Quiz IT certification exam

Question 1

A company is using Amazon Textract to extract textual data from thousands of scanned text-heavy legal documents daily. The company uses this information to process loan applications automatically. Some of the documents fail business validation and are returned to human reviewers, who investigate the errors. This activity increases the time to process the loan applications.

What should the company do to reduce the processing time of loan applications?

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Accepted Answer

Configure Amazon Textract to route low-confidence predictions to Amazon Augmented AI (Amazon A2I). Perform a manual review on those words before performing a business validation.

Answer

Configure Amazon Textract to route low-confidence predictions to Amazon SageMaker Ground Truth. Perform a manual review on those words before performing a business validation.

Answer

Use an Amazon Textract synchronous operation instead of an asynchronous operation.

Answer

Use Amazon Rekognition's feature to detect text in an image to extract the data from scanned images. Use this information to process the loan applications.

Question 2

A company ingests machine learning (ML) data from web advertising clicks into an Amazon S3 data lake. Click data is added to an Amazon Kinesis data stream by using the Kinesis Producer Library (KPL). The data is loaded into the S3 data lake from the data stream by using an Amazon Kinesis Data Firehose delivery stream. As the data volume increases, an ML specialist notices that the rate of data ingested into Amazon S3 is relatively constant. There also is an increasing backlog of data for Kinesis Data Streams and Kinesis Data Firehose to ingest.Which next step is MOST likely to improve the data ingestion rate into Amazon S3?

Accepted Answer

Increase the number of shards for the data stream.

Answer

Increase the number of S3 prefixes for the delivery stream to write to.

Answer

Decrease the retention period for the data stream.

Answer

Add more consumers using the Kinesis Client Library (KCL).

Question 3

A machine learning specialist is developing a regression model to predict rental rates from rental listings. A variable named Wall_Color represents the most prominent exterior wall color of the property. The following is the sample data, excluding all other variables:<img alt="Amazon MLS-C01 image Question 203 4767 09162024005655000000"   width="209" height="96" src="https://examgecko.com/getfile/f015d714-9940-4174-ac4b-bd3f19b8f5ac">The specialist chose a model that needs numerical input data.Which feature engineering approaches should the specialist use to allow the regression model to learn from the Wall_Color data? (Choose two.)

Accepted Answer

Add new columns that store one-hot representation of colors.

Accepted Answer

Create three columns to encode the color in RGB format.

Answer

Apply integer transformation and set Red = 1, White = 5, and Green = 10.

Answer

Replace the color name string by its length.

Answer

Replace each color name by its training set frequency.

Question 4

A data scientist is working on a public sector project for an urban traffic system. While studying the traffic patterns, it is clear to the data scientist that the traffic behavior at each light is correlated, subject to a small stochastic error term. The data scientist must model the traffic behavior to analyze the traffic patterns and reduce congestion.How will the data scientist MOST effectively model the problem?

Accepted Answer

The data scientist should obtain a correlated equilibrium policy by formulating this problem as a multi-agent reinforcement learning problem.

Answer

The data scientist should obtain the optimal equilibrium policy by formulating this problem as a single-agent reinforcement learning problem.

Answer

Rather than finding an equilibrium policy, the data scientist should obtain accurate predictors of traffic flow by using historical data through a supervised learning approach.

Answer

Rather than finding an equilibrium policy, the data scientist should obtain accurate predictors of traffic flow by using unlabeled simulated data representing the new traffic patterns in the city and applying an unsupervised learning approach.

Question 5

A data scientist is using the Amazon SageMaker Neural Topic Model (NTM) algorithm to build a model that recommends tags from blog posts. The raw blog post data is stored in an Amazon S3 bucket in JSON format. During model evaluation, the data scientist discovered that the model recommends certain stopwords such as 'a,' 'an,'' and 'the' as tags to certain blog posts, along with a few rare words that are present only in certain blog entries. After a few iterations of tag review with the content team, the data scientist notices that the rare words are unusual but feasible. The data scientist also must ensure that the tag recommendations of the generated model do not include the stopwords.What should the data scientist do to meet these requirements?

Accepted Answer

Remove the stop words from the blog post data by using the Count Vectorizer function in the scikit-learn library. Replace the blog post data in the S3 bucket with the results of the vectorizer.

Answer

Use the Amazon Comprehend entity recognition API operations. Remove the detected words from the blog post data. Replace the blog post data source in the S3 bucket.

Answer

Run the SageMaker built-in principal component analysis (PCA) algorithm with the blog post data from the S3 bucket as the data source. Replace the blog post data in the S3 bucket with the results of the training job.

Answer

Use the SageMaker built-in Object Detection algorithm instead of the NTM algorithm for the training job to process the blog post data.

Question 6

A company wants to create a data repository in the AWS Cloud for machine learning (ML) projects. The company wants to use AWS to perform complete ML lifecycles and wants to use Amazon S3 for the data storage. All of the company's data currently resides on premises and is 40 in size.The company wants a solution that can transfer and automatically update data between the on-premises object storage and Amazon S3. The solution must support encryption, scheduling, monitoring, and data integrity validation.Which solution meets these requirements?

Accepted Answer

Use AWS DataSync to make an initial copy of the entire dataset. Schedule subsequent incremental transfers of changing data until the final cutover from on premises to AWS.

Answer

Use the S3 sync command to compare the source S3 bucket and the destination S3 bucket. Determine which source files do not exist in the destination S3 bucket and which source files were modified.

Answer

Use AWS Transfer for FTPS to transfer the files from the on-premises storage to Amazon S3.

Answer

Use S3 Batch Operations to pull data periodically from the on-premises storage. Enable S3 Versioning on the S3 bucket to protect against accidental overwrites.

Question 7

A company has video feeds and images of a subway train station. The company wants to create a deep learning model that will alert the station manager if any passenger crosses the yellow safety line when there is no train in the station. The alert will be based on the video feeds. The company wants the model to detect the yellow line, the passengers who cross the yellow line, and the trains in the video feeds. This task requires labeling. The video data must remain confidential.A data scientist creates a bounding box to label the sample data and uses an object detection model. However, the object detection model cannot clearly demarcate the yellow line, the passengers who cross the yellow line, and the trains.Which labeling approach will help the company improve this model?

Accepted Answer

Use an Amazon SageMaker Ground Truth semantic segmentation labeling task. Use a private workforce as the labeling workforce.

Answer

Use Amazon Rekognition Custom Labels to label the dataset and create a custom Amazon Rekognition object detection model. Create a private workforce. Use Amazon Augmented AI (Amazon A2I) to review the low-confidence predictions and retrain the custom Amazon Rekognition model.

Answer

Use an Amazon SageMaker Ground Truth object detection labeling task. Use Amazon Mechanical Turk as the labeling workforce.

Answer

Use Amazon Rekognition Custom Labels to label the dataset and create a custom Amazon Rekognition object detection model. Create a workforce with a third-party AWS Marketplace vendor. Use Amazon Augmented AI (Amazon A2I) to review the low-confidence predictions and retrain the custom Amazon Rekognition model.

Question 8

A data engineer at a bank is evaluating a new tabular dataset that includes customer data. The data engineer will use the customer data to create a new model to predict customer behavior. After creating a correlation matrix for the variables, the data engineer notices that many of the 100 features are highly correlated with each other.Which steps should the data engineer take to address this issue? (Choose two.)

Accepted Answer

Apply principal component analysis (PCA).

Accepted Answer

Remove a portion of highly correlated features from the dataset.

Answer

Use a linear-based algorithm to train the model.

Answer

Apply min-max feature scaling to the dataset.

Answer

Apply one-hot encoding category-based variables.

Question 9

A company is building a new version of a recommendation engine. Machine learning (ML) specialists need to keep adding new data from users to improve personalized recommendations. The ML specialists gather data from the users' interactions on the platform and from sources such as external websites and social media.The pipeline cleans, transforms, enriches, and compresses terabytes of data daily, and this data is stored in Amazon S3. A set of Python scripts was coded to do the job and is stored in a large Amazon EC2 instance. The whole process takes more than 20 hours to finish, with each script taking at least an hour. The company wants to move the scripts out of Amazon EC2 into a more managed solution that will eliminate the need to maintain servers.Which approach will address all of these requirements with the LEAST development effort?

Accepted Answer

Create an AWS Glue job. Convert the scripts to PySpark. Execute the pipeline. Store the results in Amazon S3.

Answer

Load the data into an Amazon Redshift cluster. Execute the pipeline by using SQL. Store the results in Amazon S3.

Answer

Load the data into Amazon DynamoDB. Convert the scripts to an AWS Lambda function. Execute the pipeline by triggering Lambda executions. Store the results in Amazon S3.

Answer

Create a set of individual AWS Lambda functions to execute each of the scripts. Build a step function by using the AWS Step Functions Data Science SDK. Store the results in Amazon S3.

Question 10

A retail company is selling products through a global online marketplace. The company wants to use machine learning (ML) to analyze customer feedback and identify specific areas for improvement. A developer has built a tool that collects customer reviews from the online marketplace and stores them in an Amazon S3 bucket. This process yields a dataset of 40 reviews. A data scientist building the ML models must identify additional sources of data to increase the size of the dataset.Which data sources should the data scientist use to augment the dataset of reviews? (Choose three.)

Accepted Answer

Emails exchanged by customers and the company's customer service agents

Accepted Answer

Social media posts containing the name of the company or its products

Accepted Answer

A publicly available collection of customer reviews

Answer

A publicly available collection of news articles

Answer

Product sales revenue figures for the company

Answer

Instruction manuals for the company's products

Question 11

A machine learning (ML) specialist wants to create a data preparation job that uses a PySpark script with complex window aggregation operations to create data for training and testing. The ML specialist needs to evaluate the impact of the number of features and the sample count on model performance.Which approach should the ML specialist use to determine the ideal data transformations for the model?

Accepted Answer

Add an Amazon SageMaker Experiments tracker to the script to capture key parameters. Run the script as a SageMaker processing job.

Answer

Add an Amazon SageMaker Debugger hook to the script to capture key metrics. Run the script as an AWS Glue job.

Answer

Add an Amazon SageMaker Experiments tracker to the script to capture key metrics. Run the script as an AWS Glue job.

Answer

Add an Amazon SageMaker Debugger hook to the script to capture key parameters. Run the script as a SageMaker processing job.

Question 12

A data scientist has a dataset of machine part images stored in Amazon Elastic File System (Amazon EFS). The data scientist needs to use Amazon SageMaker to create and train an image classification machine learning model based on this dataset. Because of budget and time constraints, management wants the data scientist to create and train a model with the least number of steps and integration work required.How should the data scientist meet these requirements?

Accepted Answer

Run a SageMaker training job with an EFS file system as the data source.

Answer

Mount the EFS file system to a SageMaker notebook and run a script that copies the data to an Amazon FSx for Lustre file system. Run the SageMaker training job with the FSx for Lustre file system as the data source.

Answer

Launch a transient Amazon EMR cluster. Configure steps to mount the EFS file system and copy the data to an Amazon S3 bucket by using S3DistCp. Run the SageMaker training job with Amazon S3 as the data source.

Answer

Mount the EFS file system to an Amazon EC2 instance and use the AWS CLI to copy the data to an Amazon S3 bucket. Run the SageMaker training job with Amazon S3 as the data source.

Question 13

A retail company uses a machine learning (ML) model for daily sales forecasting. The company's brand manager reports that the model has provided inaccurate results for the past 3 weeks.At the end of each day, an AWS Glue job consolidates the input data that is used for the forecasting with the actual daily sales data and the predictions of the model. The AWS Glue job stores the data in Amazon S3. The company's ML team is using an Amazon SageMaker Studio notebook to gain an understanding about the source of the model's inaccuracies.What should the ML team do on the SageMaker Studio notebook to visualize the model's degradation MOST accurately?

Accepted Answer

Create a histogram of the model errors over the last 3 weeks. In addition, create a histogram of the model errors from before that period.

Answer

Create a histogram of the daily sales over the last 3 weeks. In addition, create a histogram of the daily sales from before that period.

Answer

Create a line chart with the weekly mean absolute error (MAE) of the model.

Answer

Create a scatter plot of daily sales versus model error for the last 3 weeks. In addition, create a scatter plot of daily sales versus model error from before that period.

Question 14

An ecommerce company sends a weekly email newsletter to all of its customers. Management has hired a team of writers to create additional targeted content. A data scientist needs to identify five customer segments based on age, income, and location. The customers' current segmentation is unknown. The data scientist previously built an XGBoost model to predict the likelihood of a customer responding to an email based on age, income, and location.Why does the XGBoost model NOT meet the current requirements, and how can this be fixed?

Accepted Answer

The XGBoost model is a supervised machine learning algorithm. Train a k-means model with K = 5 on the same dataset to predict a segment.

Answer

The XGBoost model provides a true/false binary output. Apply principal component analysis (PCA) with five feature dimensions to predict a segment.

Answer

The XGBoost model provides a true/false binary output. Increase the number of classes the XGBoost model predicts to five classes to predict a segment.

Answer

The XGBoost model is a supervised machine learning algorithm. Train a k-Nearest-Neighbors (kNN) model with K = 5 on the same dataset to predict a segment.

Question 15

A global financial company is using machine learning to automate its loan approval process. The company has a dataset of customer information. The dataset contains some categorical fields, such as customer location by city and housing status. The dataset also includes financial fields in different units, such as account balances in US dollars and monthly interest in US cents.The company's data scientists are using a gradient boosting regression model to infer the credit score for each customer. The model has a training accuracy of 99% and a testing accuracy of 75%. The data scientists want to improve the model's testing accuracy.Which process will improve the testing accuracy the MOST?

Accepted Answer

Use a one-hot encoder for the categorical fields in the dataset. Perform standardization on the financial fields in the dataset. Apply L1 regularization to the data.

Answer

Use tokenization of the categorical fields in the dataset. Perform binning on the financial fields in the dataset. Remove the outliers in the data by using the z-score.

Answer

Use a label encoder for the categorical fields in the dataset. Perform L1 regularization on the financial fields in the dataset. Apply L2 regularization to the data.

Answer

Use a logarithm transformation on the categorical fields in the dataset. Perform binning on the financial fields in the dataset. Use imputation to populate missing values in the dataset.

Question 16

A machine learning (ML) specialist needs to extract embedding vectors from a text series. The goal is to provide a ready-to-ingest feature space for a data scientist to develop downstream ML predictive models. The text consists of curated sentences in English. Many sentences use similar words but in different contexts. There are questions and answers among the sentences, and the embedding space must differentiate between them.Which options can produce the required embedding vectors that capture word context and sequential QA information? (Choose two.)

Accepted Answer

Amazon SageMaker BlazingText algorithm in Skip-gram mode

Accepted Answer

Combination of the Amazon SageMaker BlazingText algorithm in Batch Skip-gram mode with a custom recurrent neural network (RNN)

Answer

Amazon SageMaker seq2seq algorithm

Answer

Amazon SageMaker Object2Vec algorithm

Answer

Amazon SageMaker BlazingText algorithm in continuous bag-of-words (CBOW) mode

Question 17

A company wants to detect credit card fraud. The company has observed that an average of 2% of credit card transactions are fraudulent. A data scientist trains a classifier on a year's worth of credit card transaction data. The classifier needs to identify the fraudulent transactions. The company wants to accurately capture as many fraudulent transactions as possible.Which metrics should the data scientist use to optimize the classifier? (Select TWO.)

Accepted Answer

Fl score

Accepted Answer

True positive rate

Answer

Specificity

Answer

False positive rate

Answer

Accuracy

Question 18

Each morning, a data scientist at a rental car company creates insights about the previous day's rental car reservation demands. The company needs to automate this process by streaming the data to Amazon S3 in near real time. The solution must detect high-demand rental cars at each of the company's locations. The solution also must create a visualization dashboard that automatically refreshes with the most recent data.Which solution will meet these requirements with the LEAST development time?

Accepted Answer

Use Amazon Kinesis Data Firehose to stream the reservation data directly to Amazon S3. Detect high-demand outliers by using Amazon QuickSight ML Insights. Visualize the data in QuickSight.

Answer

Use Amazon Kinesis Data Streams to stream the reservation data directly to Amazon S3. Detect high-demand outliers by using the Random Cut Forest (RCF) trained model in Amazon SageMaker. Visualize the data in Amazon QuickSight.

Answer

Use Amazon Kinesis Data Firehose to stream the reservation data directly to Amazon S3. Detect high-demand outliers by using the Random Cut Forest (RCF) trained model in Amazon SageMaker. Visualize the data in Amazon QuickSight.

Answer

Use Amazon Kinesis Data Streams to stream the reservation data directly to Amazon S3. Detect high-demand outliers by using Amazon QuickSight ML Insights. Visualize the data in QuickSight.

Question 19

A network security vendor needs to ingest telemetry data from thousands of endpoints that run all over the world. The data is transmitted every 30 seconds in the form of records that contain 50 fields. Each record is up to 1 KB in size. The security vendor uses Amazon Kinesis Data Streams to ingest the data. The vendor requires hourly summaries of the records that Kinesis Data Streams ingests. The vendor will use Amazon Athena to query the records and to generate the summaries. The Athena queries will target 7 to 12 of the available data fields.Which solution will meet these requirements with the LEAST amount of customization to transform and store the ingested data?

Accepted Answer

Use Amazon Kinesis Data Analytics to read and aggregate the data hourly. Transform the data and store it in Amazon S3 by using Amazon Kinesis Data Firehose.

Answer

Use AWS Lambda to read and aggregate the data hourly. Transform the data and store it in Amazon S3 by using Amazon Kinesis Data Firehose.

Answer

Use Amazon Kinesis Data Firehose to read and aggregate the data hourly. Transform the data and store it in Amazon S3 by using a short-lived Amazon EMR cluster.

Answer

Use Amazon Kinesis Data Firehose to read and aggregate the data hourly. Transform the data and store it in Amazon S3 by using AWS Lambda.

Question 20

A machine learning (ML) specialist uploads 5 TB of data to an Amazon SageMaker Studio environment. The ML specialist performs initial data cleansing. Before the ML specialist begins to train a model, the ML specialist needs to create and view an analysis report that details potential bias in the uploaded data.Which combination of actions will meet these requirements with the LEAST operational overhead? (Choose two.)

Accepted Answer

Use SageMaker Clarify to automatically detect data bias

Accepted Answer

Configure SageMaker Data Wrangler to generate a bias report.

Answer

Turn on the bias detection option in SageMaker Ground Truth to automatically analyze data features.

Answer

Use SageMaker Model Monitor to generate a bias drift report.

Answer

Use SageMaker Experiments to perform a data check

Question 21

A medical device company is building a machine learning (ML) model to predict the likelihood of device recall based on customer data that the company collects from a plain text survey. One of the survey questions asks which medications the customer is taking. The data for this field contains the names of medications that customers enter manually. Customers misspell some of the medication names. The column that contains the medication name data gives a categorical feature with high cardinality but redundancy.What is the MOST effective way to encode this categorical feature into a numeric feature?

Accepted Answer

Use Amazon SageMaker Data Wrangler similarity encoding on the column to create embeddings Of vectors Of real numbers.

Answer

Spell check the column. Use Amazon SageMaker one-hot encoding on the column to transform a categorical feature to a numerical feature.

Answer

Fix the spelling in the column by using char-RNN. Use Amazon SageMaker Data Wrangler one-hot encoding to transform a categorical feature to a numerical feature.

Answer

Use Amazon SageMaker Data Wrangler ordinal encoding on the column to encode categories into an integer between O and the total number Of categories in the column.

Question 22

A manufacturing company wants to create a machine learning (ML) model to predict when equipment is likely to fail. A data science team already constructed a deep learning model by using TensorFlow and a custom Python script in a local environment. The company wants to use Amazon SageMaker to train the model.Which TensorFlow estimator configuration will train the model MOST cost-effectively?

Accepted Answer

Turn on SageMaker Training Compiler by adding compiler_config=TrainingCompilerConfig() as a parameter. Turn on managed spot training by setting the use_spot_instances parameter to True. Pass the script to the estimator in the call to the TensorFlow fit() method.

Answer

Turn on SageMaker Training Compiler by adding compiler_config=TrainingCompilerConfig() as a parameter. Pass the script to the estimator in the call to the TensorFlow fit() method.

Answer

Adjust the training script to use distributed data parallelism. Specify appropriate values for the distribution parameter. Pass the script to the estimator in the call to the TensorFlow fit() method.

Answer

Turn on SageMaker Training Compiler by adding compiler_config=TrainingCompilerConfig() as a parameter. Set the MaxWaitTimeInSeconds parameter to be equal to the MaxRuntimeInSeconds parameter. Pass the script to the estimator in the call to the TensorFlow fit() method.

Question 23

A company is creating an application to identify, count, and classify animal images that are uploaded to the company's website. The company is using the Amazon SageMaker image classification algorithm with an ImageNetV2 convolutional neural network (CNN). The solution works well for most animal images but does not recognize many animal species that are less common.The company obtains 10,000 labeled images of less common animal species and stores the images in Amazon S3. A machine learning (ML) engineer needs to incorporate the images into the model by using Pipe mode in SageMaker.Which combination of steps should the ML engineer take to train the model? (Choose two.)

Accepted Answer

Create a .lst file that contains a list of image files and corresponding class labels. Upload the .lst file to Amazon S3.

Accepted Answer

Initiate transfer learning. Train the model by using the images of less common species.

Answer

Use a ResNet model. Initiate full training mode by initializing the network with random weights.

Answer

Use an Inception model that is available with the SageMaker image classification algorithm.

Answer

Use an augmented manifest file in JSON Lines format.

Question 24

A credit card company wants to identify fraudulent transactions in real time. A data scientist builds a machine learning model for this purpose. The transactional data is captured and stored in Amazon S3. The historic data is already labeled with two classes: fraud (positive) and fair transactions (negative). The data scientist removes all the missing data and builds a classifier by using the XGBoost algorithm in Amazon SageMaker. The model produces the following results:* True positive rate (TPR): 0.700* False negative rate (FNR): 0.300* True negative rate (TNR): 0.977* False positive rate (FPR): 0.023* Overall accuracy: 0.949Which solution should the data scientist use to improve the performance of the model?

Accepted Answer

Apply the Synthetic Minority Oversampling Technique (SMOTE) on the minority class in the training dataset. Retrain the model with the updated training data.

Answer

Apply the Synthetic Minority Oversampling Technique (SMOTE) on the majority class in the training dataset. Retrain the model with the updated training data.

Answer

Undersample the minority class.

Answer

Oversample the majority class.

Question 25

A company's data scientist has trained a new machine learning model that performs better on test data than the company's existing model performs in the production environment. The data scientist wants to replace the existing model that runs on an Amazon SageMaker endpoint in the production environment. However, the company is concerned that the new model might not work well on the production environment data.The data scientist needs to perform A/B testing in the production environment to evaluate whether the new model performs well on production environment data.Which combination of steps must the data scientist take to perform the A/B testing? (Choose two.)

Accepted Answer

Create a new endpoint configuration that includes a production variant for each of the two models.

Accepted Answer

Update the existing endpoint to use the new endpoint configuration.

Answer

Create a new endpoint configuration that includes two target variants that point to different endpoints.

Answer

Deploy the new model to the existing endpoint.

Answer

Update the existing endpoint to activate the new model.

Question 26

An online store is predicting future book sales by using a linear regression model that is based on past sales data. The data includes duration, a numerical feature that represents the number of days that a book has been listed in the online store. A data scientist performs an exploratory data analysis and discovers that the relationship between book sales and duration is skewed and non-linear.Which data transformation step should the data scientist take to improve the predictions of the model?

Accepted Answer

Quantile binning

Answer

One-hot encoding

Answer

Cartesian product transformation

Answer

Normalization

Question 27

A beauty supply store wants to understand some characteristics of visitors to the store. The store has security video recordings from the past several years. The store wants to generate a report of hourly visitors from the recordings. The report should group visitors by hair style and hair color.Which solution will meet these requirements with the LEAST amount of effort?

Accepted Answer

Use a semantic segmentation algorithm to identify a visitor's hair in video frames. Pass the identified hair to an ResNet-50 algorithm to determine hair style and hair color.

Answer

Use an object detection algorithm to identify a visitor's hair in video frames. Pass the identified hair to an ResNet-50 algorithm to determine hair style and hair color.

Answer

Use an object detection algorithm to identify a visitor's hair in video frames. Pass the identified hair to an XGBoost algorithm to determine hair style and hair color.

Answer

Use a semantic segmentation algorithm to identify a visitor's hair in video frames. Pass the identified hair to an XGBoost algorithm to determine hair style and hair.

Question 28

A company wants to predict stock market price trends. The company stores stock market data each business day in Amazon S3 in Apache Parquet format. The company stores 20 GB of data each day for each stock code.A data engineer must use Apache Spark to perform batch preprocessing data transformations quickly so the company can complete prediction jobs before the stock market opens the next day. The company plans to track more stock market codes and needs a way to scale the preprocessing data transformations.Which AWS service or feature will meet these requirements with the LEAST development effort over time?

Accepted Answer

AWS Glue jobs

Answer

Amazon EMR cluster

Answer

Amazon Athena

Answer

AWS Lambda

Question 29

A company wants to enhance audits for its machine learning (ML) systems. The auditing system must be able to perform metadata analysis on the features that the ML models use. The audit solution must generate a report that analyzes the metadata. The solution also must be able to set the data sensitivity and authorship of features.Which solution will meet these requirements with the LEAST development effort?

Accepted Answer

Use Amazon SageMaker Feature Store to set feature groups for the current features that the ML models use. Assign the required metadata for each feature. Use Amazon QuickSight to analyze the metadata.

Answer

Use Amazon SageMaker Feature Store to select the features. Create a data flow to perform feature-level metadata analysis. Create an Amazon DynamoDB table to store feature-level metadata. Use Amazon QuickSight to analyze the metadata.

Answer

Use Amazon SageMaker Feature Store to set feature groups for the current features that the ML models use. Assign the required metadata for each feature. Use SageMaker Studio to analyze the metadata.

Answer

Use Amazon SageMaker Features Store to apply custom algorithms to analyze the feature-level metadata that the company requires. Create an Amazon DynamoDB table to store feature-level metadata. Use Amazon QuickSight to analyze the metadata.

Question 30

A machine learning (ML) engineer has created a feature repository in Amazon SageMaker Feature Store for the company. The company has AWS accounts for development, integration, and production. The company hosts a feature store in the development account. The company uses Amazon S3 buckets to store feature values offline. The company wants to share features and to allow the integration account and the production account to reuse the features that are in the feature repository.Which combination of steps will meet these requirements? (Select TWO.)

Accepted Answer

Create an IAM role in the development account that the integration account and production account can assume. Attach IAM policies to the role that allow access to the feature repository and the S3 buckets.

Accepted Answer

Share the feature repository that is associated the S3 buckets from the development account to the integration account and the production account by using AWS Resource Access Manager (AWS RAM).

Answer

Use AWS Security Token Service (AWS STS) from the integration account and the production account to retrieve credentials for the development account.

Answer

Set up S3 replication between the development S3 buckets and the integration and production S3 buckets.

Answer

Create an AWS PrivateLink endpoint in the development account for SageMaker.

Question 31

A company processes millions of orders every day. The company uses Amazon DynamoDB tables to store order information. When customers submit new orders, the new orders are immediately added to the DynamoDB tables. New orders arrive in the DynamoDB tables continuously.A data scientist must build a peak-time prediction solution. The data scientist must also create an Amazon OuickSight dashboard to display near real-lime order insights. The data scientist needs to build a solution that will give QuickSight access to the data as soon as new order information arrives.Which solution will meet these requirements with the LEAST delay between when a new order is processed and when QuickSight can access the new order information?

Accepted Answer

Use Amazon Kinesis Data Streams to export the data from Amazon DynamoDB to Amazon S3. Configure OuickSight to access the data in Amazon S3.

Answer

Use AWS Glue to export the data from Amazon DynamoDB to Amazon S3. Configure OuickSight to access the data in Amazon S3.

Answer

Use an API call from OuickSight to access the data that is in Amazon DynamoDB directly

Answer

Use Amazon Kinesis Data Firehose to export the data from Amazon DynamoDB to Amazon S3. Configure OuickSight to access the data in Amazon S3.

Question 32

A retail company wants to build a recommendation system for the company's website. The system needs to provide recommendations for existing users and needs to base those recommendations on each user's past browsing history. The system also must filter out any items that the user previously purchased.Which solution will meet these requirements with the LEAST development effort?

Accepted Answer

Use an Amazon Personalize USER_ PERSONAL IZATION recipe to train a model Create a real-time filter to exclude items that the user previously purchased. Create and deploy a campaign on Amazon Personalize. Use the GetRecommendations API operation to get the real-time recommendations.

Answer

Train a model by using a user-based collaborative filtering algorithm on Amazon SageMaker. Host the model on a SageMaker real-time endpoint. Configure an Amazon API Gateway API and an AWS Lambda function to handle real-time inference requests that the web application sends. Exclude the items that the user previously purchased from the results before sending the results back to the web application.

Answer

Use an Amazon Personalize PERSONALIZED_RANKING recipe to train a model. Create a real-time filter to exclude items that the user previously purchased. Create and deploy a campaign on Amazon Personalize. Use the GetPersonalizedRanking API operation to get the real-time recommendations.

Answer

Train a neural collaborative filtering model on Amazon SageMaker by using GPU instances. Host the model on a SageMaker real-time endpoint. Configure an Amazon API Gateway API and an AWS Lambda function to handle real-time inference requests that the web application sends. Exclude the items that the user previously purchased from the results before sending the results back to the web application.

Question 33

A data engineer is preparing a dataset that a retail company will use to predict the number of visitors to stores. The data engineer created an Amazon S3 bucket. The engineer subscribed the S3 bucket to an AWS Data Exchange data product for general economic indicators. The data engineer wants to join the economic indicator data to an existing table in Amazon Athena to merge with the business data. All these transformations must finish running in 30-60 minutes.Which solution will meet these requirements MOST cost-effectively?

Accepted Answer

Use an S3 event on the AWS Data Exchange S3 bucket to invoke an AWS Lambda function. Program the Lambda function to use Amazon SageMaker Data Wrangler to merge the existing business data with the Athena table. Write the result set back to Amazon S3.

Answer

Configure the AWS Data Exchange product as a producer for an Amazon Kinesis data stream. Use an Amazon Kinesis Data Firehose delivery stream to transfer the data to Amazon S3 Run an AWS Glue job that will merge the existing business data with the Athena table. Write the result set back to Amazon S3.

Answer

Use an S3 event on the AWS Data Exchange S3 bucket to invoke an AWS Lambda Function Program the Lambda function to run an AWS Glue job that will merge the existing business data with the Athena table Write the results back to Amazon S3.

Answer

Provision an Amazon Redshift cluster. Subscribe to the AWS Data Exchange product and use the product to create an Amazon Redshift Table Merge the data in Amazon Redshift. Write the results back to Amazon S3.

Question 34

A social media company wants to develop a machine learning (ML) model to detect Inappropriate or offensive content in images. The company has collected a large dataset of labeled images and plans to use the built-in Amazon SageMaker image classification algorithm to train the model. The company also intends to use SageMaker pipe mode to speed up the training....company splits the dataset into training, validation, and testing datasets. The company stores the training and validation images in folders that are named Training and Validation, respectively. The folder ...ain subfolders that correspond to the names of the dataset classes. The company resizes the images to the same sue and generates two input manifest files named training.1st and validation.1st, for the ..ing dataset and the validation dataset. respectively. Finally, the company creates two separate Amazon S3 buckets for uploads of the training dataset and the validation dataset....h additional data preparation steps should the company take before uploading the files to Amazon S3?

Accepted Answer

Generate two RecordIO files, training rec and validation.rec. from the manifest files by using the im2rec Apache MXNet utility tool. Upload the RecordlO files to the training S3 bucket.

Answer

Generate two Apache Parquet files, training.parquet and validation.parquet. by reading the images into a Pandas data frame and storing the data frame as a Parquet file. Upload the Parquet files to the training S3 bucket

Answer

Compress the training and validation directories by using the Snappy compression library Upload the manifest and compressed files to the training S3 bucket

Answer

Compress the training and validation directories by using the gzip compression library. Upload the manifest and compressed files to the training S3 bucket.

Question 35

A company operates large cranes at a busy port. The company plans to use machine learning (ML) for predictive maintenance of the cranes to avoid unexpected breakdowns and to improve productivity.The company already uses sensor data from each crane to monitor the health of the cranes in real time. The sensor data includes rotation speed, tension, energy consumption, vibration, pressure, and ...perature for each crane. The company contracts AWS ML experts to implement an ML solution.Which potential findings would indicate that an ML-based solution is suitable for this scenario? (Select TWO.)

Accepted Answer

The historical sensor data from the cranes are available with high granularity for the last 3 years.

Accepted Answer

The historical sensor data contains most common types of crane failures that the company wants to predict.

Answer

The historical sensor data does not include a significant number of data points and attributes for certain time periods.

Answer

The historical sensor data shows that simple rule-based thresholds can predict crane failures.

Answer

The historical sensor data contains failure data for only one type of crane model that is in operation and lacks failure data of most other types of crane that are in operation.

Question 36

A company wants to create an artificial intelligence (Al) yoga instructor that can lead large classes of students. The company needs to create a feature that can accurately count the number of students who are in a class. The company also needs a feature that can differentiate students who are performing a yoga stretch correctly from students who are performing a stretch incorrectly....etermine whether students are performing a stretch correctly, the solution needs to measure the location and angle of each student's arms and legs A data scientist must use Amazon SageMaker to ...ss video footage of a yoga class by extracting image frames and applying computer vision models.Which combination of models will meet these requirements with the LEAST effort? (Select TWO.)

Accepted Answer

Object Detection

Accepted Answer

Pose estimation

Answer

Image Classification

Answer

Optical Character Recognition (OCR)

Answer

Image Generative Adversarial Networks (GANs)

Question 37

A wildlife research company has a set of images of lions and cheetahs. The company created a dataset of the images. The company labeled each image with a binary label that indicates whether an image contains a lion or cheetah. The company wants to train a model to identify whether new images contain a lion or cheetah..... Dh Amazon SageMaker algorithm will meet this requirement?

Accepted Answer

Image Classification - TensorFlow

Answer

XGBoost

Answer

Object Detection - TensorFlow

Answer

Semantic segmentation - MXNet

Question 38

An ecommerce company has used Amazon SageMaker to deploy a factorization machines (FM) model to suggest products for customers. The company's data science team has developed two new models by using the TensorFlow and PyTorch deep learning frameworks. The company needs to use A/B testing to evaluate the new models against the deployed model....required A/B testing setup is as follows:* Send 70% of traffic to the FM model, 15% of traffic to the TensorFlow model, and 15% of traffic to the Py Torch model.* For customers who are from Europe, send all traffic to the TensorFlow model..sh architecture can the company use to implement the required A/B testing setup?

Accepted Answer

Create two production variants for the TensorFlow and PyTorch models. Specify the weight for each production variant in the SageMaker endpoint configuration. Update the existing SageMaker endpoint with the new configuration. To send traffic to the TensorFlow model for customers who are from Europe, set the TargetVariant header in the request to point to the variant name of the TensorFlow model.

Answer

Create two new SageMaker endpoints for the TensorFlow and PyTorch models in addition to the existing SageMaker endpoint. Create an Application Load Balancer Create a target group for each endpoint. Configure listener rules and add weight to the target groups. To send traffic to the TensorFlow model for customers who are from Europe, create an additional listener rule to forward traffic to the TensorFlow target group.

Answer

Create two production variants for the TensorFlow and PyTorch models. Create an auto scaling policy and configure the desired A/B weights to direct traffic to each production variant Update the existing SageMaker endpoint with the auto scaling policy. To send traffic to the TensorFlow model for customers who are from Europe, set the TargetVariant header in the request to point to the variant name of the TensorFlow model.

Answer

Create two new SageMaker endpoints for the TensorFlow and PyTorch models in addition to the existing SageMaker endpoint. Create a Network Load Balancer. Create a target group for each endpoint. Configure listener rules and add weight to the target groups. To send traffic to the TensorFlow model for customers who are from Europe, create an additional listener rule to forward traffic to the TensorFlow target group.

Question 39

A data scientist stores financial datasets in Amazon S3. The data scientist uses Amazon Athena to query the datasets by using SQL.The data scientist uses Amazon SageMaker to deploy a machine learning (ML) model. The data scientist wants to obtain inferences from the model at the SageMaker endpoint However, when the data .... ntist attempts to invoke the SageMaker endpoint, the data scientist receives SOL statement failures The data scientist's 1AM user is currently unable to invoke the SageMaker endpointWhich combination of actions will give the data scientist's 1AM user the ability to invoke the SageMaker endpoint? (Select THREE.)

Accepted Answer

Include a policy statement for the data scientist's 1AM user that allows the 1AM user to perform the sagemaker: lnvokeEndpoint action,

Accepted Answer

Include an inline policy for the data scientist's 1AM user that allows SageMaker to read S3 objects

Accepted Answer

Include the SQL statement 'USING EXTERNAL FUNCTION ml_function_name' in the Athena SQL query.

Answer

Attach the AmazonAthenaFullAccess AWS managed policy to the user identity.

Answer

Include a policy statement for the data scientist's 1AM user that allows the 1AM user to perform the sagemakerGetRecord action.

Answer

Perform a user remapping in SageMaker to map the 1AM user to another 1AM user that is on the hosted endpoint.

Question 40

A company is using Amazon SageMaker to build a machine learning (ML) model to predict customer churn based on customer call transcripts. Audio files from customer calls are located in an on-premises VoIP system that has petabytes of recorded calls. The on-premises infrastructure has high-velocity networking and connects to the company's AWS infrastructure through a VPN connection over a 100 Mbps connection.The company has an algorithm for transcribing customer calls that requires GPUs for inference. The company wants to store these transcriptions in an Amazon S3 bucket in the AWS Cloud for model development.Which solution should an ML specialist use to deliver the transcriptions to the S3 bucket as quickly as possible?

Accepted Answer

Order and use an AWS Snowball Edge Compute Optimized device with an NVIDIA Tesla module to run the transcription algorithm. Use AWS DataSync to send the resulting transcriptions to the transcription S3 bucket.

Answer

Order and use an AWS Snowcone device with Amazon EC2 Inf1 instances to run the transcription algorithm Use AWS DataSync to send the resulting transcriptions to the transcription S3 bucket

Answer

Order and use AWS Outposts to run the transcription algorithm on GPU-based Amazon EC2 instances. Store the resulting transcriptions in the transcription S3 bucket.

Answer

Use AWS DataSync to ingest the audio files to Amazon S3. Create an AWS Lambda function to run the transcription algorithm on the audio files when they are uploaded to Amazon S3. Configure the function to write the resulting transcriptions to the transcription S3 bucket.

Amazon MLS-C01 Practice Test 6

Amazon MLS-C01 Practice Tests