MLS-C01 practice questions 161–170

1. Question 161 · 368 in bank

   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?

   • ACreate 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.
   • BCreate 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.
   • CCreate 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.
   • DCreate 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.

   Show answer

   Answer: C

   Explanation

   Amazon SageMaker Data Wrangler is a visual data preparation tool that makes it easy to clean, transform, and featurize data for machine learning. It provides a variety of built-in transformations, including the Featurize date/time transform, which can be used to generate the new variables from the timestamp variable. The other options require the data scientist to develop code, which can be more time-consuming and error-prone. Amazon EMR and AWS Glue are both batch processing services that can be used to run Python code. However, they require the data scientist to create and manage a cluster, which can be a significant operational overhead. Amazon SageMaker Processing is a serverless processing service that can also be used to run Python code. However, it is more expensive than Data Wrangler and does not provide the same level of visual tooling.

   Source: Adapted from community discussion — Mickey321

   References

   • AWS documentation

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2. Question 162 · 368 in bank

   A manufacturing company has a production line with sensors that collect hundreds of quality metrics. The company has stored sensor data and manual inspection results in a data lake for several months. To automate quality control, the machine learning team must build an automated mechanism that determines whether the produced goods are good quality, replacement market quality, or scrap quality based on the manual inspection results. Which modeling approach will deliver the MOST accurate prediction of product quality?

   • AAmazon SageMaker DeepAR forecasting algorithm
   • BAmazon SageMaker XGBoost algorithm
   • CAmazon SageMaker Latent Dirichlet Allocation (LDA) algorithm
   • DA convolutional neural network (CNN) and ResNet

   Show answer

   Answer: B

   Explanation

   A. NO - it is not a foreacasting problem B. YES - it is a supervised classification problem C. NO - LDA is for topic modeling, sentiment analysis D. NO - ResNet (a type of CNN) is for image recognition

   Source: Adapted from community discussion — loict

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3. Question 163 · 368 in bank

   A company wants to classify user behavior as either fraudulent or normal. Based on internal research, a Machine Learning Specialist would like to build a binary classifier based on two features: age of account and transaction month. The class distribution for these features is illustrated in the figure provided.

   

   Based on this information, which model would have the HIGHEST recall with respect to the fraudulent class?

   • ADecision tree
   • BLinear support vector machine (SVM)
   • CNaive Bayesian classifier
   • DSingle Perceptron with sigmoidal activation function

   Show answer

   Answer: A

   Explanation

   Answer should be A:. B: LINEAR SVM is a linear classifier -> All of these have a linear decision boundary (so it's just a line y = mx+b). This leads to a bad recall and so A must be the right choice.

   Source: Adapted from community discussion — blubb

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4. Question 164 · 368 in bank

   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?

   • AAmazon S3
   • BAmazon Elastic Block Store (Amazon EBS)
   • CAWS CloudTrail
   • DAmazon CloudWatch

   Show answer

   Answer: D

   Explanation

   Amazon CloudWatch is a monitoring and logging service provided by AWS. It collects and stores log files and metrics from various AWS services, including Amazon SageMaker. CloudWatch allows you to gain visibility into your applications and infrastructure by providing a unified view of logs, metrics, and events.

   Source: Adapted from community discussion — Mickey321

   References

   • repost.aws documentation

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5. Question 165 · 368 in bank

   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?

   • ASupervised learning
   • BUnsupervised learning
   • CSemi-supervised learning
   • DReinforcement learning

   Show answer

   Answer: D

   Explanation

   Reinforcement learning allows the bot to continuously learn from its own experiences, adapt to changing market conditions, and optimize its decision-making process over time. It is well-suited for dynamic and uncertain environments like financial markets, where the optimal trading strategies may vary depending on various factors and trends.

   Source: Adapted from community discussion — Mickey321

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6. Question 166 · 368 in bank

   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?

   • ATurn 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.
   • BTurn 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.
   • CAdjust 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.
   • DTurn 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.

   Show answer

   Answer: B

   Explanation

   A. NO - should use Spot instances B. YES - makes use of compiler and spot instances C. NO - that is to minimize training time, not reduced cost D. NO - MaxWaitTimeInSeconds is time to find a free spot instance, it is unrelated to MaxRuntimeInSeconds which is processing time once an istance has been acquired

   Source: Adapted from community discussion — loict

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7. Question 167 · 368 in bank

   An automotive company uses computer vision in its autonomous cars. The company trained its object detection models successfully by using transfer learning from a convolutional neural network (CNN). The company trained the models by using PyTorch through the Amazon SageMaker SDK. The vehicles have limited hardware and compute power. The company wants to optimize the model to reduce memory, battery, and hardware consumption without a significant sacrifice in accuracy. Which solution will improve the computational efficiency of the models?

   • AUse Amazon CloudWatch metrics to gain visibility into the SageMaker training weights, gradients, biases, and activation outputs. Compute the filter ranks based on the training information. Apply pruning to remove the low-ranking filters. Set new weights based on the pruned set of filters. Run a new training job with the pruned model.
   • BUse Amazon SageMaker Ground Truth to build and run data labeling workflows. Collect a larger labeled dataset with the labelling workflows. Run a new training job that uses the new labeled data with previous training data.
   • CUse Amazon SageMaker Debugger to gain visibility into the training weights, gradients, biases, and activation outputs. Compute the filter ranks based on the training information. Apply pruning to remove the low-ranking filters. Set the new weights based on the pruned set of filters. Run a new training job with the pruned model.
   • DUse Amazon SageMaker Model Monitor to gain visibility into the ModelLatency metric and OverheadLatency metric of the model after the company deploys the model. Increase the model learning rate. Run a new training job.

   Show answer

   Answer: C

   Explanation

   A. NO - CloudWatch cannot get filter ranks of filters. Run a new training job with the pruned model. B. NO - Ground Truth can help improve model performance, not reduce inference cost C. YES - Filter pruning based on ranks is specific to CNN and supported by SageMaker Debugger D. NO - learning rate impacts training, not inference

   Source: Adapted from community discussion — loict

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8. Question 168 · 368 in bank

   Select 3

   A data scientist wants to improve the fit of a machine learning (ML) model that predicts house prices. The data scientist makes a first attempt to fit the model, but the fitted model has poor accuracy on both the training dataset and the test dataset. Which steps must the data scientist take to improve model accuracy? (Choose three.)

   • AIncrease the amount of regularization that the model uses.
   • BDecrease the amount of regularization that the model uses.
   • CIncrease the number of training examples that that model uses.
   • DIncrease the number of test examples that the model uses.
   • EIncrease the number of model features that the model uses.
   • FDecrease the number of model features that the model uses.

   Show answer

   Answer: BCE

   Explanation

   BCE B. Decrease the amount of regularization that the model uses: Regularization is used to prevent overfitting, but if the fitted model has poor accuracy on both the training and test datasets, reducing the amount of regularization can help the model better capture the underlying patterns and improve its accuracy. C. Increase the number of training examples that the model uses: Increasing the number of training examples allows the model to learn from a larger and more diverse dataset, which can help improve its ability to generalize and make accurate predictions. E. Increase the number of model features that the model uses: Adding more relevant features to the model can enhance its ability to capture important patterns and relationships in the data, leading to improved accuracy.

   Source: Adapted from community discussion — RRST

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9. Question 169 · 368 in bank

   A car company is developing a machine learning solution to detect whether a car is present in an image. The image dataset consists of one million images. Each image in the dataset is 200 pixels in height by 200 pixels in width. Each image is labeled as either having a car or not having a car. Which architecture is MOST likely to produce a model that detects whether a car is present in an image with the highest accuracy?

   • AUse a deep convolutional neural network (CNN) classifier with the images as input. Include a linear output layer that outputs the probability that an image contains a car.
   • BUse a deep convolutional neural network (CNN) classifier with the images as input. Include a softmax output layer that outputs the probability that an image contains a car.
   • CUse a deep multilayer perceptron (MLP) classifier with the images as input. Include a linear output layer that outputs the probability that an image contains a car.
   • DUse a deep multilayer perceptron (MLP) classifier with the images as input. Include a softmax output layer that outputs the probability that an image contains a car.

   Show answer

   Answer: B

   Explanation

   A. NO - linear output is not B. YES - CNN is good, softmax output is good C. NO - MLP is not good D. NO - MLP is not good

   Source: Adapted from community discussion — loict

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10. Question 170 · 368 in bank

    Select 2

    A company is creating an application to identify, count, and classify animal images that are uploaded to the company’s website. The company is using the Amazon SageMaker image classification algorithm with an ImageNetV2 convolutional neural network (CNN). The solution works well for most animal images but does not recognize many animal species that are less common. The company obtains 10,000 labeled images of less common animal species and stores the images in Amazon S3. A machine learning (ML) engineer needs to incorporate the images into the model by using Pipe mode in SageMaker. Which combination of steps should the ML engineer take to train the model? (Choose two.)

    • AUse a ResNet model. Initiate full training mode by initializing the network with random weights.
    • BUse an Inception model that is available with the SageMaker image classification algorithm.
    • CCreate a .lst file that contains a list of image files and corresponding class labels. Upload the .lst file to Amazon S3.
    • DInitiate transfer learning. Train the model by using the images of less common species.
    • EUse an augmented manifest file in JSON Lines format.

    Show answer

    Answer: CD

    Explanation

    A. NO - we can't change the model for transfer learning B. NO - we can't change the model for transfer learning C. YES - lst file is how we give input to SageMaker (https://medium.com/@texasdave2/itty-bitty-lst-file-format-converter-for-machine-learning-image-classification-on-aws-sagemaker-b3828c7ba9cc) D. YES - obvious E. NO - there is no extra metadata we want to provide (https://docs.aws.amazon.com/sagemaker/latest/dg/augmented-manifest.html)

    Source: Adapted from community discussion — loict

    References

    • AWS documentation
    • AWS documentation
    • AWS documentation

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MLS-C01 practice, question 161. 368 questions available.