0% 0 votes, 0 avg 16 123456789101112131415161718192021222324252627282930 This quiz randomly generates 30 questions as asked in AWS Certified Machine Learning - Specialty (MLS-C01) Congratulations! AWS Certified Machine Learning - Specialty (MLS-C01) This quiz randomly generates 30 questions (in 60 mins) as asked in AWS Certified Machine Learning - Specialty (MLS-C01). The real MLS-C01 test has 65 questions and a total time of 180 minutes. Of these, 15 questions are underlined, and only 50 questions are scored. This test randomly generates 30 questions from our question bank. For best results, practice multiple times until you achieve 100% accuracy. 1 / 30 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 histogram showing whether the most important input feature is Gaussian. 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. A scatter plot showing the performance of the objective metric over each training iteration. A scatter plot showing the correlation between maximum tree depth and the objective metric. 2 / 30 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? Root Mean Square Error (RMSE) Residual plots Area under the curve Confusion matrix 3 / 30 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? 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. 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 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. 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. 4 / 30 A Machine Learning Specialist deployed a model that provides product recommendations on a company's website. Initially, the model was performing very well and resulted in customers buying more products on average. However, within the past few months, the Specialist has noticed that the effect of product recommendations has diminished and customers are starting to return to their original habits of spending less. The Specialist is unsure of what happened, as the model has not changed from its initial deployment over a year ago. Which method should the Specialist try to improve model performance? The model needs to be completely re-engineered because it is unable to handle product inventory changes. The model's hyperparameters should be periodically updated to prevent drift. The model should be periodically retrained from scratch using the original data while adding a regularization term to handle product inventory changes The model should be periodically retrained using the original training data plus new data as product inventory changes. 5 / 30 An office security agency conducted a successful pilot using 100 cameras installed at key locations within the main office. Images from the cameras were uploaded to Amazon S3 and tagged using Amazon Rekognition, and the results were stored in Amazon ES. The agency is now looking to expand the pilot into a full production system using thousands of video cameras in its office locations globally. The goal is to identify activities performed by non-employees in real time Which solution should the agency consider? Use a proxy server at each local office and for each camera, and stream the RTSP feed to a unique Amazon Kinesis Video Streams video stream. On each stream, use Amazon Rekognition Video and create a stream processor to detect faces from a collection of known employees, and alert when non- employees are detected. Use a proxy server at each local office and for each camera, and stream the RTSP feed to a unique Amazon Kinesis Video Streams video stream. On each stream, use Amazon Rekognition Image to detect faces from a collection of known employees and alert when non-employees are detected. Install AWS DeepLens cameras and use the DeepLens_Kinesis_Video module to stream video to Amazon Kinesis Video Streams for each camera. On each stream, use Amazon Rekognition Video and create a stream processor to detect faces from a collection on each stream, and alert when non- employees are detected. Install AWS DeepLens cameras and use the DeepLens_Kinesis_Video module to stream video to Amazon Kinesis Video Streams for each camera. On each stream, run an AWS Lambda function to capture image fragments and then call Amazon Rekognition Image to detect faces from a collection of known employees, and alert when non-employees are detected. 6 / 30 An agency collects census information within a country to determine healthcare and social program needs by province and city. The census form collects responses for approximately 500 questions from each citizen. Which combination of algorithms would provide the appropriate insights? (Select TWO.) The factorization machines (FM) algorithm The Latent Dirichlet Allocation (LDA) algorithm The principal component analysis (PCA) algorithm The k-means algorithm The Random Cut Forest (RCF) algorithm The PCA and K-means algorithms are useful in collection of data using census form. 7 / 30 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.) AWS CloudTrail AWS Health AWS Trusted Advisor Amazon CloudWatch AWS Config 8 / 30 A Machine Learning Specialist has created a deep learning neural network model that performs well on the training data but performs poorly on the test data. Which of the following methods should the Specialist consider using to correct this? (Choose three.) Decrease regularization. Increase regularization. Increase dropout. Decrease dropout. Increase feature combinations. Decrease feature combinations. 9 / 30 A Data Scientist is working on an application that performs sentiment analysis. The validation accuracy is poor, and the Data Scientist thinks that the cause may be a rich vocabulary and a low average frequency of words in the dataset. Which tool should be used to improve the validation accuracy? Amazon Comprehend syntax analysis and entity detection Amazon SageMaker BlazingText cbow mode Natural Language Toolkit (NLTK) stemming and stop word removal Scikit-leam term frequency-inverse document frequency (TF-IDF) vectorizer 10 / 30 A Machine Learning team uses Amazon SageMaker to train an Apache MXNet handwritten digit classifier model using a research dataset. The team wants to receive a notification when the model is overfitting. Auditors want to view the Amazon SageMaker log activity report to ensure there are no unauthorized API calls. What should the Machine Learning team do to address the requirements with the least amount of code and fewest steps? Implement an AWS Lambda function to log Amazon SageMaker API calls to Amazon S3. Add code to push a custom metric to Amazon CloudWatch. Create an alarm in CloudWatch with Amazon SNS to receive a notification when the model is overfitting. Use AWS CloudTrail to log Amazon SageMaker API calls to Amazon S3. Add code to push a custom metric to Amazon CloudWatch. Create an alarm in CloudWatch with Amazon SNS to receive a notification when the model is overfitting. Implement an AWS Lambda function to log Amazon SageMaker API calls to AWS CloudTrail. Add code to push a custom metric to Amazon CloudWatch. Create an alarm in CloudWatch with Amazon SNS to receive a notification when the model is overfitting. Use AWS CloudTrail to log Amazon SageMaker API calls to Amazon S3. Set up Amazon SNS to receive a notification when the model is overfitting 11 / 30 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? 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. 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. 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. Send Amazon CloudWatch Logs that were generated by Amazon SageMaker to Amazon ES and use Kibana to query and visualize the log data 12 / 30 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? Create one-hot word encoding vectors. Produce a set of synonyms for every word using Amazon Mechanical Turk. Create word embedding vectors that store edit distance with every other word. Download word embeddings pre-trained on a large corpus. 13 / 30 An online reseller has a large, multi-column dataset with one column missing 30% of its data. A Machine Learning Specialist believes that certain columns in the dataset could be used to reconstruct the missing data. Which reconstruction approach should the Specialist use to preserve the integrity of the dataset? Listwise deletion Last observation carried forward Multiple imputation Mean substitution 14 / 30 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? The class distribution in the dataset is imbalanced. Dataset shuffling is disabled. The batch size is too big. The learning rate is very high. 15 / 30 A company is setting up an Amazon SageMaker environment. The corporate data security policy does not allow communication over the internet. How can the company enable the Amazon SageMaker service without enabling direct internet access to Amazon SageMaker notebook instances? Create a NAT gateway within the corporate VPC. Route Amazon SageMaker traffic through an on-premises network. Create Amazon SageMaker VPC interface endpoints within the corporate VPC. Create VPC peering with Amazon VPC hosting Amazon SageMaker. 16 / 30 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.) Perform part-of-speech tagging and keep the action verb and the nouns only. Normalize all words by making the sentence lowercase. Remove stop words using an English stopword dictionary. Correct the typography on "quck" to "quick." One-hot encode all words in the sentence. Tokenize the sentence into words. 17 / 30 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? Latent Dirichlet Allocation (LDA) for the given collection of discrete data to identify patterns in the customer database. A neural network with a minimum of three layers and random initial weights to identify patterns in the customer database. Collaborative filtering based on user interactions and correlations to identify patterns in the customer database. Random Cut Forest (RCF) over random subsamples to identify patterns in the customer database. 18 / 30 A Data Science team is designing a dataset repository where it will store a large amount of training data commonly used in its machine learning models. As Data Scientists may create an arbitrary number of new datasets every day, the solution has to scale automatically and be cost-effective. Also, it must be possible to explore the data using SQL. Which storage scheme is MOST adapted to this scenario? Store datasets as files in Amazon S3. Store datasets as files in an Amazon EBS volume attached to an Amazon EC2 instance. Store datasets as tables in a multi-node Amazon Redshift cluster. Store datasets as global tables in Amazon DynamoDB. 19 / 30 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? Linear regression Classification Clustering Reinforcement learning The goal of classification is to determine to which class or category a data point (customer in our case) belongs to. For classification problems, data scientists would use historical data with predefined target variables AKA labels (churner/non- churner) ?answers that need to be predicted ?to train an algorithm. With classification, businesses can answer the following questions: Will this customer churn or not? Will a customer renew their subscription? Will a user downgrade a pricing plan? Are there any signs of unusual customer behavior? 20 / 30 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? Receiver operating characteristic (ROC) curve Misclassification rate Root Mean Square Error (RMSE) L1 norm 21 / 30 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? Build a content-based filtering recommendation engine with Apache Spark ML on Amazon EMR Build a collaborative filtering recommendation engine with Apache Spark ML on Amazon EMR Build a model-based filtering recommendation engine with Apache Spark ML on Amazon EMR Build a combinative filtering recommendation engine with Apache Spark ML on Amazon EMR Many developers want to implement the famous Amazon model that was used to power the "People who bought this also bought these items" feature on Amazon.com. This model is based on a method called Collaborative Filtering. It takes items such as movies, books, and products that were rated highly by a set of users and recommending them to other users who also gave them high ratings. This method works well in domains where explicit ratings or implicit user actions can be gathered and analyzed. 22 / 30 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? 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. 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. 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. 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. 23 / 30 A manufacturing company has a large set of labeled historical sales data. The manufacturer would like to predict how many units of a particular part should be produced each quarter. Which machine learning approach should be used to solve this problem? Logistic regression Random Cut Forest (RCF) Principal component analysis (PCA) Linear regression 24 / 30 A retail company intends to use machine learning to categorize new products. A labeled dataset of current products was provided to the Data Science team. The dataset includes 1,200 products. The labeled dataset has 15 features for each product such as title dimensions, weight, and price. Each product is labeled as belonging to one of six categories such as books, games, electronics, and movies. Which model should be used for categorizing new products using the provided dataset for training? AnXGBoost model where the objective parameter is set to multi:softmax A deep convolutional neural network (CNN) with a softmax activation function for the last layer A regression forest where the number of trees is set equal to the number of product categories A DeepAR forecasting model based on a recurrent neural network (RNN) 25 / 30 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? The model is 86% accurate and the cost incurred by the company as a result of false negatives is less than the false positives. The precision of the model is 86%, which is less than the accuracy of the model. The model is 86% accurate and the cost incurred by the company as a result of false positives is less than the false negatives. The precision of the model is 86%, which is greater than the accuracy of the model. 26 / 30 Machine Learning Specialist is working with a media company to perform classification on popular articles from the company's website. The company is using random forests to classify how popular an article will be before it is published. A sample of the data being used is below. Given the dataset, the Specialist wants to convert the Day_Of_Week column to binary values. What technique should be used to convert this column to binary values? Binarization One-hot encoding Tokenization Normalization transformation 27 / 30 A large consumer goods manufacturer has the following products on sale: 1. 34 different toothpaste variants 2. 48 different toothbrush variants 3. 43 different mouthwash variants The entire sales history of all these products is available in Amazon S3. Currently, the company is using custom-built autoregressive integrated moving average (ARIMA) models to forecast demand for these products. The company wants to predict the demand for a new product that will soon be launched. Which solution should a Machine Learning Specialist apply? Train a custom ARIMA model to forecast demand for the new product. Train an Amazon SageMaker DeepAR algorithm to forecast demand for the new product. Train an Amazon SageMaker k-means clustering algorithm to forecast demand for the new product. Train a custom XGBoost model to forecast demand for the new product. The Amazon SageMaker DeepAR forecasting algorithm is a supervised learning algorithm for forecasting scalar (one-dimensional) time series using recurrent neural networks (RNN). Classical forecasting methods, such as autoregressive integrated moving average (ARIMA) or exponential smoothing (ETS), fit a single model to each individual time series. They then use that model to extrapolate the time series into the future. 28 / 30 A Data Scientist is developing a machine learning model to predict future patient outcomes based on information collected about each patient and their treatment plans. The model should output a continuous value as its prediction. The data available includes labeled outcomes for a set of 4,000 patients. The study was conducted on a group of individuals over the age of 65 who have a particular disease that is known to worsen with age. Initial models have performed poorly. While reviewing the underlying data, the Data Scientist notices that, out of 4,000 patient observations, there are 450 where the patient age has been input as 0. The other features for these observations appear normal compared to the rest of the sample population. How should the Data Scientist correct this issue? Drop all records from the dataset where age has been set to 0. Replace the age field value for records with a value of 0 with the mean or median value from the dataset Drop the age feature from the dataset and train the model using the rest of the features. Use k-means clustering to handle missing features 29 / 30 A Machine Learning Specialist is preparing data for training on Amazon SageMaker. The Specialist is using one of the SageMaker built-in algorithms for the training. The dataset is stored in .CSV format and is transformed into a numpy.array, which appears to be negatively affecting the speed of the training. What should the Specialist do to optimize the data for training on SageMaker? Use the SageMaker batch transform feature to transform the training data into a DataFrame. Use AWS Glue to compress the data into the Apache Parquet format. Transform the dataset into the RecordIO protobuf format. Use the SageMaker hyperparameter optimization feature to automatically optimize the data. 30 / 30 A company's Machine Learning Specialist needs to improve the training speed of a time-series forecasting model using TensorFlow. The training is currently implemented on a single-GPU machine and takes approximately 23 hours to complete. The training needs to be run daily. The model accuracy is acceptable, but the company anticipates a continuous increase in the size of the training data and a need to update the model on an hourly, rather than a daily, basis. The company also wants to minimize coding effort and infrastructure changes. What should the Machine Learning Specialist do to the training solution to allow it to scale for future demand? Do not change the TensorFlow code. Change the machine to one with a more powerful GPU to speed up the training. Change the TensorFlow code to implement a Horovod distributed framework supported by Amazon SageMaker. Parallelize the training to as many machines as needed to achieve the business goals. Switch to using a built-in AWS SageMaker DeepAR model. Parallelize the training to as many machines as needed to achieve the business goals. Move the training to Amazon EMR and distribute the workload to as many machines as needed to achieve the business goals. Your score is 0% Restart quiz
