

D : ploy a second instance pool to support a blue/green deployment of models.

E : configure the endpoint to use burstable instances.

Answer: B,D

Question 2

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 lime 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?

A : Ingest .CSV data using Apache Kafka Streams on Amazon EC2 instances and use Kafka Connect S3 toserialize data as Parquet

B : gest .CSV data from Amazon Kinesis Data Streams and use Amazon Glue to convert data into Parquet.

C : Ingest .CSV data using Apache Spark Structured Streaming in an Amazon EMR cluster and use ApacheSpark to convert data into Parquet.

D : Ingest .CSV data from Amazon Kinesis Data Streams and use Amazon Kinesis Data Firehose to convertdata into Parquet.

Answer: B

Question 3

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 4

A manufacturing company has a large set of labeled historical sales data The manufacturer would like to predict how many units of a particular part should be produced each quarter Which machine learning approach should be used to solve this problem?

A : gistic regression

B : ndom Cut Forest (RCF)

C : incipal component analysis (PCA)

D : near regression

Answer: D

Question 5