Amazon Amazon Certifications MLS-C01 Questions & Answers

• Question 111:

  A healthcare company wants to create a machine learning (ML) model to predict patient outcomes. A data science team developed an ML model by using a custom ML library. The company wants to use Amazon SageMaker to train this model. The data science team creates a custom SageMaker image to train the model. When the team tries to launch the custom image in SageMaker Studio, the data scientists encounter an error within the application.

  Which service can the data scientists use to access the logs for this error?

  A. Amazon S3

  B. Amazon Elastic Block Store (Amazon EBS)

  C. AWS CloudTrail

  D. Amazon CloudWatch

  Correct Answer: A

• Question 112:

  A data scientist wants to build a financial trading bot to automate investment decisions. The financial bot should recommend the quantity and price of an asset to buy or sell to maximize long-term profit. The data scientist will continuously stream financial transactions to the bot for training purposes. The data scientist must select the appropriate machine learning (ML) algorithm to develop the financial trading bot.

  Which type of ML algorithm will meet these requirements?

  A. Supervised learning

  B. Unsupervised learning

  C. Semi-supervised learning

  D. Reinforcement learning

  Correct Answer: C

• Question 113:

  A manufacturing company wants to create a machine learning (ML) model to predict when equipment is likely to fail. A data science team already constructed a deep learning model by using TensorFlow and a custom Python script in a local environment. The company wants to use Amazon SageMaker to train the model.

  Which TensorFlow estimator configuration will train the model MOST cost-effectively?

  A. Turn on SageMaker Training Compiler by adding compiler_config=TrainingCompilerConfig() as a parameter. Pass the script to the estimator in the call to the TensorFlow fit() method.

  B. Turn on SageMaker Training Compiler by adding compiler_config=TrainingCompilerConfig() as a parameter. Turn on managed spot training by setting the use_spot_instances parameter to True. Pass the script to the estimator in the call to the TensorFlow fit() method.

  C. Adjust the training script to use distributed data parallelism. Specify appropriate values for the distribution parameter. Pass the script to the estimator in the call to the TensorFlow fit() method.

  D. Turn on SageMaker Training Compiler by adding compiler_config=TrainingCompilerConfig() as a parameter. Set the MaxWaitTimeInSeconds parameter to be equal to the MaxRuntimeInSeconds parameter. Pass the script to the estimator in the call to the TensorFlow fit() method.

  Correct Answer: D

• Question 114:

  Each morning, a data scientist at a rental car company creates insights about the previous day's rental car reservation demands. The company needs to automate this process by streaming the data to Amazon S3 in near real time. The solution must detect high-demand rental cars at each of the company's locations. The solution also must create a visualization dashboard that automatically refreshes with the most recent data.

  Which solution will meet these requirements with the LEAST development time?

  A. Use Amazon Kinesis Data Firehose to stream the reservation data directly to Amazon S3. Detect high-demand outliers by using Amazon QuickSight ML Insights. Visualize the data in QuickSight.

  B. Use Amazon Kinesis Data Streams to stream the reservation data directly to Amazon S3. Detect high-demand outliers by using the Random Cut Forest (RCF) trained model in Amazon SageMaker. Visualize the data in Amazon QuickSight.

  C. Use Amazon Kinesis Data Firehose to stream the reservation data directly to Amazon S3. Detect high-demand outliers by using the Random Cut Forest (RCF) trained model in Amazon SageMaker. Visualize the data in Amazon QuickSight.

  D. Use Amazon Kinesis Data Streams to stream the reservation data directly to Amazon S3. Detect high-demand outliers by using Amazon QuickSight ML Insights. Visualize the data in QuickSight.

  Correct Answer: A

• Question 115:

  A machine learning (ML) engineer is integrating a production model with a customer metadata repository for real-time inference. The repository is hosted in Amazon SageMaker Feature Store. The engineer wants to retrieve only the latest version of the customer metadata record for a single customer at a time.

  Which solution will meet these requirements?

  A. Use the SageMaker Feature Store BatchGetRecord API with the record identifier. Filter to find the latest record.

  B. Create an Amazon Athena query to retrieve the data from the feature table.

  C. Create an Amazon Athena query to retrieve the data from the feature table. Use the write_time value to find the latest record.

  D. Use the SageMaker Feature Store GetRecord API with the record identifier.

  Correct Answer: D

• Question 116:

  A company's data scientist has trained a new machine learning model that performs better on test data than the company's existing model performs in the production environment. The data scientist wants to replace the existing model that runs on an Amazon SageMaker endpoint in the production environment. However, the company is concerned that the new model might not work well on the production environment data.

  The data scientist needs to perform A/B testing in the production environment to evaluate whether the new model performs well on production environment data.

  Which combination of steps must the data scientist take to perform the A/B testing? (Choose two.)

  A. Create a new endpoint configuration that includes a production variant for each of the two models.

  B. Create a new endpoint configuration that includes two target variants that point to different endpoints.

  C. Deploy the new model to the existing endpoint.

  D. Update the existing endpoint to activate the new model.

  E. Update the existing endpoint to use the new endpoint configuration.

  Correct Answer: AE

• Question 117:

  A data scientist is working on a forecast problem by using a dataset that consists of .csv files that are stored in Amazon S3. The files contain a timestamp variable in the following format:

  March 1st, 2020, 08:14pm

  There is a hypothesis about seasonal differences in the dependent variable. This number could be higher or lower for weekdays because some days and hours present varying values, so the day of the week, month, or hour could be an important factor. As a result, the data scientist needs to transform the timestamp into weekdays, month, and day as three separate variables to conduct an analysis.

  Which solution requires the LEAST operational overhead to create a new dataset with the added features?

  A. Create an Amazon EMR cluster. Develop PySpark code that can read the timestamp variable as a string, transform and create the new variables, and save the dataset as a new file in Amazon S3.

  B. Create a processing job in Amazon SageMaker. Develop Python code that can read the timestamp variable as a string, transform and create the new variables, and save the dataset as a new file in Amazon S3.

  C. Create a new flow in Amazon SageMaker Data Wrangler. Import the S3 file, use the Featurize date/time transform to generate the new variables, and save the dataset as a new file in Amazon S3.

  D. Create an AWS Glue job. Develop code that can read the timestamp variable as a string, transform and create the new variables, and save the dataset as a new file in Amazon S3.

  Correct Answer: C

• Question 118:

  An engraving company wants to automate its quality control process for plaques. The company performs the process before mailing each customized plaque to a customer. The company has created an Amazon S3 bucket that contains images of defects that should cause a plaque to be rejected. Low-confidence predictions must be sent to an internal team of reviewers who are using Amazon Augmented AI (Amazon A2I).

  Which solution will meet these requirements?

  A. Use Amazon Textract for automatic processing. Use Amazon A2I with Amazon Mechanical Turk for manual review.

  B. Use Amazon Rekognition for automatic processing. Use Amazon A2I with a private workforce option for manual review.

  C. Use Amazon Transcribe for automatic processing. Use Amazon A2I with a private workforce option for manual review.

  D. Use AWS Panorama for automatic processing. Use Amazon A2I with Amazon Mechanical Turk for manual review.

  Correct Answer: C

• Question 119:

  A machine learning (ML) engineer at a bank is building a data ingestion solution to provide transaction features to financial ML models. Raw transactional data is available in an Amazon Kinesis data stream.

  The solution must compute rolling averages of the ingested data from the data stream and must store the results in Amazon SageMaker Feature Store. The solution also must serve the results to the models in near real time.

  Which solution will meet these requirements?

  A. Load the data into an Amazon S3 bucket by using Amazon Kinesis Data Firehose. Use a SageMaker Processing job to aggregate the data and to load the results into SageMaker Feature Store as an online feature group.

  B. Write the data directly from the data stream into SageMaker Feature Store as an online feature group. Calculate the rolling averages in place within SageMaker Feature Store by using the SageMaker GetRecord API operation.

  C. Consume the data stream by using an Amazon Kinesis Data Analytics SQL application that calculates the rolling averages. Generate a result stream. Consume the result stream by using a custom AWS Lambda function that publishes the results to SageMaker Feature Store as an online feature group.

  D. Load the data into an Amazon S3 bucket by using Amazon Kinesis Data Firehose. Use a SageMaker Processing job to load the data into SageMaker Feature Store as an offline feature group. Compute the rolling averages at query time.

  Correct Answer: C

• Question 120:

  A company's data engineer wants to use Amazon S3 to share datasets with data scientists. The data scientists work in three departments: Finance. Marketing, and Human Resources. Each department has its own IAM user group. Some datasets contain sensitive information and should be accessed only by the data scientists from the Finance department.

  How can the data engineer set up access to meet these requirements?

  A. Create an S3 bucket for each dataset. Create an ACL for each S3 bucket. For each S3 bucket that contains a sensitive dataset, set the ACL to allow access only from the Finance department user group. Allow all three department user groups to access each S3 bucket that contains a non-sensitive dataset.

  B. Create an S3 bucket for each dataset. For each S3 bucket that contains a sensitive dataset, set the bucket policy to allow access only from the Finance department user group. Allow all three department user groups to access each S3 bucket that contains a non-sensitive dataset.

  C. Create a single S3 bucket that includes two folders to separate the sensitive datasets from the non-sensitive datasets. For the Finance department user group, attach an IAM policy that provides access to both folders. For the Marketing and Human Resources department user groups, attach an IAM policy that provides access to only the folder that contains the non-sensitive datasets.

  D. Create a single S3 bucket that includes two folders to separate the sensitive datasets from the non-sensitive datasets. Set the policy for the S3 bucket to allow only the Finance department user group to access the folder that contains the sensitive datasets. Allow all three department user groups to access the folder that contains the non-sensitive datasets.

  Correct Answer: C

  https://stackoverflow.com/questions/35944349/iam-aws-s3-to-restrict-to-a-specific-sub-folder

  https://aws.amazon.com/blogs/security/how-to-restrict-amazon-s3-bucket-access-to-a-specific-iam-role/