30 Free AWS MLS-C01 Practice Questions

MLS-C01 Practice Questions

1. Question 1.A large mobile network operating company is building a machine learning model to predict customers who are likely to unsubscribe from the service. The company plans to offer an incentive for these customers as the cost of churn is far greater than the cost of the incentive. The model produces the following confusion matrix after evaluating on a test dataset of 100 customers. Based on the model evaluation results, why is this a viable model for production? 

   • A.The model is 86% accurate and the cost incurred by the company as a result of false negatives is less than the false positives.
   • B.The precision of the model is 86%, which is less than the accuracy of the model.
   • C.The model is 86% accurate and the cost incurred by the company as a result of false positives is less than the false negatives.(correct answer)
   • D.The precision of the model is 86%, which is greater than the accuracy of the model.
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   Correct answer: C

   The model is 86% accurate and the cost incurred by the company as a result of false positives is less than the false negatives.

   Explanation

   Accuracy is (TP+TN)/Total. (13+73)/100 = 86%. In this scenario, False Negatives (predicting no churn when they will) are more costly than False Positives (giving incentives to those who wouldn't churn). The model captures most churners.

2. Question 2.A Machine Learning Specialist is designing a system for improving sales for a company. The objective is to use the large amount of information the company has on users' behavior and product preferences to predict which products users would like based on the users' similarity to other users. What should the Specialist do to meet this objective?

   • A.Build a content-based filtering recommendation engine with Apache Spark ML on Amazon EMR.
   • B.Build a collaborative filtering recommendation engine with Apache Spark ML on Amazon EMR.(correct answer)
   • C.Build a model-based filtering recommendation engine with Apache Spark ML on Amazon EMR.
   • D.Build a combinative filtering recommendation engine with Apache Spark ML on Amazon EMR.
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   Correct answer: B

   Build a collaborative filtering recommendation engine with Apache Spark ML on Amazon EMR.

   Explanation

   Collaborative filtering makes recommendations based on user similarity and shared preferences, whereas content-based filtering relies on item attributes.

3. Question 3.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?

   • A.Ingest .CSV data using Apache Kafka Streams on Amazon EC2 instances and use Kafka Connect S3 to serialize data as Parquet
   • B.Ingest .CSV data from Amazon Kinesis Data Streams and use Amazon Glue to convert data into Parquet.
   • C.Ingest .CSV data using Apache Spark Structured Streaming in an Amazon EMR cluster and use Apache Spark to convert data into Parquet.
   • D.Ingest .CSV data from Amazon Kinesis Data Streams and use Amazon Kinesis Data Firehose to convert data into Parquet.(correct answer)
   Show answer & explanationHide answer

   Correct answer: D

   Ingest .CSV data from Amazon Kinesis Data Streams and use Amazon Kinesis Data Firehose to convert data into Parquet.

   Explanation

   Amazon Kinesis Data Firehose can natively convert CSV data into Parquet or ORC format before delivering it to S3, provided an AWS Glue schema is defined.

4. Question 4.A city wants to monitor its air quality to address the consequences of air pollution. A Machine Learning Specialist needs to forecast the air quality in parts per million of contaminates for the next 2 days in the city. As this is a prototype, only daily data from the last year is available. Which model is MOST likely to provide the best results in Amazon SageMaker?

   • A.Use the Amazon SageMaker k-Nearest-Neighbors (kNN) algorithm on the single time series consisting of the full year of data with a predictor_typeof regressor.
   • B.Use Amazon SageMaker Random Cut Forest (RCF) on the single time series consisting of the full year of data.
   • C.Use the Amazon SageMaker Linear Learner algorithm on the single time series consisting of the full year of data with a predictor_typeof regressor.(correct answer)
   • D.Use the Amazon SageMaker Linear Learner algorithm on the single time series consisting of the full year of data with a predictor_typeof classifier.
   Show answer & explanationHide answer

   Correct answer: C

   Use the Amazon SageMaker Linear Learner algorithm on the single time series consisting of the full year of data with a predictor_typeof regressor.

   Explanation

   For time series forecasting with a single time series, the Linear Learner regressor is a suitable choice in SageMaker, especially when data is limited to a year of daily observations.

5. Question 5.A Data Engineer needs to build a model using a dataset containing customer credit card information. How can the Data Engineer ensure the data remains encrypted and the credit card information is secure?

   • A.Use a custom encryption algorithm to encrypt the data and store the data on an Amazon SageMaker instance in a VPC. Use the SageMaker DeepAR algorithm to randomize the credit card numbers..
   • B.Use an IAM policy to encrypt the data on the Amazon S3 bucket and Amazon Kinesis to automatically discard credit card numbers and insert fake credit card numbers.
   • C.Use an Amazon SageMaker launch configuration to encrypt the data once it is copied to the SageMaker instance in a VPC. Use the SageMaker Principal Component Analysis (PCA) algorithm to reduce the length of the credit card numbers.
   • D.Use AWS KMS to encrypt the data on Amazon S3 and Amazon SageMaker, and redact the credit card numbers from the customer data with AWS Glue.(correct answer)
   Show answer & explanationHide answer

   Correct answer: D

   Use AWS KMS to encrypt the data on Amazon S3 and Amazon SageMaker, and redact the credit card numbers from the customer data with AWS Glue.

   Explanation

   AWS Glue can be used to redact sensitive information like credit card numbers. AWS KMS provides encryption at rest for S3 and SageMaker volumes.

6. Question 6.A Machine Learning Specialist is using an Amazon SageMaker notebook instance in a private subnet of a corporate VPC. The ML Specialist has important data stored on the Amazon SageMaker notebook instance's Amazon EBS volume, and needs to take a snapshot of that EBS volume. However, the ML Specialist cannot find the Amazon SageMaker notebook instance's EBS volume or Amazon EC2 instance within the VPC. Why is the ML Specialist not seeing the instance visible in the VPC?

   • A.Amazon SageMaker notebook instances are based on the EC2 instances within the customer account, but they run outside of VPCs.
   • B.Amazon SageMaker notebook instances are based on the Amazon ECS service within customer accounts.
   • C.Amazon SageMaker notebook instances are based on EC2 instances running within AWS service accounts.(correct answer)
   • D.Amazon SageMaker notebook instances are based on AWS ECS instances running within AWS service accounts.
   Show answer & explanationHide answer

   Correct answer: C

   Amazon SageMaker notebook instances are based on EC2 instances running within AWS service accounts.

   Explanation

   Amazon SageMaker notebook instances run on EC2 instances within AWS service-managed accounts. While they can be connected to a customer VPC, they remain managed by the SageMaker service.

7. Question 7.A Machine Learning Specialist is building a model that will perform time series forecasting using Amazon SageMaker. The Specialist has finished training the model and is now planning to perform load testing on the endpoint so they can configure Auto Scaling for the model variant. Which approach will allow the Specialist to review the latency, memory utilization, and CPU utilization during the load test?

   • A.Review SageMaker logs that have been written to Amazon S3 by leveraging Amazon Athena and Amazon QuickSight to visualize logs as they are being produced.
   • B.Generate an Amazon CloudWatch dashboard to create a single view for the latency, memory utilization, and CPU utilization metrics that are outputted by Amazon SageMaker.(correct answer)
   • C.Build custom Amazon CloudWatch Logs and then leverage Amazon ES and Kibana to query and visualize the log data as it is generated by Amazon SageMaker.
   • D.Send Amazon CloudWatch Logs that were generated by Amazon SageMaker to Amazon ES and use Kibana to query and visualize the log data.
   Show answer & explanationHide answer

   Correct answer: B

   Generate an Amazon CloudWatch dashboard to create a single view for the latency, memory utilization, and CPU utilization metrics that are outputted by Amazon SageMaker.

   Explanation

   Amazon CloudWatch provides built-in metrics (latency, CPU, Memory) for SageMaker endpoints, and dashboards can consolidate these for easy review.

8. Question 8.A manufacturing company has structured and unstructured data stored in an Amazon S3 bucket. A Machine Learning Specialist wants to use SQL to run queries on this data. Which solution requires the LEAST effort to be able to query this data?

   • A.Use AWS Data Pipeline to transform the data and Amazon RDS to run queries.
   • B.Use AWS Glue to catalogue the data and Amazon Athena to run queries.(correct answer)
   • C.Use AWS Batch to run ETL on the data and Amazon Aurora to run the queries.
   • D.Use AWS Lambda to transform the data and Amazon Kinesis Data Analytics to run queries.
   Show answer & explanationHide answer

   Correct answer: B

   Use AWS Glue to catalogue the data and Amazon Athena to run queries.

   Explanation

   AWS Glue Crawler can catalog unstructured and structured data in S3, and Amazon Athena allows running standard SQL queries directly against that data.

9. Question 9.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?

   • A.Load 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.(correct answer)
   • B.Launch 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
   • C.Use 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.
   • D.Load 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 & explanationHide answer

   Correct answer: A

   Load 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.

   Explanation

   Using Pipe input mode allows SageMaker to stream data directly from S3 to the training instance, avoiding disk space limitations.

10. Question 10.A Machine Learning Specialist has completed a proof of concept for a company using a small data sample, and now the Specialist is ready to implement an end-to-end solution in AWS using Amazon SageMaker. The historical training data is stored in Amazon RDS. Which approach should the Specialist use for training a model using that data?

    • A.Write a direct connection to the SQL database within the notebook and pull data in.
    • B.Push the data from Microsoft SQL Server to Amazon S3 using an AWS Data Pipeline and provide the S3 location within the notebook.(correct answer)
    • C.Move the data to Amazon DynamoDB and set up a connection to DynamoDB within the notebook to pull data in.
    • D.Move the data to Amazon ElastiCache using AWS DMS and set up a connection within the notebook to pull data in for fast access.
    Show answer & explanationHide answer

    Correct answer: B

    Push the data from Microsoft SQL Server to Amazon S3 using an AWS Data Pipeline and provide the S3 location within the notebook.

    Explanation

    AWS Data Pipeline can orchestrate the movement of data from RDS to S3, which is the standard input source for SageMaker training.

11. Question 11.A Machine Learning Specialist receives customer data for an online shopping website. The data includes demographics, past visits, and locality information. The Specialist must develop a machine learning approach to identify the customer shopping patterns, preferences, and trends to enhance the website-for better service and smart recommendations. Which solution should the Specialist recommend?

    • A.Latent Dirichlet Allocation (LDA) for the given collection of discrete data to identify patterns in the customer database.
    • B.A neural network with a minimum of three layers and random initial weights to identify patterns in the customer database.
    • C.Collaborative filtering based on user interactions and correlations to identify patterns in the customer database.(correct answer)
    • D.Random Cut Forest (RCF) over random subsamples to identify patterns in the customer database.
    Show answer & explanationHide answer

    Correct answer: C

    Collaborative filtering based on user interactions and correlations to identify patterns in the customer database.

    Explanation

    Collaborative filtering is the primary technique for recommending products based on user interactions, preferences, and similarities.

12. Question 12.A Machine Learning Specialist is working with a large company to leverage machine learning within its products. The company wants to group its customers into categories based on which customers will and will not churn within the next 6 months. The company has labeled the data available to the Specialist. Which machine learning model type should the Specialist use to accomplish this task?

    • A.Linear Regression.
    • B.Classification.(correct answer)
    • C.Clustering.
    • D.Reinforcement learning.
    Show answer & explanationHide answer

    Correct answer: B

    Classification.

    Explanation

    Predicting whether a customer will or will not churn (two discrete categories) is a binary classification task.

13. Question 13.The displayed graph is from a forecasting model for testing a time series. Considering the graph only, which conclusion should a Machine Learning Specialist make about the behavior of the model? 

    • A.The model predicts both the trend and the seasonality well.(correct answer)
    • B.The model predicts the trend well, but not the seasonality.
    • C.The model predicts the seasonality well, but not the trend.
    • D.The model does not predict the trend or the seasonality well.
    Show answer & explanationHide answer

    Correct answer: A

    The model predicts both the trend and the seasonality well.

    Explanation

    A good forecasting model for time series should be able to capture both the underlying trend (general direction) and seasonality (repeating patterns).

14. Question 14.A company wants to classify user behavior as either fraudulent or normal. Based on internal research, a machine learning specialist will build a binary classifier based on two features: age of account, denoted by x, and transaction month, denoted by y. The class distributions are illustrated in the provided figure. The positive class is portrayed in red, while the negative class is portrayed in black. Which model would have the HIGHEST accuracy? 

    • A.Long short-term memory (LSTM) model with scaled exponential linear unit (SELU).
    • B.Logistic Regression.
    • C.Support vector machine (SVM) with non-linear kernel.(correct answer)
    • D.Single perceptron with tanh activation function.
    Show answer & explanationHide answer

    Correct answer: C

    Support vector machine (SVM) with non-linear kernel.

    Explanation

    The non-linear distribution of fraudulent vs normal classes suggests that a linear model (like Logistic Regression) would perform poorly. SVM with a non-linear kernel can capture complex decision boundaries.

15. Question 15.A Machine Learning Specialist at a company sensitive to security is preparing a dataset for model training. The dataset is stored in Amazon S3 and contains Personally Identifiable Information (PII). The dataset: Must be accessible from a VPC only. Must not traverse the public internet. How can these requirements be satisfied?

    • A.Create a VPC endpoint and apply a bucket access policy that restricts access to the given VPC endpoint and the VPC.(correct answer)
    • B.Create a VPC endpoint and apply a bucket access policy that allows access from the given VPC endpoint and an Amazon EC2 instance.
    • C.Create a VPC endpoint and use Network Access Control Lists (NACLs) to allow traffic between only the given VPC endpoint and an Amazon EC2 instance.
    • D.Create a VPC endpoint and use security groups to restrict access to the given VPC endpoint and an Amazon EC2 instance.
    Show answer & explanationHide answer

    Correct answer: A

    Create a VPC endpoint and apply a bucket access policy that restricts access to the given VPC endpoint and the VPC.

    Explanation

    VPC endpoints for S3 ensure data stays within the AWS network. Restricting bucket access to the VPC endpoint further secures the data.

16. Question 16.During mini-batch training of a neural network for a classification problem, a Data Scientist notices that training accuracy oscillates. What is the MOST likely cause of this issue?

    • A.The class distribution in the dataset is imbalanced.
    • B.Dataset shuffling is disabled.
    • C.The batch size is too big.
    • D.The learning rate is very high.(correct answer)
    Show answer & explanationHide answer

    Correct answer: D

    The learning rate is very high.

    Explanation

    A very high learning rate can cause the optimizer to overshoot the minimum, leading to oscillations in the loss and accuracy.

17. Question 17.An employee found a video clip with audio on a company's social media feed. The language used in the video is Spanish. English is the employee's first language, and they do not understand Spanish. The employee wants to do a sentiment analysis. What combination of services is the MOST efficient to accomplish the task?

    • A.Amazon Transcribe, Amazon Translate, and Amazon Comprehend.(correct answer)
    • B.Amazon Transcribe, Amazon Comprehend, and Amazon SageMaker seq2seq
    • C.Amazon Transcribe, Amazon Translate, and Amazon SageMaker Neural Topic Model (NTM)
    • D.Amazon Transcribe, Amazon Translate and Amazon SageMaker BlazingText.
    Show answer & explanationHide answer

    Correct answer: A

    Amazon Transcribe, Amazon Translate, and Amazon Comprehend.

    Explanation

    The workflow involves speech-to-text (Transcribe), language translation (Translate), and finally sentiment analysis of the text (Comprehend).

18. Question 18.A Machine Learning Specialist is packaging a custom ResNet model into a Docker container so the company can leverage Amazon SageMaker for training. The Specialist is using Amazon EC2 P3 instances to train the model and needs to properly configure the Docker container to leverage the NVIDIA GPUs. What does the Specialist need to do?

    • A.Bundle the NVIDIA drivers with the Docker image.
    • B.Build the Docker container to be NVIDIA-Docker compatible.(correct answer)
    • C.Organize the Docker container's file structure to execute on GPU instances.
    • D.Set the GPU flag in the Amazon SageMaker CreateTrainingJob request body.
    Show answer & explanationHide answer

    Correct answer: B

    Build the Docker container to be NVIDIA-Docker compatible.

    Explanation

    To use GPUs in SageMaker training with custom Docker containers, the container must be built to be compatible with NVIDIA-Docker to access the drivers on the host.

19. Question 19.A Machine Learning Specialist is building a Logistic Regression model that will predict whether or not a person will order a pizza. The Specialist is trying to build the optimal model with an ideal classification threshold. What model evaluation technique should the Specialist use to understand how different classification thresholds will impact the model's performance?

    • A.Receiver operating characteristic (ROC) curve.(correct answer)
    • B.Misclassification rate.
    • C.Root Mean Square Error (RMSE).
    • D.L1 norm.
    Show answer & explanationHide answer

    Correct answer: A

    Receiver operating characteristic (ROC) curve.

    Explanation

    The ROC curve plots True Positive Rate vs False Positive Rate across all possible thresholds, helping to select the optimal point.

20. Question 20.An interactive online dictionary wants to add a widget that displays words used in similar contexts. A Machine Learning Specialist is asked to provide word features for the downstream nearest neighbor model powering the widget. What should the Specialist do to meet these requirements?

    • A.Create one-hot word encoding vectors.
    • B.Produce a set of synonyms for every word using Amazon Mechanical Turk.
    • C.Create word embedding vectors that store edit distance with every other word.
    • D.Download word embeddings pre-trained on a large corpus.(correct answer)
    Show answer & explanationHide answer

    Correct answer: D

    Download word embeddings pre-trained on a large corpus.

    Explanation

    Pre-trained word embeddings (like Word2Vec or GloVe) capture semantic relationships between words based on large-scale context.

21. Question 21.A Machine Learning Specialist is configuring Amazon SageMaker so multiple Data Scientists can access notebooks, train models, and deploy endpoints. To ensure the best operational performance, the Specialist needs to be able to track how often the Scientists are deploying models, GPU and CPU utilization on the deployed SageMaker endpoints, and all errors that are generated when an endpoint is invoked. Which services are integrated with Amazon SageMaker to track this information? (Choose two.)

    • A.AWS CloudTrail.(correct answer)
    • B.AWS Health.
    • C.AWS Trusted Advisor.
    • D.Amazon CloudWatch.(correct answer)
    • E.AWS Config.
    Show answer & explanationHide answer

    Correct answer: A, D

    AWS CloudTrail. / Amazon CloudWatch.

    Explanation

    CloudTrail tracks API calls (like model deployments), while CloudWatch monitors performance metrics (CPU, GPU) and logs (errors).

22. Question 22.A retail chain has been ingesting purchasing records from its network of 20,000 stores to Amazon S3 using Amazon Kinesis Data Firehose. To support training an improved machine learning model, training records will require new but simple transformations, and some attributes will be combined. The model needs to be retrained daily. Given the large number of stores and the legacy data ingestion, which change will require the LEAST amount of development effort?

    • A.Require that the stores to switch to capturing their data locally on AWS Storage Gateway for loading into Amazon S3, then use AWS Glue to do the transformation.
    • B.Deploy an Amazon EMR cluster running Apache Spark with the transformation logic, and have the cluster run each day on the accumulating records in Amazon S3, outputting new/transformed records to Amazon S3.
    • C.Spin up a fleet of Amazon EC2 instances with the transformation logic, have them transform the data records accumulating on Amazon S3, and output the transformed records to Amazon S3.
    • D.Insert an Amazon Kinesis Data Analytics stream downstream of the Kinesis Data Firehose stream that transforms raw record attributes into simple transformed values using SQL.(correct answer)
    Show answer & explanationHide answer

    Correct answer: D

    Insert an Amazon Kinesis Data Analytics stream downstream of the Kinesis Data Firehose stream that transforms raw record attributes into simple transformed values using SQL.

    Explanation

    Inserting Kinesis Data Analytics between Firehose and S3 allows for streaming SQL transformations with minimal development effort compared to EMR or custom EC2 fleets.

23. Question 23.A Machine Learning Specialist is building a Convolutional Neural Network (CNN) that will classify 10 types of animals. The Specialist has built a series of layers in a neural network that will take an input image of an animal, pass it through a series of convolutional and pooling layers, and then finally pass it through a dense and fully connected layer with 10 nodes. The Specialist would like to get an output from the neural network that is a probability distribution of how likely it is that the input image belongs to each of the 10 classes. Which function will produce the desired output?

    • A.Dropout.
    • B.Smooth L1 loss.
    • C.Softmax.(correct answer)
    • D.Rectified linear units (ReLU).
    Show answer & explanationHide answer

    Correct answer: C

    Softmax.

    Explanation

    The Softmax function converts the output nodes of a multi-class classifier into a probability distribution that sums to 1.

24. Question 24.A Machine Learning Specialist trained a regression model, but the first iteration needs optimizing. The Specialist needs to understand whether the model is more frequently overestimating or underestimating the target. What option can the Specialist use to determine whether it is overestimating or underestimating the target value?

    • A.Root Mean Square Error (RMSE).
    • B.Residual plots.(correct answer)
    • C.Area under the curve.
    • D.Confusion matrix.
    Show answer & explanationHide answer

    Correct answer: B

    Residual plots.

    Explanation

    Residual plots show the difference between actual and predicted values. Patterns in the residuals (like consistently positive or negative) indicate systematic over or under-prediction.

25. Question 25.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? 

    • A.Decision tree.(correct answer)
    • B.Linear support vector machine (SVM).
    • C.Naive Bayesian classifier.
    • D.Single Perceptron with sigmoidal activation function.
    Show answer & explanationHide answer

    Correct answer: A

    Decision tree.

    Explanation

    For non-linear class distributions, tree-based models like Decision Trees or Random Forests are very effective at capturing the non-linear boundaries needed for high recall in complex fraud scenarios.

26. Question 26.A Machine Learning Specialist kicks off a hyperparameter tuning job for a tree-based ensemble model using Amazon SageMaker with Area Under the ROC Curve (AUC) as the objective metric. This workflow will eventually be deployed in a pipeline that retrains and tunes hyperparameters each night to model click-through on data that goes stale every 24 hours. With the goal of decreasing the amount of time it takes to train these models, and ultimately to decrease costs, the Specialist wants to reconfigure the input hyperparameter range(s). Which visualization will accomplish this?

    • A.A histogram showing whether the most important input feature is Gaussian.
    • B.A scatter plot with points colored by target variable that uses t-Distributed Stochastic Neighbor Embedding (t-SNE) to visualize the large number of input variables in an easier-to-read dimension.
    • C.A scatter plot showing the performance of the objective metric over each training iteration.
    • D.A scatter plot showing the correlation between maximum tree depth and the objective metric.(correct answer)
    Show answer & explanationHide answer

    Correct answer: D

    A scatter plot showing the correlation between maximum tree depth and the objective metric.

    Explanation

    A scatter plot showing the correlation between a hyperparameter (like tree depth) and the objective metric helps identify the most effective range for tuning.

27. Question 27.A Machine Learning Specialist is creating a new natural language processing application that processes a dataset comprised of 1 million sentences. The aim is to then run Word2Vec to generate embeddings of the sentences and enable different types of predictions. Here is an example from the dataset: 'The quck BROWN FOX jumps over the lazy dog.' Which of the following are the operations the Specialist needs to perform to correctly sanitize and prepare the data in a repeatable manner? (Choose three.)

    • A.Perform part-of-speech tagging and keep the action verb and the nouns only.
    • B.Normalize all words by making the sentence lowercase.(correct answer)
    • C.Remove stop words using an English stopword dictionary.(correct answer)
    • D.Correct the typography on 'quck' to 'quick'.
    • E.One-hot encode all words in the sentence.
    • F.Tokenize the sentence into words.(correct answer)
    Show answer & explanationHide answer

    Correct answer: B, C, F

    Normalize all words by making the sentence lowercase. / Remove stop words using an English stopword dictionary. / Tokenize the sentence into words.

    Explanation

    Common NLP preprocessing steps include normalization (lowercase), removing common stop words that don't add semantic value, and tokenizing text into individual units (words).

28. Question 28.A company is using Amazon Polly to translate plaintext documents to speech for automated company announcements. However, company acronyms are being mispronounced in the current documents. How should a Machine Learning Specialist address this issue for future documents?

    • A.Convert current documents to SSML with pronunciation tags.
    • B.Create an appropriate pronunciation lexicon.(correct answer)
    • C.Output speech marks to guide in pronunciation.
    • D.Use Amazon Lex to preprocess the text files for pronunciation.
    Show answer & explanationHide answer

    Correct answer: B

    Create an appropriate pronunciation lexicon.

    Explanation

    Pronunciation lexicons in Amazon Polly allow you to customize how specific words or acronyms are pronounced across all documents.

29. Question 29.An insurance company is developing a new device for vehicles that uses a camera to observe drivers' behavior and alert them when they appear distracted. The company created approximately 10,000 training images in a controlled environment that a Machine Learning Specialist will use to train and evaluate machine learning models. During the model evaluation, the Specialist notices that the training error rate diminishes faster as the number of epochs increases and the model is not accurately inferring on the unseen test images. Which of the following should be used to resolve this issue? (Choose two.)

    • A.Add vanishing gradient to the model.
    • B.Perform data augmentation on the training data.(correct answer)
    • C.Make the neural network architecture complex.
    • D.Use gradient checking in the model.
    • E.Add L2 regularization to the model.(correct answer)
    Show answer & explanationHide answer

    Correct answer: B, E

    Perform data augmentation on the training data. / Add L2 regularization to the model.

    Explanation

    Training error decreasing while test error stays high is a classic sign of overfitting. Data augmentation and L2 regularization are standard techniques to improve generalization.

30. Question 30.When submitting Amazon SageMaker training jobs using one of the built-in algorithms, which common parameters MUST be specified? (Choose three.)

    • A.The training channel identifying the location of training data on an Amazon S3 bucket.
    • B.The validation channel identifying the location of validation data on an Amazon S3 bucket.
    • C.The IAM role that Amazon SageMaker can assume to perform tasks on behalf of the users.(correct answer)
    • D.Hyperparameters in a JSON array as documented for the algorithm used.
    • E.The Amazon EC2 instance class specifying whether training will be run using CPU or GP(correct answer)
    • F.The output path specifying where on an Amazon S3 bucket the trained model will persist.(correct answer)
    Show answer & explanationHide answer

    Correct answer: C, E, F

    The IAM role that Amazon SageMaker can assume to perform tasks on behalf of the users. / The Amazon EC2 instance class specifying whether training will be run using CPU or GP / The output path specifying where on an Amazon S3 bucket the trained model will persist.

    Explanation

    When using SageMaker built-in algorithms, you must always specify the IAM execution role, the training instance type, and the S3 output path for the model artifacts.

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