Quiz IT certification exam

Question 1

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?

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Accepted Answer

A convolutional neural network (CNN) and ResNet

Answer

Amazon SageMaker DeepAR forecasting algorithm

Answer

Amazon SageMaker XGBoost algorithm

Answer

Amazon SageMaker Latent Dirichlet Allocation (LDA) algorithm

Question 2

A manufacturing company uses machine learning (ML) models to detect quality issues. The models use images that are taken of the company's product at the end of each production step. The company has thousands of machines at the production site that generate one image per second on average.The company ran a successful pilot with a single manufacturing machine. For the pilot, ML specialists used an industrial PC that ran AWS IoT Greengrass with a long-running AWS Lambda function that uploaded the images to Amazon S3. The uploaded images invoked a Lambda function that was written in Python to perform inference by using an Amazon SageMaker endpoint that ran a custom model. The inference results were forwarded back to a web service that was hosted at the production site to prevent faulty products from being shipped.The company scaled the solution out to all manufacturing machines by installing similarly configured industrial PCs on each production machine. However, latency for predictions increased beyond acceptable limits. Analysis shows that the internet connection is at its capacity limit.How can the company resolve this issue MOST cost-effectively?

Accepted Answer

Deploy the Lambda function and the ML models onto the AWS IoT Greengrass core that is running on the industrial PCs that are installed on each machine. Extend the long-running Lambda function that runs on AWS IoT Greengrass to invoke the Lambda function with the captured images and run the inference on the edge component that forwards the results directly to the web service.

Answer

Set up a 10 Gbps AWS Direct Connect connection between the production site and the nearest AWS Region. Use the Direct Connect connection to upload the images. Increase the size of the instances and the number of instances that are used by the SageMaker endpoint.

Answer

Extend the long-running Lambda function that runs on AWS IoT Greengrass to compress the images and upload the compressed files to Amazon S3. Decompress the files by using a separate Lambda function that invokes the existing Lambda function to run the inference pipeline.

Answer

Use auto scaling for SageMaker. Set up an AWS Direct Connect connection between the production site and the nearest AWS Region. Use the Direct Connect connection to upload the images.

Question 3

A company distributes an online multiple-choice survey to several thousand people. Respondents to the survey can select multiple options for each question.A machine learning (ML) engineer needs to comprehensively represent every response from all respondents in a dataset. The ML engineer will use the dataset to train a logistic regression model.Which solution will meet these requirements?

Accepted Answer

Perform one-hot encoding on every possible option for each question of the survey.

Answer

Perform binning on all the answers each respondent selected for each question.

Answer

Use Amazon Mechanical Turk to create categorical labels for each set of possible responses.

Answer

Use Amazon Textract to create numeric features for each set of possible responses.

Question 4

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? (Select THREE.)

Accepted Answer

Decrease the amount of regularization that the model uses.

Accepted Answer

Increase the number of training examples that that model uses.

Accepted Answer

Increase the number of model features that the model uses.

Answer

Increase the amount of regularization that the model uses.

Answer

Increase the number of test examples that the model uses.

Answer

Decrease the number of model features that the model uses.

Question 5

A manufacturing company stores production volume data in a PostgreSQL database.The company needs an end-to-end solution that will give business analysts the ability to prepare data for processing and to predict future production volume based the previous year's production volume. The solution must not require the company to have coding knowledge.Which solution will meet these requirements with the LEAST effort?

Accepted Answer

Use AWS Glue DataBrew to read the data that is in the PostgreSQL database and to perform the data preparation. Use Amazon SageMaker Canvas for the prediction modeling.

Answer

Use AWS Database Migration Service (AWS DMS) to transfer the data from the PostgreSQL database to an Amazon S3 bucket. Create an Amazon EMR cluster to read the S3 bucket and perform the data preparation. Use Amazon SageMaker Studio for the prediction modeling.

Answer

Use AWS Database Migration Service (AWS DMS) to transfer the data from the PostgreSQL database to an Amazon S3 bucket. Use AWS Glue to read the data in the S3 bucket and to perform the data preparation. Use Amazon SageMaker Canvas for the prediction modeling.

Answer

Use AWS Glue DataBrew to read the data that is in the PostgreSQL database and to perform the data preparation. Use Amazon SageMaker Studio for the prediction modeling.

Question 6

A music streaming company is building a pipeline to extract features. The company wants to store the features for offline model training and online inference. The company wants to track feature history and to give the company's data science teams access to the features.Which solution will meet these requirements with the MOST operational efficiency?

Accepted Answer

Use Amazon SageMaker Feature Store to store features for model training and inference. Create an online store for online inference. Create an offline store for model training. Create an 1AM role for data scientists to access and search through feature groups.

Answer

Use Amazon SageMaker Feature Store to store features for model training and inference. Create an online store for both online inference and model training. Create an 1AM role for data scientists to access and search through feature groups.

Answer

Create one Amazon S3 bucket to store online inference features. Create a second S3 bucket to store offline model training features. Turn on

Answer

Create two separate Amazon DynamoDB tables to store online inference features and offline model training features. Use time-based versioning on both tables. Query the DynamoDB table for online inference. Move the data from DynamoDB to Amazon S3 when a new SageMaker training job is launched. Create an 1AM policy that allows data scientists to access both tables.

Question 7

A company is building a predictive maintenance model for its warehouse equipment. The model must predict the probability of failure of all machines in the warehouse. The company has collected 10.000 event samples within 3 months. The event samples include 100 failure cases that are evenly distributed across 50 different machine types.How should the company prepare the data for the model to improve the model's accuracy?

Accepted Answer

Oversample the failure cases by using the Synthetic Minority Oversampling Technique (SMOTE).

Answer

Adjust the class weight to account for each machine type.

Answer

Undersample the non-failure events. Stratify the non-failure events by machine type.

Answer

Undersample the non-failure events by using the Synthetic Minority Oversampling Technique (SMOTE).

Question 8

An ecommerce company has observed that customers who use the company's website rarely view items that the website recommends to customers. The company wants to recommend items to customers that customers are more likely to want to purchase.Which solution will meet this requirement in the SHORTEST amount of time?

Accepted Answer

Integrate Amazon Personalize into the company's website to provide customers with personalized recommendations.

Answer

Host the company's website on Amazon EC2 Accelerated Computing instances to increase the website response speed.

Answer

Host the company's website on Amazon EC2 GPU-based instances to increase the speed of the website's search tool.

Answer

Use Amazon SageMaker to train a Neural Collaborative Filtering (NCF) model to make product recommendations.

Question 9

A tourism company uses a machine learning (ML) model to make recommendations to customers. The company uses an Amazon SageMaker environment and set hyperparameter tuning completion criteria to MaxNumberOfTrainingJobs.An ML specialist wants to change the hyperparameter tuning completion criteria. The ML specialist wants to stop tuning immediately after an internal algorithm determines that tuning job is unlikely to improve more than 1% over the objective metric from the best training job.Which completion criteria will meet this requirement?

Accepted Answer

CompleteOnConvergence

Answer

MaxRuntimelnSeconds

Answer

TargetObjectiveMetricValue

Answer

MaxNumberOfTrainingJobsNotlmproving

Question 10

A machine learning (ML) specialist uploads a dataset to an Amazon S3 bucket that is protected by server-side encryption with AWS KMS keys (SSE-KMS). The ML specialist needs to ensure that an Amazon SageMaker notebook instance can read the dataset that is in Amazon S3.Which solution will meet these requirements?

Accepted Answer

Assign an IAM role that provides S3 read access for the dataset to the SageMaker notebook. Grant permission in the KMS key policy to the 1AM role.

Answer

Define security groups to allow all HTTP inbound and outbound traffic. Assign the security groups to the SageMaker notebook instance.

Answer

Configure the SageMaker notebook instance to have access to the VPC. Grant permission in the AWS Key Management Service (AWS KMS) key policy to the notebook's VPC.

Answer

Assign the same KMS key that encrypts the data in Amazon S3 to the SageMaker notebook instance.

Question 11

A data scientist is designing a repository that will contain many images of vehicles. The repository must scale automatically in size to store new images every day. The repository must support versioning of the images. The data scientist must implement a solution that maintains multiple immediately accessible copies of the data in different AWS Regions.Which solution will meet these requirements?

Accepted Answer

Amazon S3 with S3 Cross-Region Replication (CRR)

Answer

Amazon Elastic Block Store (Amazon EBS) with snapshots that are shared in a secondary Region

Answer

Amazon Elastic File System (Amazon EFS) Standard storage that is configured with Regional availability

Answer

AWS Storage Gateway Volume Gateway

Question 12

An insurance company is creating an application to automate car insurance claims. A machine learning (ML) specialist used an Amazon SageMaker Object Detection - TensorFlow built-in algorithm to train a model to detect scratches and dents in images of cars. After the model was trained, the ML specialist noticed that the model performed better on the training dataset than on the testing dataset.Which approach should the ML specialist use to improve the performance of the model on the testing data?

Accepted Answer

Increase the value of the L2 hyperparameter.

Answer

Increase the value of the momentum hyperparameter.

Answer

Reduce the value of the dropout_rate hyperparameter.

Answer

Reduce the value of the learning_rate hyperparameter.

Question 13

A machine learning (ML) specialist is training a linear regression model. The specialist notices that the model is overfitting. The specialist applies an L1 regularization parameter and runs the model again. This change results in all features having zero weights.What should the ML specialist do to improve the model results?

Accepted Answer

Decrease the L1 regularization parameter. Do not change any other training parameters.

Answer

Increase the L1 regularization parameter. Do not change any other training parameters.

Answer

Introduce a large L2 regularization parameter. Do not change the current L1 regularization value.

Answer

Introduce a small L2 regularization parameter. Do not change the current L1 regularization value.

Question 14

A machine learning (ML) specialist at a retail company must build a system to forecast the daily sales for one of the company's stores. The company provided the ML specialist with sales data for this store from the past 10 years. The historical dataset includes the total amount of sales on each day for the store. Approximately 10% of the days in the historical dataset are missing sales data.The ML specialist builds a forecasting model based on the historical dataset. The specialist discovers that the model does not meet the performance standards that the company requires.Which action will MOST likely improve the performance for the forecasting model?

Accepted Answer

Replace missing values in the dataset by using linear interpolation.

Answer

Aggregate sales from stores in the same geographic area.

Answer

Apply smoothing to correct for seasonal variation.

Answer

Change the forecast frequency from daily to weekly.

Question 15

A company plans to build a custom natural language processing (NLP) model to classify and prioritize user feedback. The company hosts the data and all machine learning (ML) infrastructure in the AWS Cloud. The ML team works from the company's office, which has an IPsec VPN connection to one VPC in the AWS Cloud.The company has set both the enableDnsHostnames attribute and the enableDnsSupport attribute of the VPC to true. The company's DNS resolvers point to the VPC DNS. The company does not allow the ML team to access Amazon SageMaker notebooks through connections that use the public internet. The connection must stay within a private network and within the AWS internal network.Which solution will meet these requirements with the LEAST development effort?

Accepted Answer

Create a VPC interface endpoint for the SageMaker notebook in the VPC. Access the notebook through a VPN connection and the VPC endpoint.

Answer

Create a bastion host by using Amazon EC2 in a public subnet within the VPC. Log in to the bastion host through a VPN connection. Access the SageMaker notebook from the bastion host.

Answer

Create a bastion host by using Amazon EC2 in a private subnet within the VPC with a NAT gateway. Log in to the bastion host through a VPN connection. Access the SageMaker notebook from the bastion host.

Answer

Create a NAT gateway in the VPC. Access the SageMaker notebook HTTPS endpoint through a VPN connection and the NAT gateway.

Question 16

A bank has collected customer data for 10 years in CSV format. The bank stores the data in an on-premises server. A data science team wants to use Amazon SageMaker to build and train a machine learning (ML) model to predict churn probability. The team will use the historical data. The data scientists want to perform data transformations quickly and to generate data insights before the team builds a model for production.Which solution will meet these requirements with the LEAST development effort?

Accepted Answer

Upload the data into an Amazon S3 bucket. Allow SageMaker to access the data that is in the bucket. Import the data from the S3 bucket into SageMaker Data Wrangler. Perform data transformations and generate insights within Data Wrangler.

Answer

Upload the data into the SageMaker Data Wrangler console directly. Perform data transformations and generate insights within Data Wrangler.

Answer

Upload the data into the SageMaker Data Wrangler console directly. Allow SageMaker and Amazon QuickSight to access the data that is in an Amazon S3 bucket. Perform data transformations in Data Wrangler and save the transformed data into a second S3 bucket. Use QuickSight to generate data insights.

Answer

Upload the data into an Amazon S3 bucket. Allow SageMaker to access the data that is in the bucket. Import the data from the bucket into SageMaker Data Wrangler. Perform data transformations in Data Wrangler. Save the data into a second S3 bucket. Use a SageMaker Studio notebook to generate data insights.

Question 17

A machine learning (ML) engineer is preparing a dataset for a classification model. The ML engineer notices that some continuous numeric features have a significantly greater value than most other features. A business expert explains that the features are independently informative and that the dataset is representative of the target distribution.After training, the model's inferences accuracy is lower than expected.Which preprocessing technique will result in the GREATEST increase of the model's inference accuracy?

Accepted Answer

Normalize the problematic features.

Answer

Bootstrap the problematic features.

Answer

Remove the problematic features.

Answer

Extrapolate synthetic features.

Question 18

A data scientist needs to create a model for predictive maintenance. The model will be based on historical data to identify rare anomalies in the data.The historical data is stored in an Amazon S3 bucket. The data scientist needs to use Amazon SageMaker Data Wrangler to ingest the data. The data scientists also needs to perform exploratory data analysis (EDA) to understand the statistical properties of the data.Which solution will meet these requirements with the LEAST amount of compute resources?

Accepted Answer

Import the data by using the First K option. Infer the value of K from domain knowledge.

Answer

Import the data by using the None option.

Answer

Import the data by using the Stratified option.

Answer

Import the data by using the Randomized option. Infer the random size from domain knowledge.

Question 19

A company wants to use machine learning (ML) to improve its customer churn prediction model. The company stores data in an Amazon Redshift data warehouse.A data science team wants to use Amazon Redshift machine learning (Amazon Redshift ML) to build a model and run predictions for new data directly within the data warehouse.Which combination of steps should the company take to use Amazon Redshift ML to meet these requirements? (Select THREE.)

Accepted Answer

Define the feature variables and target variable for the churn prediction model.

Accepted Answer

Write a CREATE MODEL SQL statement to create a model.

Accepted Answer

Use the SQL prediction function to run predictions,

Answer

Use the SQL EXPLAIN_MODEL function to run predictions.

Answer

Use Amazon Redshift Spectrum to train the model.

Answer

Manually export the training data to Amazon S3.

Question 20

A business to business (B2B) ecommerce company wants to develop a fair and equitable risk mitigation strategy to reject potentially fraudulent transactions. The company wants to reject fraudulent transactions despite the possibility of losing some profitable transactions or customers.Which solution will meet these requirements with the LEAST operational effort?

Accepted Answer

Use the Amazon Fraud Detector prediction API to approve or deny any activities that Fraud Detector identifies as fraudulent.

Answer

Use Amazon SageMaker to approve transactions only for products the company has sold in the past.

Answer

Use Amazon SageMaker to train a custom fraud detection model based on customer data.

Answer

Use the Amazon Fraud Detector prediction API to identify potentially fraudulent activities so the company can review the activities and reject fraudulent transactions.

Question 21

A company has a podcast platform that has thousands of users. The company implemented an algorithm to detect low podcast engagement based on a 10-minute running window of user events such as listening to. pausing, and closing the podcast. A machine learning (ML) specialist is designing the ingestion process for these events. The ML specialist needs to transform the data to prepare the data for inference.How should the ML specialist design the transformation step to meet these requirements with the LEAST operational effort?

Accepted Answer

Use Amazon Kinesis Data Streams to ingest event data. Use Amazon Managed Service for Apache Flink (previously known as Amazon Kinesis Data Analytics) to transform the most recent 10 minutes of data before inference.

Answer

Use an Amazon Managed Streaming for Apache Kafka (Amazon MSK) cluster to ingest event data. Use Amazon Managed Service for Apache Flink (previously known as Amazon Kinesis Data Analytics) to transform the most recent 10 minutes of data before inference.

Answer

Use Amazon Kinesis Data Streams to ingest event data. Store the data in Amazon S3 by using Amazon Data Firehose. Use AWS Lambda to transform the most recent 10 minutes of data before inference.

Answer

Use an Amazon Managed Streaming for Apache Kafka (Amazon MSK) cluster to ingest event data. Use AWS Lambda to transform the most recent 10 minutes of data before inference.

Question 22

A finance company has collected stock return data for 5.000 publicly traded companies. A financial analyst has a dataset that contains 2.000 attributes for each company. The financial analyst wants to use Amazon SageMaker to identify the top 15 attributes that are most valuable to predict future stock returns.Which solution will meet these requirements with the LEAST operational overhead?

Accepted Answer

Use Amazon SageMaker Autopilot to build a regression model to predict the stock returns. Identify the most predictive features based on an Amazon SageMaker Clarify report.

Answer

Use the linear learner algorithm in SageMaker to train a linear regression model to predict the stock returns. Identify the most predictive features by ranking absolute coefficient values.

Answer

Use random forest regression in SageMaker to train a model to predict the stock returns. Identify the most predictive features based on Gini importance scores.

Answer

Use an Amazon SageMaker Data Wrangler quick model visualization to predict the stock returns. Identify the most predictive features based on the quick model's feature importance scores.

Question 23

A company's machine learning (ML) specialist is designing a scalable data storage solution for Amazon SageMaker. The company has an existing TensorFlow-based model that uses a train.py script. The model relies on static training data that is currently stored in TFRecord format.What should the ML specialist do to provide the training data to SageMaker with the LEAST development overhead?

Accepted Answer

Use SageMaker script mode, and use train.py unchanged. Put the TFRecord data into an Amazon S3 bucket. Point the SageMaker training invocation to the S3 bucket without reformatting the training data.

Answer

Put the TFRecord data into an Amazon S3 bucket. Use AWS Glue or AWS Lambda to reformat the data to protobuf format and store the data in a second S3 bucket. Point the SageMaker training invocation to the second S3 bucket.

Answer

Rewrite the train.py script to add a section that converts TFRecord data to protobuf format. Point the SageMaker training invocation to the local path of the data. Ingest the protobuf data instead of the TFRecord data.

Answer

Use SageMaker script mode, and use train.py unchanged. Point the SageMaker training invocation to the local path of the data without reformatting the training data.

Question 24

An ecommerce company wants to train a large image classification model with 10.000 classes. The company runs multiple model training iterations and needs to minimize operational overhead and cost. The company also needs to avoid loss of work and model retraining.Which solution will meet these requirements?

Accepted Answer

Use managed spot training in Amazon SageMaker. Launch the training jobs with checkpointing enabled.

Answer

Create the training jobs as AWS Batch jobs that use Amazon EC2 Spot Instances in a managed compute environment.

Answer

Use Amazon EC2 Spot Instances to run the training jobs. Use a Spot Instance interruption notice to save a snapshot of the model to Amazon S3 before an instance is terminated.

Answer

Use AWS Lambda to run the training jobs. Save model weights to Amazon S3.

Question 25

A company stores its documents in Amazon S3 with no predefined product categories. A data scientist needs to build a machine learning model to categorize the documents for all the company's products.Which solution will meet these requirements with the MOST operational efficiency?

Accepted Answer

Train an Amazon SageMaker Neural Topic Model (NTM) model to generate the product categories.

Answer

Build a custom clustering model. Create a Dockerfile and build a Docker image. Register the Docker image in Amazon Elastic Container Registry (Amazon ECR). Use the custom image in Amazon SageMaker to generate a trained model.

Answer

Tokenize the data and transform the data into tabulai data. Train an Amazon SageMaker k-means mode to generate the product categories.

Answer

Train an Amazon SageMaker Blazing Text model to generate the product categories.

Question 26

A machine learning (ML) specialist is building a credit score model for a financial institution. The ML specialist has collected data for the previous 3 years of transactions and third-party metadata that is related to the transactions.After the ML specialist builds the initial model, the ML specialist discovers that the model has low accuracy for both the training data and the test data. The ML specialist needs to improve the accuracy of the model.Which solutions will meet this requirement? (Select TWO.)

Accepted Answer

Increase the number of passes on the existing training data. Perform more hyperparameter tuning.

Accepted Answer

Add new domain-specific features. Use more complex models.

Answer

Increase the amount of regularization. Use fewer feature combinations.

Answer

Use fewer feature combinations. Decrease the number of numeric attribute bins.

Answer

Decrease the amount of training data examples. Reduce the number of passes on the existing training data.

Question 27

A data scientist uses Amazon SageMaker Data Wrangler to analyze and visualize data. The data scientist wants to refine a training dataset by selecting predictor variables that are strongly predictive of the target variable. The target variable correlates with other predictor variables.The data scientist wants to understand the variance in the data along various directions in the feature space.Which solution will meet these requirements?

Accepted Answer

Use the SageMaker Data Wrangler multicollinearity measurement features with the principal component analysis (PCA) algorithm to provide a feature space that includes all of the predictor variables.

Answer

Use the SageMaker Data Wrangler multicollinearity measurement features with a variance inflation factor (VIF) score. Use the VIF score as a measurement of how closely the variables are related to each other.

Answer

Use the SageMaker Data Wrangler Data Quality and Insights Report quick model visualization to estimate the expected quality of a model that is trained on the data.

Answer

Use the SageMaker Data Wrangler Data Quality and Insights Report feature to review features by their predictive power.

Question 28

Acybersecurity company is collecting on-premises server logs, mobile app logs, and loT sensor data. The company backs up the ingested data in an Amazon S3 bucket and sends the ingested data to Amazon OpenSearch Service for further analysis. Currently, the company has a custom ingestion pipeline that is running on Amazon EC2 instances. The company needs to implement a new serverless ingestion pipeline that can automatically scale to handle sudden changes in the data flow.Which solution will meet these requirements MOST cost-effectively?

Accepted Answer

Create one Amazon Kinesis data stream. Create two Amazon Data Firehose delivery streams to send data to the S3 bucket and OpenSearch Service. Connect the delivery streams to the data stream. Configure the data sources to send data to the data stream.

Answer

Create two Amazon Data Firehose delivery streams to send data to the S3 bucket and OpenSearch Service. Configure the data sources to send data to the delivery streams.

Answer

Create one Amazon Data Firehose delivery stream to send data to OpenSearch Service. Configure the delivery stream to back up the raw data to the S3 bucket. Configure the data sources to send data to the delivery stream.

Answer

Create one Amazon Kinesis data stream. Create one Amazon Data Firehose delivery stream to send data to OpenSearch Service. Configure the delivery stream to back up the data to the S3 bucket. Connect the delivery stream to the data stream. Configure the data sources to send data to the data stream.

Question 29

A company decides to use Amazon SageMaker to develop machine learning (ML) models. The company will host SageMaker notebook instances in a VPC. The company stores training data in an Amazon S3 bucket. Company security policy states that SageMaker notebook instances must not have internet connectivity.Which solution will meet the company's security requirements?

Accepted Answer

Configure the VPC that contains the SageMaker notebook instances to use VPC interface endpoints to establish connections for training and hosting. Modify any existing security groups that are associated with the VPC interface endpoint to only allow outbound connections for training and hosting.

Answer

Connect the SageMaker notebook instances that are in the VPC by using AWS Site-to-Site VPN to encrypt all internet-bound traffic. Configure VPC flow logs. Monitor all network traffic to detect and prevent any malicious activity.

Answer

Create an IAM policy that prevents access to the internet. Apply the IAM policy to an IAM role. Assign the IAM role to the SageMaker notebook instances in addition to any IAM roles that are already assigned to the instances.

Answer

Create VPC security groups to prevent all incoming and outgoing traffic. Assign the security groups to the SageMaker notebook instances.

Question 30

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 specialist notices that two features are perfectly linearly dependent.Why could this be an issue for the linear least squares regression model?

Accepted Answer

It could create a singular matrix during optimization, which fails to define a unique solution.

Answer

It could cause the backpropagation algorithm to fail during training.

Answer

It could modify the loss function during optimization, causing it to fail during training.

Answer

It could introduce non-linear dependencies within the data, which could invalidate the linear assumptions of the model.

Question 31

A company is building a predictive maintenance system using real-time data from devices on remote sites. There is no AWS Direct Connect connection or VPN connection between the sites and the company's VPC. The data needs to be ingested in real time from the devices into Amazon S3.Transformation is needed to convert the raw data into clean .csv data to be fed into the machine learning (ML) model. The transformation needs to happen during the ingestion process. When transformation fails, the records need to be stored in a specific location in Amazon S3 for human review. The raw data before transformation also needs to be stored in Amazon S3.How should an ML specialist architect the solution to meet these requirements with the LEAST effort?

Accepted Answer

Use Amazon Data Firehose with Amazon S3 as the destination. Configure Firehose to invoke an AWS Lambda function for data transformation. Enable source record backup on Firehose.

Answer

Use Amazon Managed Streaming for Apache Kafka. Set up workers in Amazon Elastic Container Service (Amazon ECS) to move data from Kafka brokers to Amazon S3 while transforming it. Configure workers to store raw and unsuccessfully transformed data in different S3 buckets.

Answer

Use Amazon Data Firehose with Amazon S3 as the destination. Configure Firehose to invoke an Apache Spark job in AWS Glue for data transformation. Enable source record backup and configure the error prefix.

Answer

Use Amazon Kinesis Data Streams in front of Amazon Data Firehose. Use Kinesis Data Streams with AWS Lambda to store raw data in Amazon S3. Configure Firehose to invoke a Lambda function for data transformation with Amazon S3 as the destination.

Question 32

A machine learning (ML) specialist is running an Amazon SageMaker hyperparameter optimization job for a model that is based on the XGBoost algorithm. The ML specialist selects Root Mean Square Error (RMSE) as the objective evaluation metric.The ML specialist discovers that the model is overfitting and cannot generalize well on the validation data. The ML specialist decides to resolve the model overfitting by using SageMaker automatic model tuning (AMT).Which solution will meet this requirement?

Accepted Answer

Configure SageMaker AMT to stop training jobs early.

Answer

Configure SageMaker AMT to use a static range of hyperparameter values.

Answer

Configure SageMaker AMT to increase the number of parallel training jobs.

Answer

Configure SageMaker AMT to run the training jobs with a warm start.

Question 33

A car company has dealership locations in multiple cities. The company uses a machine learning (ML) recommendation system to market cars to its customers.An ML engineer trained the ML recommendation model on a dataset that includes multiple attributes about each car. The dataset includes attributes such as car brand, car type, fuel efficiency, and price.The ML engineer uses Amazon SageMaker Data Wrangler to analyze and visualize data. The ML engineer needs to identify the distribution of car prices for a specific type of car.Which type of visualization should the ML engineer use to meet these requirements?

Accepted Answer

Use the SageMaker Data Wrangler histogram visualization to inspect the range of values for the specific feature.

Answer

Use the SageMaker Data Wrangler scatter plot visualization to inspect the relationship between the car price and type of car.

Answer

Use the SageMaker Data Wrangler quick model visualization to quickly evaluate the data and produce importance scores for the car price and type of car.

Answer

Use the SageMaker Data Wrangler anomaly detection visualization to identify outliers for the specific features.

Question 34

A sports analytics company is providing services at a marathon. Each runner in the marathon will have their race ID printed as text on the front of their shirt. The company needs to extract race IDs from images of the runners.Which solution will meet these requirements with the LEAST operational overhead?

Accepted Answer

Use Amazon Rekognition.

Answer

Use a custom convolutional neural network (CNN).

Answer

Use the Amazon SageMaker Object Detection algorithm.

Answer

Use Amazon Lookout for Vision.

Question 35

A retail company collects customer comments about its products from social media, the company website, and customer call logs. A team of data scientists and engineers wants to find common topics and determine which products the customers are referring to in their comments. The team is using natural language processing (NLP) to build a model to help with this classification.Each product can be classified into multiple categories that the company defines. These categories are related but are not mutually exclusive. For example, if there is mention of 'Sample Yogurt' in the document of customer comments, then 'Sample Yogurt' should be classified as 'yogurt,' 'snack,' and 'dairy product.'The team is using Amazon Comprehend to train the model and must complete the project as soon as possible.Which functionality of Amazon Comprehend should the team use to meet these requirements?

Accepted Answer

Custom classification with multi-label mode

Answer

Custom classification with multi-class mode

Answer

Custom entity recognition

Answer

Built-in models

Question 36

A manufacturing company wants to monitor its devices for anomalous behavior. A data scientist has trained an Amazon SageMaker scikit-learn model that classifies a device as normal or anomalous based on its 4-day telemetry. The 4-day telemetry of each device is collected in a separate file and is placed in an Amazon S3 bucket once every hour. The total time to run the model across the telemetry for all devices is 5 minutes.What is the MOST cost-effective solution for the company to use to run the model across the telemetry for all the devices?

Accepted Answer

SageMaker Batch Transform

Answer

SageMaker Asynchronous Inference

Answer

SageMaker Processing

Answer

A SageMaker multi-container endpoint

Question 37

A company operates an amusement park. The company wants to collect, monitor, and store real-time traffic data at several park entrances by using strategically placed cameras. The company's security team must be able to immediately access the data for viewing. Stored data must be indexed and must be accessible to the company's data science team.Which solution will meet these requirements MOST cost-effectively?

Accepted Answer

Use Amazon Kinesis Video Streams to ingest, index, and store the data. Use the built-in HTTP live streaming (HLS) capability for viewing by the security team.

Answer

Use Amazon Kinesis Video Streams to ingest, index, and store the data. Use the built-in integration with Amazon Rekognition for viewing by the security team.

Answer

Use Amazon Rekognition Video and the GStreamer plugin to ingest the data for viewing by the security team. Use Amazon Kinesis Data Streams to index and store the data.

Answer

Use Amazon Data Firehose to ingest, index, and store the data. Use the built-in HTTP live streaming (HLS) capability for viewing by the security team.

Question 38

A machine learning (ML) specialist is developing a model for a company. The model will classify and predict sequences of objects that are displayed in a video. The ML specialist decides to use a hybrid architecture that consists of a convolutional neural network (CNN) followed by a classifier three-layer recurrent neural network (RNN).The company developed a similar model previously but trained the model to classify a different set of objects. The ML specialist wants to save time by using the previously trained model and adapting the model for the current use case and set of objects.Which combination of steps will accomplish this goal with the LEAST amount of effort? (Select TWO.)

Accepted Answer

Reinitialize the weights of the last fully connected layer of the CNN. Retrain the CNN on the classification task by using the new set of objects.

Accepted Answer

Reinitialize the weights of the last layer of the RNN. Retrain the entire model on the prediction task by using the new set of objects.

Answer

Reinitialize the weights of the entire CNN. Retrain the CNN on the classification task by using the new set of objects.

Answer

Reinitialize the weights of the entire network. Retrain the entire network on the prediction task by using the new set of objects.

Answer

Reinitialize the weights of the entire RNN. Retrain the entire model on the prediction task by using the new set of objects.

Question 39

A growing company has a business-critical key performance indicator (KPI) for the uptime of a machine learning (ML) recommendation system. The company is using Amazon SageMaker hosting services to develop a recommendation model in a single Availability Zone within an AWS Region.A machine learning (ML) specialist must develop a solution to achieve high availability. The solution must have a recovery time objective (RTO) of 5 minutes.Which solution will meet these requirements with the LEAST effort?

Accepted Answer

Deploy multiple instances for each production endpoint in a VPC that spans at least two subnets that are in a second Availability Zone.

Answer

Deploy multiple instances for each endpoint in a VPC that spans at least two Regions.

Answer

Use the SageMaker auto scaling feature for the hosted recommendation models.

Answer

Frequently generate backups of the production recommendation model. Deploy the backups in a second Region.

Question 40

A machine learning (ML) engineer is integrating a production model with a customer metadata repository for real-time inference. The repository is hosted in Amazon SageMaker Feature Store. The engineer wants to retrieve only the latest version of the customer metadata record for a single customer at a time.Which solution will meet these requirements?

Accepted Answer

Use the SageMaker Feature Store GetRecord API with the record identifier.

Answer

Use the SageMaker Feature Store BatchGetRecord API with the record identifier. Filter to find the latest record.

Answer

Create an Amazon Athena query to retrieve the data from the feature table.

Answer

Create an Amazon Athena query to retrieve the data from the feature table. Use the write_time value to find the latest record.

Amazon MLS-C01 Practice Test 8

Amazon MLS-C01 Practice Tests

Amazon MLS-C01 Practice Test 8

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Amazon MLS-C01 Practice Tests

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