Quiz IT certification exam

Question 1

A data scientist is building a linear regression model. The scientist inspects the dataset and notices that the mode of the distribution is lower than the median, and the median is lower than the mean.

Which data transformation will give the data scientist the ability to apply a linear regression model?

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Accepted Answer

Logarithmic transformation

Answer

Exponential transformation

Answer

Polynomial transformation

Answer

Sinusoidal transformation

Question 2

A company is planning a marketing campaign to promote a new product to existing customers. The company has data (or past promotions that are similar. The company decides to try an experiment to send a more expensive marketing package to a smaller number of customers. The company wants to target the marketing campaign to customers who are most likely to buy the new product. The experiment requires that at least 90% of the customers who are likely to purchase the new product receive the marketing materials....company trains a model by using the linear learner algorithm in Amazon SageMaker. The model has a recall score of 80% and a precision of 75%....should the company retrain the model to meet these requirements?

Accepted Answer

Set the target_recall hyperparameter to 90% Set the binaryclassrfier model_selection_critena hyperparameter to recall_at_target_precision.

Answer

Set the targetprecision hyperparameter to 90%. Set the binary classifier model selection criteria hyperparameter to precision at_jarget recall.

Answer

Use 90% of the historical data for training Set the number of epochs to 20.

Answer

Set the normalize_jabel hyperparameter to true. Set the number of classes to 2.

Question 3

A data scientist receives a collection of insurance claim records. Each record includes a claim ID. the final outcome of the insurance claim, and the date of the final outcome.The final outcome of each claim is a selection from among 200 outcome categories. Some claim records include only partial information. However, incomplete claim records include only 3 or 4 outcome ...gones from among the 200 available outcome categories. The collection includes hundreds of records for each outcome category. The records are from the previous 3 years.The data scientist must create a solution to predict the number of claims that will be in each outcome category every month, several months in advance.Which solution will meet these requirements?

Accepted Answer

Perform forecasting by using claim IDs and dates to identify the expected number ot claims in each outcome category every month.

Answer

Perform classification every month by using supervised learning of the 20X3 outcome categories based on claim contents.

Answer

Perform reinforcement learning by using claim IDs and dates Instruct the insurance agents who submit the claim records to estimate the expected number of claims in each outcome category every month

Answer

Perform classification by using supervised learning of the outcome categories for which partial information on claim contents is provided. Perform forecasting by using claim IDs and dates for all other outcome categories.

Question 4

A retail company stores 100 GB of daily transactional data in Amazon S3 at periodic intervals. The company wants to identify the schema of the transactional data. The company also wants to perform transformations on the transactional data that is in Amazon S3.The company wants to use a machine learning (ML) approach to detect fraud in the transformed data.Which combination of solutions will meet these requirements with the LEAST operational overhead? {Select THREE.)

Accepted Answer

Use AWS Glue crawlers to scan the data and identify the schema.

Accepted Answer

Use AWS Glue workflows and AWS Glue jobs to perform data transformations.

Accepted Answer

Use Amazon Fraud Detector to train a model to detect fraud.

Answer

Use Amazon Athena to scan the data and identify the schema.

Answer

Use Amazon Redshift to store procedures to perform data transformations

Answer

Use Amazon Redshift ML to train a model to detect fraud.

Question 5

A company's machine learning (ML) specialist is building a computer vision model to classify 10 different traffic signs. The company has stored 100 images of each class in Amazon S3, and the company has another 10.000 unlabeled images. All the images come from dash cameras and are a size of 224 pixels * 224 pixels. After several training runs, the model is overfitting on the training data.Which actions should the ML specialist take to address this problem? (Select TWO.)

Accepted Answer

Use data augmentation to rotate and translate the labeled images.

Accepted Answer

Use the Amazon SageMaker k-nearest neighbors (k-NN) algorithm to label the unlabeled images.

Answer

Use Amazon SageMaker Ground Truth to label the unlabeled images

Answer

Use image preprocessing to transform the images into grayscale images.

Answer

Replace the activation of the last layer with a sigmoid.

Question 6

A machine learning (ML) specialist is using the Amazon SageMaker DeepAR forecasting algorithm to train a model on CPU-based Amazon EC2 On-Demand instances. The model currently takes multiple hours to train. The ML specialist wants to decrease the training time of the model.Which approaches will meet this requirement7 (SELECT TWO )

Accepted Answer

Replace CPU-based EC2 instances with GPU-based EC2 instances.

Accepted Answer

Use multiple training instances.

Answer

Replace On-Demand Instances with Spot Instances

Answer

Configure model auto scaling dynamically to adjust the number of instances automatically.

Answer

Use a pre-trained version of the model. Run incremental training.

Question 7

A data scientist obtains a tabular dataset that contains 150 correlated features with different ranges to build a regression model. The data scientist needs to achieve more efficient model training by implementing a solution that minimizes impact on the model's performance. The data scientist decides to perform a principal component analysis (PCA) preprocessing step to reduce the number of features to a smaller set of independent features before the data scientist uses the new features in the regression model.Which preprocessing step will meet these requirements?

Accepted Answer

Load the data into Amazon SageMaker Data Wrangler. Scale the data with a Min Max Scaler transformation step Use the SageMaker built-in algorithm for PCA on the scaled dataset to transform the data.

Answer

Use the Amazon SageMaker built-in algorithm for PCA on the dataset to transform the data

Answer

Reduce the dimensionality of the dataset by removing the features that have the highest correlation Load the data into Amazon SageMaker Data Wrangler Perform a Standard Scaler transformation step to scale the data Use the SageMaker built-in algorithm for PCA on the scaled dataset to transform the data

Answer

Reduce the dimensionality of the dataset by removing the features that have the lowest correlation. Load the data into Amazon SageMaker Data Wrangler. Perform a Min Max Scaler transformation step to scale the data. Use the SageMaker built-in algorithm for PCA on the scaled dataset to transform the data.

Question 8

A financial services company wants to automate its loan approval process by building a machine learning (ML) model. Each loan data point contains credit history from a third-party data source and demographic information about the customer. Each loan approval prediction must come with a report that contains an explanation for why the customer was approved for a loan or was denied for a loan. The company will use Amazon SageMaker to build the model.Which solution will meet these requirements with the LEAST development effort?

Accepted Answer

Use SageMaker Clarify to generate the explanation report. Attach the report to the predicted results.

Answer

Use SageMaker Model Debugger to automatically debug the predictions, generate the explanation, and attach the explanation report.

Answer

Use AWS Lambda to provide feature importance and partial dependence plots. Use the plots to generate and attach the explanation report.

Answer

Use custom Amazon Cloud Watch metrics to generate the explanation report. Attach the report to the predicted results.

Question 9

An obtain relator collects the following data on customer orders: demographics, behaviors, location, shipment progress, and delivery time. A data scientist joins all the collected datasets. The result is a single dataset that includes 980 variables.The data scientist must develop a machine learning (ML) model to identify groups of customers who are likely to respond to a marketing campaign.Which combination of algorithms should the data scientist use to meet this requirement? (Select TWO.)

Accepted Answer

K-means

Accepted Answer

Principal component analysis (PCA)

Answer

Latent Dirichlet Allocation (LDA)

Answer

Se mantic feg mentation

Answer

Factorization machines (FM)

Question 10

A machine learning (ML) developer for an online retailer recently uploaded a sales dataset into Amazon SageMaker Studio. The ML developer wants to obtain importance scores for each feature of the dataset. The ML developer will use the importance scores to feature engineer the dataset.Which solution will meet this requirement with the LEAST development effort?

Accepted Answer

Use SageMaker Data Wrangler to perform a Gini importance score analysis.

Answer

Use a SageMaker notebook instance to perform principal component analysis (PCA).

Answer

Use a SageMaker notebook instance to perform a singular value decomposition analysis.

Answer

Use the multicollinearity feature to perform a lasso feature selection to perform an importance scores analysis.

Question 11

A machine learning engineer is building a bird classification model. The engineer randomly separates a dataset into a training dataset and a validation dataset. During the training phase, the model achieves very high accuracy. However, the model did not generalize well during validation of the validation dataset. The engineer realizes that the original dataset was imbalanced.What should the engineer do to improve the validation accuracy of the model?

Accepted Answer

Perform stratified sampling on the original dataset.

Answer

Acquire additional data about the majority classes in the original dataset.

Answer

Use a smaller, randomly sampled version of the training dataset.

Answer

Perform systematic sampling on the original dataset.

Question 12

A data scientist is trying to improve the accuracy of a neural network classification model. The data scientist wants to run a large hyperparameter tuning job in Amazon SageMaker.However, previous smaller tuning jobs on the same model often ran for several weeks. The ML specialist wants to reduce the computation time required to run the tuning job.Which actions will MOST reduce the computation time for the hyperparameter tuning job? (Select TWO.)

Accepted Answer

Use the Hyperband tuning strategy.

Accepted Answer

Set a lower value for the MaxNumberOfTrainingJobs parameter.

Answer

Increase the number of hyperparameters.

Answer

Use the grid search tuning strategy

Answer

Set a lower value for the MaxParallelTrainingJobs parameter.

Question 13

A company is setting up a mechanism for data scientists and engineers from different departments to access an Amazon SageMaker Studio domain. Each department has a unique SageMaker Studio domain.The company wants to build a central proxy application that data scientists and engineers can log in to by using their corporate credentials. The proxy application will authenticate users by using the company's existing Identity provider (IdP). The application will then route users to the appropriate SageMaker Studio domain.The company plans to maintain a table in Amazon DynamoDB that contains SageMaker domains for each department.How should the company meet these requirements?

Accepted Answer

Use the SageMaker CreatePresignedDomainUrl API to generate a presigned URL for each domain according to the DynamoDB table. Pass the presigned URL to the proxy application.

Answer

Use the SageMaker CreateHuman TaskUi API to generate a UI URL. Pass the URL to the proxy application.

Answer

Use the Amazon SageMaker ListHumanTaskUis API to list all UI URLs. Pass the appropriate URL to the DynamoDB table so that the proxy application can use the URL.

Answer

Use the SageMaker CreatePresignedNotebookInstanceUrl API to generate a presigned URL. Pass the presigned URL to the proxy application.

Question 14

A global bank requires a solution to predict whether customers will leave the bank and choose another bank. The bank is using a dataset to train a model to predict customer loss. The training dataset has 1,000 rows. The training dataset includes 100 instances of customers who left the bank.A machine learning (ML) specialist is using Amazon SageMaker Data Wrangler to train a churn prediction model by using a SageMaker training job. After training, the ML specialist notices that the model returns only false results. The ML specialist must correct the model so that it returns more accurate predictions.Which solution will meet these requirements?

Accepted Answer

Apply Synthetic Minority Oversampling Technique (SMOTE) to the training dataset before training.

Answer

Apply anomaly detection to remove outliers from the training dataset before training.

Answer

Apply normalization to the features of the training dataset before training.

Answer

Apply undersampling to the training dataset before training.

Question 15

A developer at a retail company is creating a daily demand forecasting model. The company stores the historical hourly demand data in an Amazon S3 bucket. However, the historical data does not include demand data for some hours.The developer wants to verify that an autoregressive integrated moving average (ARIMA) approach will be a suitable model for the use case.How should the developer verify the suitability of an ARIMA approach?

Accepted Answer

Use Amazon SageMaker Data Wrangler. Import the data from Amazon S3. Impute hourly missing data. Perform a Seasonal Trend decomposition.

Answer

Use Amazon SageMaker Autopilot. Create a new experiment that specifies the S3 data location. Choose ARIMA as the machine learning (ML) problem. Check the model performance.

Answer

Use Amazon SageMaker Data Wrangler. Import the data from Amazon S3. Resample data by using the aggregate daily total. Perform a Seasonal Trend decomposition.

Answer

Use Amazon SageMaker Autopilot. Create a new experiment that specifies the S3 data location. Impute missing hourly values. Choose ARIMA as the machine learning (ML) problem. Check the model performance.

Question 16

A law firm handles thousands of contracts every day. Every contract must be signed. Currently, a lawyer manually checks all contracts for signatures.The law firm is developing a machine learning (ML) solution to automate signature detection for each contract. The ML solution must also provide a confidence score for each contract page.Which Amazon Textract API action can the law firm use to generate a confidence score for each page of each contract?

Accepted Answer

Use the AnalyzeDocument API action. Set the FeatureTypes parameter to SIGNATURES. Return the confidence scores for each page.

Answer

Use the Prediction API call on the documents. Return the signatures and confidence scores for each page.

Answer

Use the StartDocumentAnalysis API action to detect the signatures. Return the confidence scores for each page.

Answer

Use the GetDocumentAnalysis API action to detect the signatures. Return the confidence scores for each page

Question 17

An ecommerce company has developed a XGBoost model in Amazon SageMaker to predict whether a customer will return a purchased item. The dataset is imbalanced. Only 5% of customers return itemsA data scientist must find the hyperparameters to capture as many instances of returned items as possible. The company has a small budget for compute.How should the data scientist meet these requirements MOST cost-effectively?

Accepted Answer

Tune the csv_weight hyperparameter and the scale_pos_weight hyperparameter by using automatic model tuning (AMT). Optimize on {'HyperParameterTuningJobObjective': {'MetricName': 'validation:f1', 'Type': 'Maximize'}}.

Answer

Tune all possible hyperparameters by using automatic model tuning (AMT). Optimize on {'HyperParameterTuningJobObjective': {'MetricName': 'validation:accuracy', 'Type': 'Maximize'}}

Answer

Tune all possible hyperparameters by using automatic model tuning (AMT). Optimize on {'HyperParameterTuningJobObjective': {'MetricName': 'validation:f1', 'Type': 'Maximize'}}.

Answer

Tune the csv_weight hyperparameter and the scale_pos_weight hyperparameter by using automatic model tuning (AMT). Optimize on {'HyperParameterTuningJobObjective': {'MetricName': 'validation:f1', 'Type': 'Minimize'}).

Question 18

A data scientist uses Amazon SageMaker Data Wrangler to define and perform transformations and feature engineering on historical data. The data scientist saves the transformations to SageMaker Feature Store.The historical data is periodically uploaded to an Amazon S3 bucket. The data scientist needs to transform the new historic data and add it to the online feature store The data scientist needs to prepare the .....historic data for training and inference by using native integrations.Which solution will meet these requirements with the LEAST development effort?

Accepted Answer

Configure Amazon EventBridge to run a predefined SageMaker pipeline to perform the transformations when a new data is detected in the S3 bucket.

Answer

Use AWS Lambda to run a predefined SageMaker pipeline to perform the transformations on each new dataset that arrives in the S3 bucket.

Answer

Run an AWS Step Functions step and a predefined SageMaker pipeline to perform the transformations on each new dalaset that arrives in the S3 bucket

Answer

Use Apache Airflow to orchestrate a set of predefined transformations on each new dataset that arrives in the S3 bucket.

Question 19

A data scientist at a financial services company used Amazon SageMaker to train and deploy a model that predicts loan defaults. The model analyzes new loan applications and predicts the risk of loan default. To train the model, the data scientist manually extracted loan data from a database. The data scientist performed the model training and deployment steps in a Jupyter notebook that is hosted on SageMaker Studio notebooks. The model's prediction accuracy is decreasing over time. Which combination of slept in the MOST operationally efficient way for the data scientist to maintain the model's accuracy? (Select TWO.)

Accepted Answer

Use SageMaker Pipelines to create an automated workflow that extracts fresh data, trains the model, and deploys a new version of the model.

Accepted Answer

Configure SageMaker Model Monitor with an accuracy threshold to check for model drift. Initiate an Amazon CloudWatch alarm when the threshold is exceeded. Connect the workflow in SageMaker Pipelines with the CloudWatch alarm to automatically initiate retraining.

Answer

Store the model predictions in Amazon S3 Create a daily SageMaker Processing job that reads the predictions from Amazon S3, checks for changes in model prediction accuracy, and sends an email notification if a significant change is detected.

Answer

Rerun the steps in the Jupyter notebook that is hosted on SageMaker Studio notebooks to retrain the model and redeploy a new version of the model.

Answer

Export the training and deployment code from the SageMaker Studio notebooks into a Python script. Package the script into an Amazon Elastic Container Service (Amazon ECS) task that an AWS Lambda function can initiate.

Question 20

An insurance company developed a new experimental machine learning (ML) model to replace an existing model that is in production. The company must validate the quality of predictions from the new experimental model in a production environment before the company uses the new experimental model to serve general user requests.Which one model can serve user requests at a time. The company must measure the performance of the new experimental model without affecting the current live trafficWhich solution will meet these requirements?

Accepted Answer

Shadow deployment

Answer

A/B testing

Answer

Canary release

Answer

Blue/green deployment

Question 21

An ecommerce company wants to use machine learning (ML) to monitor fraudulent transactions on its website. The company is using Amazon SageMaker to research, train, deploy, and monitor the ML models.The historical transactions data is in a .csv file that is stored in Amazon S3 The data contains features such as the user's IP address, navigation time, average time on each page, and the number of clicks for ....session. There is no label in the data to indicate if a transaction is anomalous.Which models should the company use in combination to detect anomalous transactions? (Select TWO.)

Accepted Answer

Random Cut Forest (RCF)

Accepted Answer

XGBoost

Answer

IP Insights

Answer

K-nearest neighbors (k-NN)

Answer

Linear learner with a logistic function

Question 22

A finance company needs to forecast the price of a commodity. The company has compiled a dataset of historical daily prices. A data scientist must train various forecasting models on 80% of the dataset and must validate the efficacy of those models on the remaining 20% of the dataset.What should the data scientist split the dataset into a training dataset and a validation dataset to compare model performance?

Accepted Answer

Pick a date so that 80% to the data points precede the date Assign that group of data points as the training dataset. Assign all the remaining data points to the validation dataset.

Answer

Pick a date so that 80% of the data points occur after the date. Assign that group of data points as the training dataset. Assign all the remaining data points to the validation dataset.

Answer

Starting from the earliest date in the dataset. pick eight data points for the training dataset and two data points for the validation dataset. Repeat this stratified sampling until no data points remain.

Answer

Sample data points randomly without replacement so that 80% of the data points are in the training dataset. Assign all the remaining data points to the validation dataset.

Question 23

An engraving company wants to automate its quality control process for plaques. The company performs the process before mailing each customized plaque to a customer. The company has created an Amazon S3 bucket that contains images of defects that should cause a plaque to be rejected. Low-confidence predictions must be sent to an internal team of reviewers who are using Amazon Augmented Al (Amazon A2I).Which solution will meet these requirements?

Accepted Answer

Use Amazon Rekognition for automatic processing. Use Amazon A2I with a private workforce option for manual review.

Answer

Use Amazon Textract for automatic processing. Use Amazon A2I with Amazon Mechanical Turk for manual review.

Answer

Use Amazon Transcribe for automatic processing. Use Amazon A2I with a private workforce option for manual review.

Answer

Use AWS Panorama for automatic processing Use Amazon A2I with Amazon Mechanical Turk for manual review

Question 24

An online delivery company wants to choose the fastest courier for each delivery at the moment an order is placed. The company wants to implement this feature for existing users and new users of its application. Data scientists have trained separate models with XGBoost for this purpose, and the models are stored in Amazon S3. There is one model fof each city where the company operates.The engineers are hosting these models in Amazon EC2 for responding to the web client requests, with one instance for each model, but the instances have only a 5% utilization in CPU and memory, ....operation engineers want to avoid managing unnecessary resources.Which solution will enable the company to achieve its goal with the LEAST operational overhead?

Accepted Answer

Prepare an Amazon SageMaker Docker container based on the open-source multi-model server. Remove the existing instances and create a multi-model endpoint in SageMaker instead, pointing to the S3 bucket containing all the models Invoke the endpoint from the web client at runtime, specifying the TargetModel parameter according to the city of each request.

Answer

Create an Amazon SageMaker notebook instance for pulling all the models from Amazon S3 using the boto3 library. Remove the existing instances and use the notebook to perform a SageMaker batch transform for performing inferences offline for all the possible users in all the cities. Store the results in different files in Amazon S3. Point the web client to the files.

Answer

Keep only a single EC2 instance for hosting all the models. Install a model server in the instance and load each model by pulling it from Amazon S3. Integrate the instance with the web client using Amazon API Gateway for responding to the requests in real time, specifying the target resource according to the city of each request.

Answer

Prepare a Docker container based on the prebuilt images in Amazon SageMaker. Replace the existing instances with separate SageMaker endpoints. one for each city where the company operates. Invoke the endpoints from the web client, specifying the URL and EndpomtName parameter according to the city of each request.

Question 25

A company builds computer-vision models that use deep learning for the autonomous vehicle industry. A machine learning (ML) specialist uses an Amazon EC2 instance that has a CPU: GPU ratio of 12:1 to train the models.The ML specialist examines the instance metric logs and notices that the GPU is idle half of the time The ML specialist must reduce training costs without increasing the duration of the training jobs.Which solution will meet these requirements?

Accepted Answer

Switch to an instance type that has a CPU GPU ratio of 6:1.

Answer

Switch to an instance type that has only CPUs.

Answer

Use a heterogeneous cluster that has two different instances groups.

Answer

Use memory-optimized EC2 Spot Instances for the training jobs.

Question 26

A company is building a new supervised classification model in an AWS environment. The company's data science team notices that the dataset has a large quantity of variables Ail the variables are numeric. The model accuracy for training and validation is low. The model's processing time is affected by high latency The data science team needs to increase the accuracy of the model and decrease the processing.How it should the data science team do to meet these requirements?

Accepted Answer

Use a principal component analysis (PCA) model.

Answer

Create new features and interaction variables.

Answer

Apply normalization on the feature set.

Answer

Use a multiple correspondence analysis (MCA) model

Question 27

A company wants to forecast the daily price of newly launched products based on 3 years of data for older product prices, sales, and rebates. The time-series data has irregular timestamps and is missing some values.Data scientist must build a dataset to replace the missing values. The data scientist needs a solution that resamptes the data daily and exports the data for further modeling.Which solution will meet these requirements with the LEAST implementation effort?

Accepted Answer

Use Amazon SageMaker Studio Data Wrangler.

Answer

Use Amazon EMR Serveriess with PySpark.

Answer

Use AWS Glue DataBrew.

Answer

Use Amazon SageMaker Studio Notebook with Pandas.

Question 28

A data scientist is building a forecasting model for a retail company by using the most recent 5 years of sales records that are stored in a data warehouse. The dataset contains sales records for each of the company's stores across five commercial regions The data scientist creates a working dataset with StorelD. Region. Date, and Sales Amount as columns. The data scientist wants to analyze yearly average sales for each region. The scientist also wants to compare how each region performed compared to average sales across all commercial regions.Which visualization will help the data scientist better understand the data trend?

Accepted Answer

Create an aggregated dataset by using the Pandas GroupBy function to get average sales for each year for each region Create a bar plot, faceted by year, of average sales for each region Add a horizontal line in each facet to represent average sales.

Answer

Create an aggregated dataset by using the Pandas GroupBy function to get average sales for each year for each store. Create a bar plot, faceted by year, of average sales for each store. Add an extra bar in each facet to represent average sales.

Answer

Create an aggregated dataset by using the Pandas GroupBy function to get average sales for each year for each store. Create a bar plot, colored by region and faceted by year, of average sales for each store. Add a horizontal line in each facet to represent average sales.

Answer

Create an aggregated dataset by using the Pandas GroupBy function to get average sales for each year for each region Create a bar plot of average sales for each region. Add an extra bar in each facet to represent average sales.

Question 29

An ecommerce company is automating the categorization of its products based on images. A data scientist has trained a computer vision model using the Amazon SageMaker image classification algorithm. The images for each product are classified according to specific product lines. The accuracy of the model is too low when categorizing new products. All of the product images have the same dimensions and are stored within an Amazon S3 bucket. The company wants to improve the model so it can be used for new products as soon as possible.Which steps would improve the accuracy of the solution? (Choose three.)

Accepted Answer

Augment the images in the dataset. Use open-source libraries to crop, resize, flip, rotate, and adjust the brightness and contrast of the images.

Accepted Answer

Use Amazon Rekognition Custom Labels to train a new model.

Accepted Answer

Check whether there are class imbalances in the product categories, and apply oversampling or undersampling as required. Store the new dataset in Amazon S3.

Answer

Use the SageMaker semantic segmentation algorithm to train a new model to achieve improved accuracy.

Answer

Use the Amazon Rekognition DetectLabels API to classify the products in the dataset.

Answer

Use a SageMaker notebook to implement the normalization of pixels and scaling of the images. Store the new dataset in Amazon S3.

Question 30

A data scientist is training a text classification model by using the Amazon SageMaker built-in BlazingText algorithm. There are 5 classes in the dataset, with 300 samples for category A, 292 samples for category B, 240 samples for category C, 258 samples for category D, and 310 samples for category E.The data scientist shuffles the data and splits off 10% for testing. After training the model, the data scientist generates confusion matrices for the training and test sets. Amazon MLS-C01 image Question 270 4834 09162024005655000000

Amazon MLS-C01 image Question 270 4834 09162024005655000000

What could the data scientist conclude form these results?

Accepted Answer

The model is overfitting for classes B and E.

Answer

Classes C and D are too similar.

Answer

The dataset is too small for holdout cross-validation.

Answer

The data distribution is skewed.

Question 31

A company that manufactures mobile devices wants to determine and calibrate the appropriate sales price for its devices. The company is collecting the relevant data and is determining data features that it can use to train machine learning (ML) models. There are more than 1,000 features, and the company wants to determine the primary features that contribute to the sales price.Which techniques should the company use for feature selection? (Choose three.)

Accepted Answer

Correlation plot with heat maps

Accepted Answer

Univariate selection

Accepted Answer

Feature importance with a tree-based classifier

Answer

Data scaling with standardization and normalization

Answer

Data binning

Answer

Data augmentation

Question 32

A company uses sensors on devices such as motor engines and factory machines to measure parameters, temperature and pressure. The company wants to use the sensor data to predict equipment malfunctions and reduce services outages.The Machine learning (ML) specialist needs to gather the sensors data to train a model to predict device malfunctions The ML spoctafst must ensure that the data does not contain outliers before training the ..el.What can the ML specialist meet these requirements with the LEAST operational overhead?

Accepted Answer

Use an Amazon SageMaker Data Wrangler anomaly detection visualization to find outliers in the dataset. Add a transformation to a Data Wrangler data flow to remove outliers.

Answer

Load the data into an Amazon SagcMaker Studio notebook. Calculate the first and third quartile Use a SageMaker Data Wrangler data (low to remove only values that are outside of those quartiles.

Answer

Use an Amazon SageMaker Data Wrangler bias report to find outliers in the dataset Use a Data Wrangler data flow to remove outliers based on the bias report.

Answer

Use Amazon Lookout for Equipment to find and remove outliers from the dataset.

Question 33

A data engineer needs to provide a team of data scientists with the appropriate dataset to run machine learning training jobs. The data will be stored in Amazon S3. The data engineer is obtaining the data from an Amazon Redshift database and is using join queries to extract a single tabular dataset. A portion of the schema is as follows:...traction Timestamp (Timeslamp)...JName(Varchar)...JNo (Varchar)Th data engineer must provide the data so that any row with a CardNo value of NULL is removed. Also, the TransactionTimestamp column must be separated into a TransactionDate column and a isactionTime column Finally, the CardName column must be renamed to NameOnCard.The data will be extracted on a monthly basis and will be loaded into an S3 bucket. The solution must minimize the effort that is needed to set up infrastructure for the ingestion and transformation. The solution must be automated and must minimize the load on the Amazon Redshift clusterWhich solution meets these requirements?

Accepted Answer

Set up an AWS Glue job that has the Amazon Redshift cluster as the source and the S3 bucket as the destination Use the built-in transforms Filter, Map. and RenameField to perform the required transformations. Schedule the job to run monthly.

Answer

Set up an Amazon EMR cluster Create an Apache Spark job to read the data from the Amazon Redshift cluster and transform the data. Load the data into the S3 bucket. Schedule the job to run monthly.

Answer

Set up an Amazon EC2 instance with a SQL client tool, such as SQL Workbench/J. to query the data from the Amazon Redshift cluster directly. Export the resulting dataset into a We. Upload the file into the S3 bucket. Perform these tasks monthly.

Answer

Use Amazon Redshift Spectrum to run a query that writes the data directly to the S3 bucket. Create an AWS Lambda function to run the query monthly

Question 34

A manufacturing company needs to identify returned smartphones that have been damaged by moisture. The company has an automated process that produces 2.000 diagnostic values for each phone. The database contains more than five million phone evaluations. The evaluation process is consistent, and there are no missing values in the data. A machine learning (ML) specialist has trained an Amazon SageMaker linear learner ML model to classify phones as moisture damaged or not moisture damaged by using all available features. The model's F1 score is 0.6.What changes in model training would MOST likely improve the model's F1 score? (Select TWO.)

Accepted Answer

Continue to use the SageMaker linear learner algorithm. Reduce the number of features with the SageMaker principal component analysis (PCA) algorithm.

Accepted Answer

Use the SageMaker k-nearest neighbors (k-NN) algorithm. Set a dimension reduction target of less than 1,000 to train the model.

Answer

Continue to use the SageMaker linear learner algorithm. Reduce the number of features with the scikit-iearn multi-dimensional scaling (MDS) algorithm.

Answer

Continue to use the SageMaker linear learner algorithm. Set the predictor type to regressor.

Answer

Use the SageMaker k-means algorithm with k of less than 1.000 to train the model

Question 35

A company deployed a machine learning (ML) model on the company website to predict real estate prices. Several months after deployment, an ML engineer notices that the accuracy of the model has gradually decreased.The ML engineer needs to improve the accuracy of the model. The engineer also needs to receive notifications for any future performance issues.Which solution will meet these requirements?

Accepted Answer

Perform incremental training to update the model. Activate Amazon SageMaker Model Monitor to detect model performance issues and to send notifications.

Answer

Use Amazon SageMaker Model Governance. Configure Model Governance to automatically adjust model hyper para meters. Create a performance threshold alarm in Amazon CloudWatch to send notifications.

Answer

Use Amazon SageMaker Debugger with appropriate thresholds. Configure Debugger to send Amazon CloudWatch alarms to alert the team Retrain the model by using only data from the previous several months.

Answer

Use only data from the previous several months to perform incremental training to update the model. Use Amazon SageMaker Model Monitor to detect model performance issues and to send notifications.

Question 36

A university wants to develop a targeted recruitment strategy to increase new student enrollment. A data scientist gathers information about the academic performance history of students. The data scientist wants to use the data to build student profiles. The university will use the profiles to direct resources to recruit students who are likely to enroll in the university.Which combination of steps should the data scientist take to predict whether a particular student applicant is likely to enroll in the university? (Select TWO)

Accepted Answer

Use Amazon SageMaker Ground Truth to sort the data into two groups named 'enrolled' or 'not enrolled.'

Accepted Answer

Use a classification algorithm to run predictions

Answer

Use a forecasting algorithm to run predictions.

Answer

Use a regression algorithm to run predictions.

Answer

Use the built-in Amazon SageMaker k-means algorithm to cluster the data into two groups named 'enrolled' or 'not enrolled.'

Question 37

A real-estate company is launching a new product that predicts the prices of new houses. The historical data for the properties and prices is stored in .csv format in an Amazon S3 bucket. The data has a header, some categorical fields, and some missing values. The company's data scientists have used Python with a common open-source library to fill the missing values with zeros. The data scientists have dropped all of the categorical fields and have trained a model by using the open-source linear regression algorithm with the default parameters.The accuracy of the predictions with the current model is below 50%. The company wants to improve the model performance and launch the new product as soon as possible.Which solution will meet these requirements with the LEAST operational overhead?

Accepted Answer

Create an IAM role for Amazon SageMaker with access to the S3 bucket. Create a SageMaker AutoML job with SageMaker Autopilot pointing to the bucket with the dataset. Specify the price as the target attribute. Wait for the job to complete. Deploy the best model for predictions.

Answer

Create a service-linked role for Amazon Elastic Container Service (Amazon ECS) with access to the S3 bucket. Create an ECS cluster that is based on an AWS Deep Learning Containers image. Write the code to perform the feature engineering. Train a logistic regression model for predicting the price, pointing to the bucket with the dataset. Wait for the training job to complete. Perform the inferences.

Answer

Create an Amazon SageMaker notebook with a new IAM role that is associated with the notebook. Pull the dataset from the S3 bucket. Explore different combinations of feature engineering transformations, regression algorithms, and hyperparameters. Compare all the results in the notebook, and deploy the most accurate configuration in an endpoint for predictions.

Answer

Create an IAM role with access to Amazon S3, Amazon SageMaker, and AWS Lambda. Create a training job with the SageMaker built-in XGBoost model pointing to the bucket with the dataset. Specify the price as the target feature. Wait for the job to complete. Load the model artifact to a Lambda function for inference on prices of new houses.

Question 38

A data scientist is training a large PyTorch model by using Amazon SageMaker. It takes 10 hours on average to train the model on GPU instances. The data scientist suspects that training is not converging and that resource utilization is not optimal.What should the data scientist do to identify and address training issues with the LEAST development effort?

Accepted Answer

Use the SageMaker Debugger vanishing_gradient and LowGPUUtilization built-in rules to detect issues and to launch the StopTrainingJob action if issues are detected.

Answer

Use CPU utilization metrics that are captured in Amazon CloudWatch. Configure a CloudWatch alarm to stop the training job early if low CPU utilization occurs.

Answer

Use high-resolution custom metrics that are captured in Amazon CloudWatch. Configure an AWS Lambda function to analyze the metrics and to stop the training job early if issues are detected.

Answer

Use the SageMaker Debugger confusion and feature_importance_overweight built-in rules to detect issues and to launch the StopTrainingJob action if issues are detected.

Question 39

A company needs to deploy a chatbot to answer common questions from customers. The chatbot must base its answers on company documentation.Which solution will meet these requirements with the LEAST development effort?

Accepted Answer

Index company documents by using Amazon Kendra. Integrate the chatbot with Amazon Kendra by using the Amazon Kendra Query API operation to answer customer questions.

Answer

Train a Bidirectional Attention Flow (BiDAF) network based on past customer questions and company documents. Deploy the model as a real-time Amazon SageMaker endpoint. Integrate the model with the chatbot by using the SageMaker Runtime InvokeEndpoint API operation to answer customer questions.

Answer

Train an Amazon SageMaker BlazingText model based on past customer questions and company documents. Deploy the model as a real-time SageMaker endpoint. Integrate the model with the chatbot by using the SageMaker Runtime InvokeEndpoint API operation to answer customer questions.

Answer

Index company documents by using Amazon OpenSearch Service. Integrate the chatbot with OpenSearch Service by using the OpenSearch Service k-nearest neighbors (k-NN) Query API operation to answer customer questions.

Question 40

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).

Amazon MLS-C01 Practice Test 7

Amazon MLS-C01 Practice Tests