Amazon Amazon Certifications MLS-C01 Questions & Answers

• Question 151:

  A company wants to deliver digital car management services to its customers. The company plans to analyze data to predict the likelihood of users changing cars. The company has 10 TB of data that is stored in an Amazon Redshift cluster.

  The company's data engineering team is using Amazon SageMaker Studio for data analysis and model development. Only a subset of the data is relevant for developing the machine learning models. The data engineering team needs a

  secure and cost-effective way to export the data to a data repository in Amazon S3 for model development.

  Which solutions will meet these requirements? (Choose two.)

  A. Launch multiple medium-sized instances in a distributed SageMaker Processing job. Use the prebuilt Docker images for Apache Spark to query and plot the relevant data and to export the relevant data from Amazon Redshift to Amazon S3.

  B. Launch multiple medium-sized notebook instances with a PySpark kernel in distributed mode. Download the data from Amazon Redshift to the notebook cluster. Query and plot the relevant data. Export the relevant data from the notebook cluster to Amazon S3.

  C. Use AWS Secrets Manager to store the Amazon Redshift credentials. From a SageMaker Studio notebook, use the stored credentials to connect to Amazon Redshift with a Python adapter. Use the Python client to query the relevant data and to export the relevant data from Amazon Redshift to Amazon S3.

  D. Use AWS Secrets Manager to store the Amazon Redshift credentials. Launch a SageMaker extra-large notebook instance with block storage that is slightly larger than 10 TB. Use the stored credentials to connect to Amazon Redshift with a Python adapter. Download, query, and plot the relevant data. Export the relevant data from the local notebook drive to Amazon S3.

  E. Use SageMaker Data Wrangler to query and plot the relevant data and to export the relevant data from Amazon Redshift to Amazon S3.

  Correct Answer: CE

• Question 152:

  A finance company needs to forecast the price of a commodity. The company has compiled a dataset of historical daily prices. A data scientist must train various forecasting models on 80% of the dataset and must validate the efficacy of those models on the remaining 20% of the dataset.

  How should the data scientist split the dataset into a training dataset and a validation dataset to compare model performance?

  A. Pick a date so that 80% of the data points precede the date. Assign that group of data points as the training dataset. Assign all the remaining data points to the validation dataset.

  B. Pick a date so that 80% of the data points occur after the date. Assign that group of data points as the training dataset. Assign all the remaining data points to the validation dataset.

  C. Starting from the earliest date in the dataset, pick eight data points for the training dataset and two data points for the validation dataset. Repeat this stratified sampling until no data points remain.

  D. Sample data points randomly without replacement so that 80% of the data points are in the training dataset. Assign all the remaining data points to the validation dataset.

  Correct Answer: A

• Question 153:

  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.

  Correct Answer: A

  Target and metadata must be in two files and loaded from S3, based on documentation: https://docs.aws.amazon.com/forecast/latest/dg/dataset-import-guidelines-troubleshooting.html

• Question 154:

  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. Redeploy the model as a batch transform job on an M5 instance.

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

  C. Redeploy the model on a P3dn instance.

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

  Correct Answer: B

  https://aws.amazon.com/machine-learning/elastic-inference/

• Question 155:

  A data scientist uses an Amazon SageMaker notebook instance to conduct data exploration and analysis. This requires certain Python packages that are not natively available on Amazon SageMaker to be installed on the notebook instance. How can a machine learning specialist ensure that required packages are automatically available on the notebook instance for the data scientist to use?

  A. Install AWS Systems Manager Agent on the underlying Amazon EC2 instance and use Systems Manager Automation to execute the package installation commands.

  B. Create a Jupyter notebook file (.ipynb) with cells containing the package installation commands to execute and place the file under the /etc/init directory of each Amazon SageMaker notebook instance.

  C. Use the conda package manager from within the Jupyter notebook console to apply the necessary conda packages to the default kernel of the notebook.

  D. Create an Amazon SageMaker lifecycle configuration with package installation commands and assign the lifecycle configuration to the notebook instance.

  Correct Answer: D

  https://docs.aws.amazon.com/sagemaker/latest/dg/nbi-add-external.html

• Question 156:

  A data scientist needs to identify fraudulent user accounts for a company's ecommerce platform. The company wants the ability to determine if a newly created account is associated with a previously known fraudulent user. The data scientist is using AWS Glue to cleanse the company's application logs during ingestion.

  Which strategy will allow the data scientist to identify fraudulent accounts?

  A. Execute the built-in FindDuplicates Amazon Athena query.

  B. Create a FindMatches machine learning transform in AWS Glue.

  C. Create an AWS Glue crawler to infer duplicate accounts in the source data.

  D. Search for duplicate accounts in the AWS Glue Data Catalog.

  Correct Answer: B

  Reference: https://docs.aws.amazon.com/glue/latest/dg/machine-learning.html

• Question 157:

  A Data Scientist is developing a machine learning model to classify whether a financial transaction is fraudulent. The labeled data available for training consists of 100,000 non-fraudulent observations and 1,000 fraudulent observations.

  The Data Scientist applies the XGBoost algorithm to the data, resulting in the following confusion matrix when the trained model is applied to a previously unseen validation dataset. The accuracy of the model is 99.1%, but the Data Scientist needs to reduce the number of false negatives.

  

  Which combination of steps should the Data Scientist take to reduce the number of false negative predictions by the model? (Choose two.)

  A. Change the XGBoost eval_metric parameter to optimize based on Root Mean Square Error (RMSE).

  B. Increase the XGBoost scale_pos_weight parameter to adjust the balance of positive and negative weights.

  C. Increase the XGBoost max_depth parameter because the model is currently underfitting the data.

  D. Change the XGBoost eval_metric parameter to optimize based on Area Under the ROC Curve (AUC).

  E. Decrease the XGBoost max_depth parameter because the model is currently overfitting the data.

  Correct Answer: BD

  First of all this is strangely worded question because first it says "reduce the number of false negatives" and then "reduce the number of false positive predictions". Also, the confusion matrix is displayed unconventionally. Actual should be in columns and predictions in rows. Having said that, you do not know if it over- or underfitting or perfectly fit. They don't tell you how it performed in training vs. test so you just don't know. If you want to reduce false positives, I would definitely change the scale_pos_weight and/or optimize based on AUC. A is illogical because RMSE applied to regression not classification. C and E both assume that you know how well the model fits, which you do not.

• Question 158:

  A retail company uses a machine learning (ML) model for daily sales forecasting. The model has provided inaccurate results for the past 3 weeks. At the end of each day, an AWS Glue job consolidates the input data that is used for the forecasting with the actual daily sales data and the predictions of the model. The AWS Glue job stores the data in Amazon S3.

  The company's ML team determines that the inaccuracies are occurring because of a change in the value distributions of the model features. The ML team must implement a solution that will detect when this type of change occurs in the future.

  Which solution will meet these requirements with the LEAST amount of operational overhead?

  A. Use Amazon SageMaker Model Monitor to create a data quality baseline. Confirm that the emit_metrics option is set to Enabled in the baseline constraints file. Set up an Amazon CloudWatch alarm for the metric.

  B. Use Amazon SageMaker Model Monitor to create a model quality baseline. Confirm that the emit_metrics option is set to Enabled in the baseline constraints file. Set up an Amazon CloudWatch alarm for the metric.

  C. Use Amazon SageMaker Debugger to create rules to capture feature values Set up an Amazon CloudWatch alarm for the rules.

  D. Use Amazon CloudWatch to monitor Amazon SageMaker endpoints. Analyze logs in Amazon CloudWatch Logs to check for data drift.

  Correct Answer: A

  "If the statistical nature of the data that your model receives while in production drifts away from the nature of the baseline data it was trained on, the model begins to lose accuracy in its predictions. Amazon SageMaker Model Monitor uses rules to detect data drift and alerts you when it happens." https://docs.aws.amazon.com/sagemaker/latest/dg/model-monitor-data-quality.html

• Question 159:

  A machine learning (ML) specialist has prepared and used a custom container image with Amazon SageMaker to train an image classification model. The ML specialist is performing hyperparameter optimization (HPO) with this custom container image to produce a higher quality image classifier.

  The ML specialist needs to determine whether HPO with the SageMaker built-in image classification algorithm will produce a better model than the model produced by HPO with the custom container image. All ML experiments and HPO jobs must be invoked from scripts inside SageMaker Studio notebooks.

  How can the ML specialist meet these requirements in the LEAST amount of time?

  A. Prepare a custom HPO script that runs multiple training jobs in SageMaker Studio in local mode to tune the model of the custom container image. Use the automatic model tuning capability of SageMaker with early stopping enabled to tune the model of the built-in image classification algorithm. Select the model with the best objective metric value.

  B. Use SageMaker Autopilot to tune the model of the custom container image. Use the automatic model tuning capability of SageMaker with early stopping enabled to tune the model of the built-in image classification algorithm. Compare the objective metric values of the resulting models of the SageMaker AutopilotAutoML job and the automatic model tuning job. Select the model with the best objective metric value.

  C. Use SageMaker Experiments to run and manage multiple training jobs and tune the model of the custom container image. Use the automatic model tuning capability of SageMaker to tune the model of the built-in image classification algorithm. Select the model with the best objective metric value.

  D. Use the automatic model tuning capability of SageMaker to tune the models of the custom container image and the built-in image classification algorithm at the same time. Select the model with the best objective metric value.

  Correct Answer: B

• Question 160:

  A company is building a machine learning (ML) model to classify images of plants. An ML specialist has trained the model using the Amazon SageMaker built-in Image Classification algorithm. The model is hosted using a SageMaker endpoint on an ml.m5.xlarge instance for real-time inference. When used by researchers in the field, the inference has greater latency than is acceptable. The latency gets worse when multiple researchers perform inference at the same time on their devices. Using Amazon CloudWatch metrics, the ML specialist notices that the ModelLatency metric shows a high value and is responsible for most of the response latency.

  The ML specialist needs to fix the performance issue so that researchers can experience less latency when performing inference from their devices.

  Which action should the ML specialist take to meet this requirement?

  A. Change the endpoint instance to an ml.t3 burstable instance with the same vCPU number as the ml.m5.xlarge instance has.

  B. Attach an Amazon Elastic Inference ml.eia2.medium accelerator to the endpoint instance.

  C. Enable Amazon SageMaker Autopilot to automatically tune performance of the model.

  D. Change the endpoint instance to use a memory optimized ML instance.

  Correct Answer: B

  https://aws.amazon.com/premiumsupport/knowledge-center/sagemaker-endpoint-latency/