MLS-C01 practice questions 351–360

1. Question 351 · 368 in bank

   A real estate company wants to create a machine learning model for predicting housing prices based on a historical dataset. The dataset contains 32 features. Which model will meet the business requirement?

   • ALogistic regression
   • BLinear regression
   • CK-means
   • DPrincipal component analysis (PCA)

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   Answer: B

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2. Question 352 · 368 in bank

   A Machine Learning Specialist is applying a linear least squares regression model to a dataset with 1,000 records and 50 features. Prior to training, the ML Specialist notices that two features are perfectly linearly dependent. Why could this be an issue for the linear least squares regression model?

   • AIt could cause the backpropagation algorithm to fail during training
   • BIt could create a singular matrix during optimization, which fails to define a unique solution
   • CIt could modify the loss function during optimization, causing it to fail during training
   • DIt could introduce non-linear dependencies within the data, which could invalidate the linear assumptions of the model

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   Answer: B

   Explanation

   B: If two features in the dataset are perfectly linearly dependent, it means that one feature can be expressed as a linear combination of the other. This can create a singular matrix during optimization, as the linear model would be trying to fit a linear equation to a dataset where one variable is fully determined by the other. This would lead to an ill-defined optimization problem, as there would be no unique solution that minimizes the sum of the squares of the residuals. This could lead to problems during training, as the model would not be able to find appropriate parameter values to fit the data.

   Source: Adapted from community discussion — Sneep

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3. Question 353 · 368 in bank

   Given the following confusion matrix for a movie classification model, what is the true class frequency for Romance and the predicted class frequency for Adventure?

   

   • AThe true class frequency for Romance is 77.56% and the predicted class frequency for Adventure is 20.85%
   • BThe true class frequency for Romance is 57.92% and the predicted class frequency for Adventure is 13.12%
   • CThe true class frequency for Romance is 0.78 and the predicted class frequency for Adventure is (0.47-0.32)
   • DThe true class frequency for Romance is 77.56% ֳ— 0.78 and the predicted class frequency for Adventure is 20.85% ֳ— 0.32

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   Answer: B

   References

   • AWS documentation

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4. Question 354 · 368 in bank

   A Machine Learning Specialist wants to bring a custom algorithm to Amazon SageMaker. The Specialist implements the algorithm in a Docker container supported by Amazon SageMaker. How should the Specialist package the Docker container so that Amazon SageMaker can launch the training correctly?

   • AModify the bash_profile file in the container and add a bash command to start the training program
   • BUse CMD config in the Dockerfile to add the training program as a CMD of the image
   • CConfigure the training program as an ENTRYPOINT named train
   • DCopy the training program to directory /opt/ml/train

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   Answer: C

   Explanation

   I would answer C: https://docs.aws.amazon.com/sagemaker/latest/dg/your-algorithms-training-algo-dockerfile.html "To configure a Docker container to run as an executable, use an ENTRYPOINT instruction in a Dockerfile. SageMaker overrides any default CMD statement in a container by specifying the train argument after the image name"

   Source: Adapted from community discussion — [Removed]

   References

   • AWS documentation

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5. Question 355 · 368 in bank

   Select 2

   A Data Scientist needs to analyze employment data. The dataset contains approximately 10 million observations on people across 10 different features. During the preliminary analysis, the Data Scientist notices that income and age distributions are not normal. While income levels shows a right skew as expected, with fewer individuals having a higher income, the age distribution also shows a right skew, with fewer older individuals participating in the workforce. Which feature transformations can the Data Scientist apply to fix the incorrectly skewed data? (Choose two.)

   • ACross-validation
   • BNumerical value binning
   • CHigh-degree polynomial transformation
   • DLogarithmic transformation
   • EOne hot encoding

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   Answer: BD

   Explanation

   B because we have skewed data with few exeptions D log transform can change distribution of data not C - because there is no indicaiton in the text, that data is following any of the HIGH DEGREE polynomial distribution like x^ 10

   Source: Adapted from community discussion — Juka3lj

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6. Question 356 · 368 in bank

   A web-based company wants to improve its conversion rate on its landing page. Using a large historical dataset of customer visits, the company has repeatedly trained a multi-class deep learning network algorithm on Amazon SageMaker. However, there is an overfitting problem: training data shows 90% accuracy in predictions, while test data shows 70% accuracy only. The company needs to boost the generalization of its model before deploying it into production to maximize conversions of visits to purchases. Which action is recommended to provide the HIGHEST accuracy model for the company's test and validation data?

   • AIncrease the randomization of training data in the mini-batches used in training
   • BAllocate a higher proportion of the overall data to the training dataset
   • CApply L1 or L2 regularization and dropouts to the training
   • DReduce the number of layers and units (or neurons) from the deep learning network

   Show answer

   Answer: C

   Explanation

   I think C will be answer, because we even don't know how many layers now, so apply L1,L2 and dropouts layer will be first resort to solve overfitting. If it still does not work, then to reduce layers

   Source: Adapted from community discussion — knightknt

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7. Question 357 · 368 in bank

   A Machine Learning Specialist is given a structured dataset on the shopping habits of a company's customer base. The dataset contains thousands of columns of data and hundreds of numerical columns for each customer. The Specialist wants to identify whether there are natural groupings for these columns across all customers and visualize the results as quickly as possible. What approach should the Specialist take to accomplish these tasks?

   • AEmbed the numerical features using the t-distributed stochastic neighbor embedding (t-SNE) algorithm and create a scatter plot.
   • BRun k-means using the Euclidean distance measure for different values of k and create an elbow plot.
   • CEmbed the numerical features using the t-distributed stochastic neighbor embedding (t-SNE) algorithm and create a line graph.
   • DRun k-means using the Euclidean distance measure for different values of k and create box plots for each numerical column within each cluster.

   Show answer

   Answer: A

   Explanation

   A is definitely the correct answer. Pay attention to what the question is asking: "whether there are natural groupings for these columns across all customers and visualize the results as quickly as possible" The key point is to visualize the "groupings"(exactly what t-SNE scatter plot does, it visualize high-dimensional data points on 2D space). The question does not ask to visualize how many groups you would classify (K-Means Elbow Plot does not visualize the groupings, it is used to determine the optimal # of groups=K).

   Source: Adapted from community discussion — SophieSu

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8. Question 358 · 368 in bank

   A Machine Learning Specialist is developing a custom video recommendation model for an application. The dataset used to train this model is very large with millions of data points and is hosted in an Amazon S3 bucket. The Specialist wants to avoid loading all of this data onto an Amazon SageMaker notebook instance because it would take hours to move and will exceed the attached 5 GB Amazon EBS volume on the notebook instance. Which approach allows the Specialist to use all the data to train the model?

   • ALoad a smaller subset of the data into the SageMaker notebook and train locally. Confirm that the training code is executing and the model parameters seem reasonable. Initiate a SageMaker training job using the full dataset from the S3 bucket using Pipe input mode.
   • BLaunch an Amazon EC2 instance with an AWS Deep Learning AMI and attach the S3 bucket to the instance. Train on a small amount of the data to verify the training code and hyperparameters. Go back to Amazon SageMaker and train using the full dataset
   • CUse AWS Glue to train a model using a small subset of the data to confirm that the data will be compatible with Amazon SageMaker. Initiate a SageMaker training job using the full dataset from the S3 bucket using Pipe input mode.
   • DLoad a smaller subset of the data into the SageMaker notebook and train locally. Confirm that the training code is executing and the model parameters seem reasonable. Launch an Amazon EC2 instance with an AWS Deep Learning AMI and attach the S3 bucket to train the full dataset.

   Show answer

   Answer: A

   Explanation

   Answer is A. The answer to this question is about Pipe mode from S3. The only options are A and C. As AWS Glue cannot be use to create models which is option C. The correct answer is A

   Source: Adapted from community discussion — JayK

   References

   • AWS documentation

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9. Question 359 · 368 in bank

   A Machine Learning Specialist is planning to create a long-running Amazon EMR cluster. The EMR cluster will have 1 master node, 10 core nodes, and 20 task nodes. To save on costs, the Specialist will use Spot Instances in the EMR cluster. Which nodes should the Specialist launch on Spot Instances?

   • AMaster node
   • BAny of the core nodes
   • CAny of the task nodes
   • DBoth core and task nodes

   Show answer

   Answer: C

   Explanation

   C is the correct answer. "Long-Running Clusters and Data Warehouses If you are running a persistent Amazon EMR cluster that has a predictable variation in computational capacity, such as a data warehouse, you can handle peak demand at lower cost with Spot Instances. You can launch your master and core instance groups as On-Demand Instances to handle the normal capacity and launch task instance groups as Spot Instances to handle your peak load requirements."

   Source: Adapted from community discussion — SophieSu

   References

   • AWS documentation
   • AWS documentation

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10. Question 360 · 368 in bank

    A manufacturer of car engines collects data from cars as they are being driven. The data collected includes timestamp, engine temperature, rotations per minute (RPM), and other sensor readings. The company wants to predict when an engine is going to have a problem, so it can notify drivers in advance to get engine maintenance. The engine data is loaded into a data lake for training. Which is the MOST suitable predictive model that can be deployed into production?

    • AAdd labels over time to indicate which engine faults occur at what time in the future to turn this into a supervised learning problem. Use a recurrent neural network (RNN) to train the model to recognize when an engine might need maintenance for a certain fault.
    • BThis data requires an unsupervised learning algorithm. Use Amazon SageMaker k-means to cluster the data.
    • CAdd labels over time to indicate which engine faults occur at what time in the future to turn this into a supervised learning problem. Use a convolutional neural network (CNN) to train the model to recognize when an engine might need maintenance for a certain fault.
    • DThis data is already formulated as a time series. Use Amazon SageMaker seq2seq to model the time series.

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    Answer: A

    Explanation

    This is a supervised problem and needs labels. Can't use clustering to find when faults can happen. CNN is for images not for timeseries data here. Hence, A seems appropriate.

    Source: Adapted from community discussion — ac71

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MLS-C01 practice, question 351. 368 questions available.