Newest Amazon MLS-C01 Practice Questions at Zero Cost

Get a glimpse of the real MLS-C01 certification exam challenges with our free Amazon MLS-C01 practice test questions.

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.)

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?

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

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

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

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

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?

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

BLoad 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.

CReduce 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

DReduce 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.

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 cluster

Which solution meets these requirements?

ASet 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.

BSet 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.

CSet 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.

DUse 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

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?

ALoad 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.

BUse 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.

CUse 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.

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

Master the AWS Certified Machine Learning - Specialty MLS-C01 exam like never before! You’ve reviewed the free MLS-C01 practice questions, but the actual AWS Certified Machine Learning certification exam demands more. Elevate your preparation with Certsmarket premium AWS Certified Machine Learning MLS-C01 practice exam questions.

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