Free Amazon MLS-C01 Exam Questions

Question 1

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 2

A financial services company wants to automate its loan approval process by building a machine learning (ML) model. Each loan data point contains credit history from a third-party data source and demographic information about the customer. Each loan approval prediction must come with a report that contains an explanation for why the customer was approved for a loan or was denied for a loan. The company will use Amazon SageMaker to build the model. Which solution will meet these requirements with the LEAST development effort?

A : SageMaker Model Debugger to automatically debug the predictions, generate the explanation, and attach the explanation report.

B : AWS Lambda to provide feature importance and partial dependence plots. Use the plots to generate and attach the explanation report

C : SageMaker Clarify to generate the explanation report. Attach the report to the predicted results.

D : Use custom Amazon CloudWatch metrics to generate the explanation report. Attach the report to the predicted results.

Answer: A

Question 3

A financial services company is building a robust serverless data lake on Amazon S3. The data lake should be flexible and meet the following requirements:

* Support querying old and new data on Amazon S3 through Amazon Athena and Amazon Redshift Spectrum.

* Support event-driven ETL pipelines.

* Provide a quick and easy way to understand metadata.

Which approach meets trfese requirements?

A : an AWS Glue crawler to crawl S3 data, an AWS Lambda function to trigger an AWS Glue ETL job, and an AWS Glue Data catalog to search and discover metadata.

B : an AWS Glue crawler to crawl S3 data, an AWS Lambda function to trigger an AWS Batch job, and an external Apache Hive metastore to search and discover metadata.

C : an AWS Glue crawler to crawl S3 data, an Amazon CloudWatch alarm to trigger an AWS Batch job, and an AWS Glue Data Catalog to search and discover metadata.

D : an AWS Glue crawler to crawl S3 data, an Amazon CloudWatch alarm to trigger an AWS Glue ETL job, and an external Apache Hive metastore to search and discover metadata.

Answer: A

Question 4

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 5

A data scientist must build a custom recommendation model in Amazon SageMaker for an online retail company. Due to the nature of the company's products, customers buy only 4-5 products every 5-10 years. So, the company relies on a steady stream of new customers. When a new customer signs up, the company collects data on the customer's preferences. Below is a sample of the data available to the data scientist.

How should the data scientist split the dataset into a training and test set for this use case?

A : uffle all interaction data. Split off the last 10% of the interaction data for the test set.

B : entify the most recent 10% of interactions for each user. Split off these interactions for the test set.

C : entify the 10% of users with the least interaction data. Split off all interaction data from these users for the test set.

D : ndomly select 10% of the users. Split off all interaction data from these users for the test set.

Answer: D