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MLS-C01 Practice Questions

AWS Certified Machine Learning - Specialty

Last Update 2 months ago
Total Questions : 330

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Our free AWS Certified Specialty practice questions crafted to reflect the domains and difficulty of the actual exam. The detailed rationales explain the 'why' behind each answer, reinforcing key concepts about MLS-C01. Use this test to pinpoint which areas you need to focus your study on.

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Question # 71

A Data Scientist is working on an application that performs sentiment analysis. The validation accuracy is poor and the Data Scientist thinks that the cause may be a rich vocabulary and a low average frequency of words in the dataset

Which tool should be used to improve the validation accuracy?

Options:

A.  

Amazon Comprehend syntax analysts and entity detection

B.  

Amazon SageMaker BlazingText allow mode

C.  

Natural Language Toolkit (NLTK) stemming and stop word removal

D.  

Scikit-learn term frequency-inverse document frequency (TF-IDF) vectorizers

Discussion 0
Question # 72

A company uses camera images of the tops of items displayed on store shelves to determine which items

were removed and which ones still remain. After several hours of data labeling, the company has a total of

1,000 hand-labeled images covering 10 distinct items. The training results were poor.

Which machine learning approach fulfills the company’s long-term needs?

Options:

A.  

Convert the images to grayscale and retrain the model

B.  

Reduce the number of distinct items from 10 to 2, build the model, and iterate

C.  

Attach different colored labels to each item, take the images again, and build the model

D.  

Augment training data for each item using image variants like inversions and translations, build the model, and iterate.

Discussion 0
Question # 73

A company offers an online shopping service to its customers. The company wants to enhance the site’s security by requesting additional information when customers access the site from locations that are different from their normal location. The company wants to update the process to call a machine learning (ML) model to determine when additional information should be requested.

The company has several terabytes of data from its existing ecommerce web servers containing the source IP addresses for each request made to the web server. For authenticated requests, the records also contain the login name of the requesting user.

Which approach should an ML specialist take to implement the new security feature in the web application?

Options:

A.  

Use Amazon SageMaker Ground Truth to label each record as either a successful or failed access attempt. Use Amazon SageMaker to train a binary classification model using the factorization machines (FM) algorithm.

B.  

Use Amazon SageMaker to train a model using the IP Insights algorithm. Schedule updates and retraining of the model using new log data nightly.

C.  

Use Amazon SageMaker Ground Truth to label each record as either a successful or failed access attempt. Use Amazon SageMaker to train a binary classification model using the IP Insights algorithm.

D.  

Use Amazon SageMaker to train a model using the Object2Vec algorithm. Schedule updates and retraining of the model using new log data nightly.

Discussion 0
Question # 74

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

Which strategy will allow the data scientist to identify fraudulent accounts?

Options:

A.  

Execute the built-in FindDuplicates Amazon Athena query.

B.  

Create a FindMatches machine learning transform in AWS Glue.

C.  

Create an AWS Glue crawler to infer duplicate accounts in the source data.

D.  

Search for duplicate accounts in the AWS Glue Data Catalog.

Discussion 0
Question # 75

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?

Options:

A.  

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

B.  

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

C.  

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

D.  

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

Discussion 0
Question # 76

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?

Options:

A.  

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

B.  

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

C.  

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

D.  

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

Discussion 0
Question # 77

A Machine Learning Specialist is working with a large cybersecurily company that manages security events in real time for companies around the world The cybersecurity company wants to design a solution that will allow it to use machine learning to score malicious events as anomalies on the data as it is being ingested The company also wants be able to save the results in its data lake for later processing and analysis

What is the MOST efficient way to accomplish these tasks'?

Options:

A.  

Ingest the data using Amazon Kinesis Data Firehose, and use Amazon Kinesis Data Analytics Random Cut Forest (RCF) for anomaly detection Then use Kinesis Data Firehose to stream the results to Amazon S3

B.  

Ingest the data into Apache Spark Streaming using Amazon EMR. and use Spark MLlib with k-means to perform anomaly detection Then store the results in an Apache Hadoop Distributed File System (HDFS) using Amazon EMR with a replication factor of three as the data lake

C.  

Ingest the data and store it in Amazon S3 Use AWS Batch along with the AWS Deep Learning AMIs to train a k-means model using TensorFlow on the data in Amazon S3.

D.  

Ingest the data and store it in Amazon S3. Have an AWS Glue job that is triggered on demand transform the new data Then use the built-in Random Cut Forest (RCF) model within Amazon SageMaker to detect anomalies in the data

Discussion 0
Question # 78

A retail company wants to update its customer support system. The company wants to implement automatic routing of customer claims to different queues to prioritize the claims by category.

Currently, an operator manually performs the category assignment and routing. After the operator classifies and routes the claim, the company stores the claim’s record in a central database. The claim’s record includes the claim’s category.

The company has no data science team or experience in the field of machine learning (ML). The company’s small development team needs a solution that requires no ML expertise.

Which solution meets these requirements?

Options:

A.  

Export the database to a .csv file with two columns: claim_label and claim_text. Use the Amazon SageMaker Object2Vec algorithm and the .csv file to train a model. Use SageMaker to deploy the model to an inference endpoint. Develop a service in the application to use the inference endpoint to process incoming claims, predict the labels, and route the claims to the appropriate queue.

B.  

Export the database to a .csv file with one column: claim_text. Use the Amazon SageMaker Latent Dirichlet Allocation (LDA) algorithm and the .csv file to train a model. Use the LDA algorithm to detect labels automatically. Use SageMaker to deploy the model to an inference endpoint. Develop a service in the application to use the inference endpoint to process incoming claims, predict the labels, and route the claims to the appropriate queue.

C.  

Use Amazon Textract to process the database and automatically detect two columns: claim_label and claim_text. Use Amazon Comprehend custom classification and the extracted information to train the custom classifier. Develop a service in the application to use the Amazon Comprehend API to process incoming claims, predict the labels, and route the claims to the appropriate queue.

D.  

Export the database to a .csv file with two columns: claim_label and claim_text. Use Amazon Comprehend custom classification and the .csv file to train the custom classifier. Develop a service in the application to use the Amazon Comprehend API to process incoming claims, predict the labels, and route the claims to the appropriate queue.

Discussion 0
Question # 79

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?

Options:

A.  

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.

B.  

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.

C.  

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.

D.  

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.

Discussion 0
Question # 80

A Machine Learning Specialist is deciding between building a naive Bayesian model or a full Bayesian network for a classification problem. The Specialist computes the Pearson correlation coefficients between each feature and finds that their absolute values range between 0.1 to 0.95.

Which model describes the underlying data in this situation?

Options:

A.  

A naive Bayesian model, since the features are all conditionally independent.

B.  

A full Bayesian network, since the features are all conditionally independent.

C.  

A naive Bayesian model, since some of the features are statistically dependent.

D.  

A full Bayesian network, since some of the features are statistically dependent.

Discussion 0
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