MLS-C01 Exam: Absolutely Free Online Practice

Question 1
- A company has a podcast platform that has thousands of users. The company has implemented an anomaly detection algorithm to detect low podcast engagement based on a 10-minute running window of user events such as listening, pausing, and exiting the podcast. A machine learning (ML) specialist is designing the data ingestion of these events with the knowledge that the event payload needs some small transformations before inference. How should the ML specialist design the data ingestion to meet these requirements with the LEAST operational overhead?
  A : Ingest event data by using a GraphQLAPI in AWS AppSync. Store the data in an Amazon DynamoDB table. Use DynamoDB Streams to call an AWS Lambda function to transform the most recent 10 minutes of data before inference.
  
  B : Ingest event data by using Amazon Kinesis Data Streams. Store the data in Amazon S3 by using Amazon Kinesis Data Firehose. Use AWS Glue to transform the most recent 10 minutes of data before inference.
  
  C : Ingest event data by using Amazon Kinesis Data Streams. Use an Amazon Kinesis Data Analytics for Apache Flink application to transform the most recent 10 minutes of data before inference.
  
  D : Ingest event data by using Amazon Managed Streaming for Apache Kafka (Amazon MSK). Use an AWS Lambda function to transform the most recent 10 minutes of data before inference
  
  Answer: B
Question 2
- A data scientist wants to use Amazon Forecast to build a forecasting model for inventory demand for a retail company. The company has provided a dataset of historic inventory demand for its products as a .csv file stored in an Amazon S3 bucket. The table below shows a sample of the dataset.
  
  How should the data scientist transform the data?
  
  A : Use ETL jobs in AWS Glue to separate the dataset into a target time series dataset and an item metadata dataset. Upload both datasets as .csv files to Amazon S3.
  
  B : Use a Jupyter notebook in Amazon SageMaker to separate the dataset into a related time series dataset and an item metadata dataset. Upload both datasets as tables in Amazon Aurora.
  
  C : Use AWS Batch jobs to separate the dataset into a target time series dataset, a related time series dataset, and an item metadata dataset. Upload them directly to Forecast from a local machine.
  
  D : Use a Jupyter notebook in Amazon SageMaker to transform the data into the optimized protobuf recordIO format. Upload the dataset in this format to Amazon S3.
  
  Answer: A
Question 3
- A data engineer is using AWS Glue to create optimized, secure datasets in Amazon S3. The data science team wants the ability to access the ETL scripts directly from Amazon SageMaker notebooks within a VPC. After this setup is complete, the data science team wants the ability to run the AWS Glue job and invoke the SageMaker training job. Which combination of steps should the data engineer take to meet these requirements? (Choose three.)
  
  A : Create a SageMaker development endpoint in the data science team-s VPC.
  
  B : Create an AWS Glue development endpoint in the data science team-s VPC
  
  C : Create SageMaker notebooks by using the AWS Glue development endpoint.
  
  D : Create SageMaker notebooks by using the SageMaker console
  
  E : Attach a decryption policy to the SageMaker notebooks.
  
  F : Create an IAM policy and an IAM role for the SageMaker notebooks.
  
  Answer: A,D,F
Question 4
- n online delivery company wants to choose the fastest courier for each delivery at the moment an order is placed. The company wants to implement this feature for existing users and new users of its application. Data scientists have trained separate models with XGBoost for this purpose, and the models are stored in Amazon S3. There is one model for each city where the company operates.
  Operation engineers are hosting these models in Amazon EC2 for responding to the web client requests, with one instance for each model, but the instances have only a 5% utilization in CPU and memory. The operation engineers want to avoid managing unnecessary resources.
  Which solution will enable the company to achieve its goal with the LEAST operational overhead?
  A : Create an Amazon SageMaker notebook instance for pulling all the models from Amazon S3 using the boto3 library. Remove the existing instances and use the notebook to perform a SageMaker batch transform for performing inferences offline for all the possible users in all the cities. Store the results in different files in Amazon S3. Point the web client to the files.
  
  B : Prepare an Amazon SageMaker Docker container based on the open-source multi-model server. Remove the existing instances and create a multi-model endpoint in SageMaker instead, pointing to the S3 bucket containing all the models. Invoke the endpoint from the web client at runtime, specifying the TargetModel parameter according to the city of each request.
  
  C : Keep only a single EC2 instance for hosting all the models. Install a model server in the instance and load each model by pulling it from Amazon S3. Integrate the instance with the web client using Amazon API Gateway for responding to the requests in real time, specifying the target resource according to the city of each request.
  
  D : Prepare a Docker container based on the prebuilt images in Amazon SageMaker. Replace the existing instances with separate SageMaker endpoints, one for each city where the company operates. Invoke the endpoints from the web client, specifying the URL and EndpointName parameter according to the city of each request.
  
  Answer: B
Question 5
- For the given confusion matrix, what is the recall and precision of the model?
  
  A : Recall = 0.92 Precision = 0.84
  
  B : Recall = 0.84 Precision = 0.8
  
  C : Recall = 0.92 Precision = 0.8
  
  D : Recall = 0.8 Precision = 0.92
  
  Answer: C

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