MLS-C01 practice questions 221–230

1. Question 221 · 368 in bank

   A company deployed a machine learning (ML) model on the company website to predict real estate prices. Several months after deployment, an ML engineer notices that the accuracy of the model has gradually decreased. The ML engineer needs to improve the accuracy of the model. The engineer also needs to receive notifications for any future performance issues. Which solution will meet these requirements?

   • APerform incremental training to update the model. Activate Amazon SageMaker Model Monitor to detect model performance issues and to send notifications.
   • BUse Amazon SageMaker Model Governance. Configure Model Governance to automatically adjust model hyperparameters. Create a performance threshold alarm in Amazon CloudWatch to send notifications.
   • CUse Amazon SageMaker Debugger with appropriate thresholds. Configure Debugger to send Amazon CloudWatch alarms to alert the team. Retrain the model by using only data from the previous several months.
   • DUse only data from the previous several months to perform incremental training to update the model. Use Amazon SageMaker Model Monitor to detect model performance issues and to send notifications.

   Show answer

   Answer: A

   Explanation

   Incremental training involves updating the model with new data over time. Amazon SageMaker Model Monitor is a suitable choice for monitoring model performance. It can detect drift and anomalies in real-time predictions and send notifications.

   Source: Adapted from community discussion — AIWave

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2. Question 222 · 368 in bank

   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 time. 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?

   • AIngest .CSV data using Apache Kafka Streams on Amazon EC2 instances and use Kafka Connect S3 to serialize data as Parquet
   • BIngest .CSV data from Amazon Kinesis Data Streams and use Amazon Glue to convert data into Parquet.
   • CIngest .CSV data using Apache Spark Structured Streaming in an Amazon EMR cluster and use Apache Spark to convert data into Parquet.
   • DIngest .CSV data from Amazon Kinesis Data Streams and use Amazon Kinesis Data Firehose to convert data into Parquet.

   Show answer

   Answer: D

   Explanation

   D is wrong as kinesis firehose can convert from JSON to parquet but here we have CSV. B is correct and here is another proof link: https://medium.com/searce/convert-csv-json-files-to-apache-parquet-using-aws-glue-a760d177b45f

   Source: Adapted from community discussion — vetal

   References

   • AWS documentation
   • AWS documentation
   • AWS documentation
   • AWS documentation
   • AWS documentation

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3. Question 223 · 368 in bank

   Select 3

   When submitting Amazon SageMaker training jobs using one of the built-in algorithms, which common parameters MUST be specified? (Choose three.)

   • AThe training channel identifying the location of training data on an Amazon S3 bucket.
   • BThe validation channel identifying the location of validation data on an Amazon S3 bucket.
   • CThe IAM role that Amazon SageMaker can assume to perform tasks on behalf of the users.
   • DHyperparameters in a JSON array as documented for the algorithm used.
   • EThe Amazon EC2 instance class specifying whether training will be run using CPU or GPU.
   • FThe output path specifying where on an Amazon S3 bucket the trained model will persist.

   Show answer

   Answer: ACF

   Explanation

   From here https://docs.aws.amazon.com/zh_tw/sagemaker/latest/dg/API_CreateTrainingJob.html .. the only "Required: Yes" attributes are: 1. AlgorithmSpecification (in this TrainingInputMode is Required - i.e. File or Pipe) 2. OutputDataConfig (in this S3OutputPath is Required - where the model artifacts are stored) 3. ResourceConfig (in this EC2 InstanceType and VolumeSizeInGB are required) 4. RoleArn (..The Amazon Resource Name (ARN) of an IAM role that Amazon SageMaker can assume to perform tasks on your behalf...the caller of this API must have the iam:PassRole permission.) 5. StoppingCondition 6. TrainingJobName (The name of the training job. The name must be unique within an AWS Region in an AWS account.) From the given options in the questions.. we have 2, 3, and 4 above. so, the answer is CEF.

   Source: Adapted from community discussion — VB

   References

   • AWS documentation
   • AWS documentation
   • AWS documentation
   • AWS documentation
   • AWS documentation

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4. Question 224 · 368 in bank

   Select 2

   A university wants to develop a targeted recruitment strategy to increase new student enrollment. A data scientist gathers information about the academic performance history of students. The data scientist wants to use the data to build student profiles. The university will use the profiles to direct resources to recruit students who are likely to enroll in the university. Which combination of steps should the data scientist take to predict whether a particular student applicant is likely to enroll in the university? (Choose two.)

   • AUse Amazon SageMaker Ground Truth to sort the data into two groups named "enrolled" or "not enrolled."
   • BUse a forecasting algorithm to run predictions.
   • CUse a regression algorithm to run predictions.
   • DUse a classification algorithm to run predictions.
   • EUse the built-in Amazon SageMaker k-means algorithm to cluster the data into two groups named "enrolled" or "not enrolled."

   Show answer

   Answer: AD

   Explanation

   The data scientist should use Amazon SageMaker Ground Truth to sort the data into two groups named "enrolled" or "not enrolled." This will create a labeled dataset that can be used for supervised learning. The data scientist should then use a classification algorithm to run predictions on the test data. A classification algorithm is a suitable choice for predicting a binary outcome, such as enrollment status, based on the input features, such as academic performance. A classification IT Certification Guaranteed, The Easy Way! 163 algorithm will output a probability for each class label and assign the most likely label to each observation. References: Use Amazon SageMaker Ground Truth to Label Data Classification Algorithm in Machine Learning

   Source: Adapted from community discussion — kyuhuck

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5. Question 225 · 368 in bank

   Select 2

   A machine learning (ML) specialist is using the Amazon SageMaker DeepAR forecasting algorithm to train a model on CPU-based Amazon EC2 On-Demand instances. The model currently takes multiple hours to train. The ML specialist wants to decrease the training time of the model. Which approaches will meet this requirement? (Choose two.)

   • AReplace On-Demand Instances with Spot Instances.
   • BConfigure model auto scaling dynamically to adjust the number of instances automatically.
   • CReplace CPU-based EC2 instances with GPU-based EC2 instances.
   • DUse multiple training instances.
   • EUse a pre-trained version of the model. Run incremental training.

   Show answer

   Answer: CD

   Explanation

   The best approaches to decrease the training time of the model are C and D, because they can improve the computational efficiency and parallelization of the training process. These approaches have the following benefits: C: Replacing CPU-based EC2 instances with GPU-based EC2 instances can speed up the training of the DeepAR algorithm, as it can leverage the parallel processing power of GPUs to perform matrix operations and gradient computations faster than CPUs12. The DeepAR algorithm supports GPUbased EC2 instances such as ml.p2 and ml.p33. D: Using multiple training instances can also reduce the training time of the DeepAR algorithm, as it can distribute the workload across multiple nodes and perform data parallelism4. The DeepAR algorithm supports distributed training with multiple CPU-based or GPU-based EC2 instances3. The other options are not effective or relevant, because they have the following drawbacks:

   Source: Adapted from community discussion — kyuhuck

   References

   • AWS documentation

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6. Question 226 · 368 in bank

   A chemical company has developed several machine learning (ML) solutions to identify chemical process abnormalities. The time series values of independent variables and the labels are available for the past 2 years and are sufficient to accurately model the problem. The regular operation label is marked as 0 The abnormal operation label is marked as 1. Process abnormalities have a significant negative effect on the company’s profits. The company must avoid these abnormalities. Which metrics will indicate an ML solution that will provide the GREATEST probability of detecting an abnormality?

   • APrecision = 0.91 - Recall = 0.6
   • BPrecision = 0.61 - Recall = 0.98
   • CPrecision = 0.7 - Recall = 0.9
   • DPrecision = 0.98 - Recall = 0.8

   Show answer

   Answer: B

   Explanation

   To maximize the probability of detecting an abnormality, the focus should be on high recall (the ability of the model to find all actual positives), especially in scenarios where missing an abnormality could have significant negative effects. Between the given options: B. Precision = 0.61 - Recall = 0.98 This option has the highest recall, meaning it is best at identifying actual abnormalities (label 1), which is crucial for minimizing the risk of undetected process abnormalities. Although precision is lower (indicating more false positives), in this context, ensuring abnormalities are detected (even at the cost of investigating more false alarms) is more critical.

   Source: Adapted from community discussion — Alice1234

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7. Question 227 · 368 in bank

   An 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?

   • ACreate 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.
   • BPrepare 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.
   • CKeep 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.
   • DPrepare 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.

   Show answer

   Answer: B

   Explanation

   A multi-model endpoint in Amazon SageMaker is an endpoint that can host multiple machine learning models simultaneously. This allows you to deploy and manage multiple models on a single endpoint, reducing operational costs and simplifying deployment and management tasks. Each model is associated with a specific container image and can be invoked using a unique model name or endpoint name. This feature is useful when you have multiple models that need to be deployed together or when you want to reduce the number of endpoints that need to be managed.

   Source: Adapted from community discussion — rav009

   References

   • AWS documentation

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8. Question 228 · 368 in bank

   A company builds computer-vision models that use deep learning for the autonomous vehicle industry. A machine learning (ML) specialist uses an Amazon EC2 instance that has a CPU:GPU ratio of 12:1 to train the models. The ML specialist examines the instance metric logs and notices that the GPU is idle half of the time. The ML specialist must reduce training costs without increasing the duration of the training jobs. Which solution will meet these requirements?

   • ASwitch to an instance type that has only CPUs.
   • BUse a heterogeneous cluster that has two different instances groups.
   • CUse memory-optimized EC2 Spot Instances for the training jobs.
   • DSwitch to an instance type that has a CPU:GPU ratio of 6:1.

   Show answer

   Answer: D

   Explanation

   The topic is wierd. GPU is idle, which means CPU is not able to feed data to GPU in time, which means more CPU is needed. The current ratio is 12:1, and you need to increase the ratio. D is wrong because 6:1 is smaller than 12:1

   Source: Adapted from community discussion — rav009

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9. Question 229 · 368 in bank

   A company wants to forecast the daily price of newly launched products based on 3 years of data for older product prices, sales, and rebates. The time-series data has irregular timestamps and is missing some values. Data scientist must build a dataset to replace the missing values. The data scientist needs a solution that resamples the data daily and exports the data for further modeling. Which solution will meet these requirements with the LEAST implementation effort?

   • AUse Amazon EMR Serverless with PySpark.
   • BUse AWS Glue DataBrew.
   • CUse Amazon SageMaker Studio Data Wrangler.
   • DUse Amazon SageMaker Studio Notebook with Pandas.

   Show answer

   Answer: C

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10. Question 230 · 368 in bank

    A data scientist is building a forecasting model for a retail company by using the most recent 5 years of sales records that are stored in a data warehouse. The dataset contains sales records for each of the company’s stores across five commercial regions. The data scientist creates a working dataset with StoreID. Region. Date, and Sales Amount as columns. The data scientist wants to analyze yearly average sales for each region. The scientist also wants to compare how each region performed compared to average sales across all commercial regions. Which visualization will help the data scientist better understand the data trend?

    • ACreate an aggregated dataset by using the Pandas GroupBy function to get average sales for each year for each store. Create a bar plot, faceted by year, of average sales for each store. Add an extra bar in each facet to represent average sales.
    • BCreate an aggregated dataset by using the Pandas GroupBy function to get average sales for each year for each store. Create a bar plot, colored by region and faceted by year, of average sales for each store. Add a horizontal line in each facet to represent average sales.
    • CCreate an aggregated dataset by using the Pandas GroupBy function to get average sales for each year for each region. Create a bar plot of average sales for each region. Add an extra bar in each facet to represent average sales.
    • DCreate an aggregated dataset by using the Pandas GroupBy function to get average sales for each year for each region. Create a bar plot, faceted by year, of average sales for each region. Add a horizontal line in each facet to represent average sales.

    Show answer

    Answer: D

    Explanation

    Explanation: The best visualization for this task is to create a bar plot, faceted by year, of average sales for each region and add a horizontal line in each facet to represent average sales. This way, the data scientist can easily compare the yearly average sales for each region with the overall average sales and see the IT Certification Guaranteed, The Easy Way! 170 trends over time. The bar plot also allows the data scientist to see the relative performance of each region within each year and across years. The other options are less effective because they either do not show the yearly trends, do not show the overall average sales, or do not group the data by region. References: pandas.DataFrame.groupby - pandas 2.1.4 documentation pandas.DataFrame.plot.bar - pandas 2.1.4 documentation Matplotlib - Bar Plot - Online Tutorials Library

    Source: Adapted from community discussion — kyuhuck

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MLS-C01 practice, question 221. 368 questions available.