AWS Certified Machine Learning - Specialty

• #41

  A data scientist needs to identify fraudulent user accounts for a company's ecommerce platform. The company wants the ability to determine if a newly created account is associated with a previously known fraudulent user. The data scientist is using AWS Glue to cleanse the company's application logs during ingestion. Which strategy will allow the data scientist to identify fraudulent accounts?

  • AExecute the built-in FindDuplicates Amazon Athena query.
  • BCreate a FindMatches machine learning transform in AWS Glue.
  • CCreate an AWS Glue crawler to infer duplicate accounts in the source data.
  • DSearch for duplicate accounts in the AWS Glue Data Catalog.
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• #42

  A Data Scientist needs to create a serverless ingestion and analytics solution for high-velocity, real-time streaming data. The ingestion process must buffer and convert incoming records from JSON to a query-optimized, columnar format without data loss. The output datastore must be highly available, and Analysts must be able to run SQL queries against the data and connect to existing business intelligence dashboards. Which solution should the Data Scientist build to satisfy the requirements?

  • ACreate a schema in the AWS Glue Data Catalog of the incoming data format. Use an Amazon Kinesis Data Firehose delivery stream to stream the data and transform the data to Apache Parquet or ORC format using the AWS GlueData Catalog before delivering to Amazon S3. Have the Analysts query the data directly from Amazon S3 using Amazon Athena, and connect to BI tools using the Athena Java Database Connectivity (JDBC) connector.
  • BWrite each JSON record to a staging location in Amazon S3. Use the S3 Put event to trigger an AWS Lambda function that transforms the data into Apache Parquet or ORC format and writes the data to a processed data location inAmazon S3. Have the Analysts query the data directly from Amazon S3 using Amazon Athena, and connect to BI tools using the Athena Java Database Connectivity (JDBC) connector.
  • CWrite each JSON record to a staging location in Amazon S3. Use the S3 Put event to trigger an AWS Lambda function that transforms the data into Apache Parquet or ORC format and inserts it into an Amazon RDS PostgreSQLdatabase. Have the Analysts query and run dashboards from the RDS database.
  • DUse Amazon Kinesis Data Analytics to ingest the streaming data and perform real-time SQL queries to convert the records to Apache Parquet before delivering to Amazon S3. Have the Analysts query the data directly from Amazon S3using Amazon Athena and connect to BI tools using the Athena Java Database Connectivity (JDBC) connector.
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• #43

  A company is building a predictive maintenance model based on machine learning (ML). The data is stored in a fully private Amazon S3 bucket that is encrypted at rest with AWS Key Management Service (AWS KMS) CMKs. An ML specialist must run data preprocessing by using an Amazon SageMaker Processing job that is triggered from code in an Amazon SageMaker notebook. The job should read data from Amazon S3, process it, and upload it back to the same S3 bucket. The preprocessing code is stored in a container image in Amazon Elastic Container Registry (Amazon ECR). The ML specialist needs to grant permissions to ensure a smooth data preprocessing workflow. Which set of actions should the ML specialist take to meet these requirements?

  • ACreate an IAM role that has permissions to create Amazon SageMaker Processing jobs, S3 read and write access to the relevant S3 bucket, and appropriate KMS and ECR permissions. Attach the role to the SageMaker notebook instance. Create an Amazon SageMaker Processing job from the notebook.
  • BCreate an IAM role that has permissions to create Amazon SageMaker Processing jobs. Attach the role to the SageMaker notebook instance. Create an Amazon SageMaker Processing job with an IAM role that has read and write permissions to the relevant S3 bucket, and appropriate KMS and ECR permissions.
  • CCreate an IAM role that has permissions to create Amazon SageMaker Processing jobs and to access Amazon ECR. Attach the role to the SageMaker notebook instance. Set up both an S3 endpoint and a KMS endpoint in the default VPC. Create Amazon SageMaker Processing jobs from the notebook.
  • DCreate an IAM role that has permissions to create Amazon SageMaker Processing jobs. Attach the role to the SageMaker notebook instance. Set up an S3 endpoint in the default VPC. Create Amazon SageMaker Processing jobs with the access key and secret key of the IAM user with appropriate KMS and ECR permissions.
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• #44

  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?

  • AConvert current documents to SSML with pronunciation tags.
  • BCreate an appropriate pronunciation lexicon.
  • COutput speech marks to guide in pronunciation.
  • DUse Amazon Lex to preprocess the text files for pronunciation.
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• #45

  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?

  • ADropout.
  • BSmooth L1 loss.
  • CSoftmax.
  • DRectified linear units (ReLU).
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• #46

  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?

  • AAnXGBoost model where the objective parameter is set to multi:softmax.
  • BA deep Convolutional Neural Network (CNN) with a softmax activation function for the last layer.
  • CA regression forest where the number of trees is set equal to the number of product categories.
  • DA DeepAR forecasting model based on a Recurrent Neural Network (RNN).
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• #473 चुनें

  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.)

  • APerform part-of-speech tagging and keep the action verb and the nouns only.
  • BNormalize all words by making the sentence lowercase.
  • CRemove stop words using an English stopword dictionary.
  • DCorrect the typography on 'quck' to 'quick'.
  • EOne-hot encode all words in the sentence.
  • FTokenize the sentence into words.
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• #48

  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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• #49

  This graph shows the training and validation loss against the epochs for a neural network. The network being trained is as follows: Two dense layers, one output neuron. 100 neurons in each layer. 100 epochs. Random initialization of weights. Which technique can be used to improve model performance in terms of accuracy in the validation set?

  • AEarly stopping.
  • BRandom initialization of weights with appropriate seed.
  • CIncreasing the number of epochs.
  • DAdding another layer with the 100 neurons.
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• #50

  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?

  • AListwise deletion.
  • BLast observation carried forward.
  • CMultiple imputation.
  • DMean substitution.
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• #51

  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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• #52

  A Machine Learning Specialist is assigned a TensorFlow project using Amazon SageMaker for training, and needs to continue working for an extended period with no Wi-Fi access. Which approach should the Specialist use to continue working?

  • AInstall Python 3 and boto3 on their laptop and continue the code development using that environment.
  • BDownload the TensorFlow Docker container used in Amazon SageMaker from GitHub to their local environment, and use the Amazon SageMaker Python SDK to test the code.
  • CDownload TensorFlow from tensorflow.org to emulate the TensorFlow kernel in the SageMaker environment.
  • DDownload the SageMaker notebook to their local environment, then install Jupyter Notebooks on their laptop and continue the development in a local notebook.
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• #53

  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?

  • ALinear Regression.
  • BClassification.
  • CClustering.
  • DReinforcement learning.
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• #54

  A data scientist must build a custom recommendation model in Amazon SageMaker for an online retail company. Due to the nature of the company's products, customers buy only 4-5 products every 5-10 years. So, the company relies on a steady stream of new customers. When a new customer signs up, the company collects data on the customer's preferences. Below is a sample of the data available to the data scientist. How should the data scientist split the dataset into a training and test set for this use case?

  • AShuffle all interaction data. Split off the last 10% of the interaction data for the test set.
  • BIdentify the most recent 10% of interactions for each user. Split off these interactions for the test set.
  • CIdentify the 10% of users with the least interaction data. Split off all interaction data from these users for the test set.
  • DRandomly select 10% of the users. Split off all interaction data from these users for the test set.
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• #55

  A Data Scientist is training a multilayer perception (MLP) on a dataset with multiple classes. The target class of interest is unique compared to the other classes within the dataset, but it does not achieve and acceptable recall metric. The Data Scientist has already tried varying the number and size of the MLP's hidden layers, which has not significantly improved the results. A solution to improve recall must be implemented as quickly as possible. Which techniques should be used to meet these requirements?

  • AGather more data using Amazon Mechanical Turk and then retrain.
  • BTrain an anomaly detection model instead of an MLP.
  • CTrain an XGBoost model instead of an MLP.
  • DAdd class weights to the MLP's loss function and then retrain.
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• #56

  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?

  • ABundle the NVIDIA drivers with the Docker image.
  • BBuild the Docker container to be NVIDIA-Docker compatible.
  • COrganize the Docker container's file structure to execute on GPU instances.
  • DSet the GPU flag in the Amazon SageMaker CreateTrainingJob request body.
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• #57

  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?

  • ABuild a content-based filtering recommendation engine with Apache Spark ML on Amazon EMR.
  • BBuild a collaborative filtering recommendation engine with Apache Spark ML on Amazon EMR.
  • CBuild a model-based filtering recommendation engine with Apache Spark ML on Amazon EMR.
  • DBuild a combinative filtering recommendation engine with Apache Spark ML on Amazon EMR.
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• #58

  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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• #59

  A media company with a very large archive of unlabeled images, text, audio, and video footage wishes to index its assets to allow rapid identification of relevant content by the Research team. The company wants to use machine learning to accelerate the efforts of its in-house researchers who have limited machine learning expertise. Which is the FASTEST route to index the assets?

  • AUse Amazon Rekognition, Amazon Comprehend, and Amazon Transcribe to tag data into distinct categories/classes.
  • BCreate a set of Amazon Mechanical Turk Human Intelligence Tasks to label all footage.
  • CUse Amazon Transcribe to convert speech to text. Use the Amazon SageMaker Neural Topic Model (NTM) and Object Detection algorithms to tag data into distinct categories/classes.
  • DUse the AWS Deep Learning AMI and Amazon EC2 GPU instances to create custom models for audio transcription and topic modeling, and use object detection to tag data into distinct categories/classes.
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• #60

  A large consumer goods manufacturer has the following products on sale: 34 different toothpaste variants. 48 different toothbrush variants. 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?

  • ATrain a custom ARIMA model to forecast demand for the new product.
  • BTrain an Amazon SageMaker DeepAR algorithm to forecast demand for the new product.
  • CTrain an Amazon SageMaker K-means clustering algorithm to forecast demand for the new product.
  • DTrain a custom XGBoost model to forecast demand for the new product.
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अपना स्कोर ट्रैक करना, समयबद्ध मॉक परीक्षा देना और AI व्याख्या पाना चाहते हैं? निःशुल्क खाता बनाएँ