Are you seeking the AWS Certified Machine Learning - Specialty exam (MLS-C01) Certification? Passcert has recently updated its AWS Certified Machine Learning - Specialty (MLS-C01) Exam Dumps, which contain comprehensive and up-to-date information to help you prepare for the exam. By studying these AWS Certified Machine Learning - Specialty (MLS-C01) Exam Dumps, you can familiarize yourself with the exam format, practice with sample questions, and gain a deeper understanding of the key concepts and techniques in machine learning on AWS. With the guidance of Passcert, you can confidently approach the exam and increase your chances of passing it on your first attempt.

This credential helps organizations identify and develop talent with critical skills for implementing cloud initiatives. Earning AWS Certified Machine Learning - Specialty validates expertise in building, training, tuning, and deploying machine learning (ML) models on AWS. The AWS Certified Machine Learning - Specialty (MLS-C01) exam is intended for individuals who perform an artificial intelligence/machine learning (AI/ML) development or data science role. The exam validates a candidate's ability to design, build, deploy, optimize, train, tune, and maintain ML solutions for given business problems by using the AWS Cloud.

● Select and justify the appropriate ML approach for a given business problem.

● Identify appropriate AWS services to implement ML solutions.

● Design and implement scalable, cost-optimized, reliable, and secure ML solutions. 

Level: Specialty

Length: 180 minutes to complete the exam

Cost: 300 USD 

Format: 65 questions; either multiple choice or multiple response.

Delivery method: Pearson VUE testing center or online proctored exam.

Score: 750 (100-1000)

Task Statement 1.1: Create data repositories for ML. 

Task Statement 1.2: Identify and implement a data ingestion solution. 

Task Statement 1.3: Identify and implement a data transformation solution. 

Task Statement 2.1: Sanitize and prepare data for modeling. 

Task Statement 2.2: Perform feature engineering. 

Task Statement 2.3: Analyze and visualize data for ML. 

Task Statement 3.1: Frame business problems as ML problems. 

Task Statement 3.2: Select the appropriate model(s) for a given ML problem. 

Task Statement 3.3: Train ML models. 

Task Statement 3.4: Perform hyperparameter optimization. 

Task Statement 3.5: Evaluate ML models. 

Task Statement 4.1: Build ML solutions for performance, availability, scalability, resiliency, and fault tolerance. 

Task Statement 4.2: Recommend and implement the appropriate ML services and features for a given problem. 

Task Statement 4.3: Apply basic AWS security practices to ML solutions. 

Task Statement 4.4: Deploy and operationalize ML solutions. 

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?

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

B.Decrease the retention period for the data stream.

C.Increase the number of shards for the data stream.

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

Answer: C

Amazon Connect has recently been tolled out across a company as a contact call center The solution has been configured to store voice call recordings on Amazon S3

The content of the voice calls are being analyzed for the incidents being discussed by the call operators Amazon Transcribe is being used to convert the audio to text, and the output is stored on Amazon S3

Which approach will provide the information required for further analysis?

A.Use Amazon Comprehend with the transcribed files to build the key topics

B.Use Amazon Translate with the transcribed files to train and build a model for the key topics

C.Use the AWS Deep Learning AMI with Gluon Semantic Segmentation on the transcribed files to train and build a model for the key topics

D.Use the Amazon SageMaker k-Nearest-Neighbors (kNN) algorithm on the transcribed files to generate a word embeddings dictionary for the key topics

Answer: A

A retail company wants to combine its customer orders with the product description data from its product catalog. The structure and format of the records in each dataset is different. A data analyst tried to use a spreadsheet to combine the datasets, but the effort resulted in duplicate records and records that were not properly combined. The company needs a solution that it can use to combine similar records from the two datasets and remove any duplicates.

Which solution will meet these requirements?

A.Use an AWS Lambda function to process the data. Use two arrays to compare equal strings in the fields from the two datasets and remove any duplicates.

B.Create AWS Glue crawlers for reading and populating the AWS Glue Data Catalog. Call the AWS Glue SearchTables API operation to perform a fuzzy-matching search on the two datasets, and cleanse the data accordingly.

C.Create AWS Glue crawlers for reading and populating the AWS Glue Data Catalog. Use the FindMatches transform to cleanse the data.

D.Create an AWS Lake Formation custom transform. Run a transformation for matching products from the Lake Formation console to cleanse the data automatically.

Answer: C

A web-based company wants to improve its conversion rate on its landing page Using a large historical dataset of customer visits, the company has repeatedly trained a multi-class deep learning network algorithm on Amazon SageMaker However there is an overfitting problem training data shows 90% accuracy in predictions, while test data shows 70% accuracy only

The company needs to boost the generalization of its model before deploying it into production to maximize conversions of visits to purchases

Which action is recommended to provide the HIGHEST accuracy model for the company's test and validation data?

A.Increase the randomization of training data in the mini-batches used in training.

B.Allocate a higher proportion of the overall data to the training dataset

C.Apply L1 or L2 regularization and dropouts to the training.

D.Reduce the number of layers and units (or neurons) from the deep learning network.

Answer: C

A Machine Learning Specialist needs to be able to ingest streaming data and store it in Apache Parquet files for exploration and analysis. Which of the following services would both ingest and store this data in the correct format?

A.AWSDMS

B.Amazon Kinesis Data Streams

C.Amazon Kinesis Data Firehose

D.Amazon Kinesis Data Analytics

Answer:C

A company is running a machine learning prediction service that generates 100 TB of predictions every day A Machine Learning Specialist must generate a visualization of the daily precision-recall curve from the predictions, and forward a read-only version to the Business team.

Which solution requires the LEAST coding effort?

A.Run a daily Amazon EMR workflow to generate precision-recall data, and save the results in Amazon S3 Give the Business team read-only access to S3

B.Generate daily precision-recall data in Amazon QuickSight, and publish the results in a dashboard shared with the Business team

C.Run a daily Amazon EMR workflow to generate precision-recall data, and save the results in Amazon S3 Visualize the arrays in Amazon QuickSight, and publish them in a dashboard shared with the Business team

D.Generate daily precision-recall data in Amazon ES, and publish the results in a dashboard shared with the Business team.

Answer: C

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?

A.Exponential transformation

B.Logarithmic transformation

C.Polynomial transformation

D.Sinusoidal transformation

Answer: B

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?

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

B.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.

C.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.

D.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: D

A Machine Learning team uses Amazon SageMaker to train an Apache MXNet handwritten digit classifier model using a research dataset. The team wants to receive a notification when the model is overfitting. Auditors want to view the Amazon SageMaker log activity report to ensure there are no unauthorized API calls.

What should the Machine Learning team do to address the requirements with the least amount of code and fewest steps?

A.Implement an AWS Lambda function to long Amazon SageMaker API calls to Amazon S3. Add code to push a custom metric to Amazon CloudWatch. Create an alarm in CloudWatch with Amazon SNS to receive a notification when the model is overfitting.

B.Use AWS CloudTrail to log Amazon SageMaker API calls to Amazon S3. Add code to push a custom metric to Amazon CloudWatch. Create an alarm in CloudWatch with Amazon SNS to receive a notification when the model is overfitting.

C.Implement an AWS Lambda function to log Amazon SageMaker API calls to AWS CloudTrail. Add code to push a custom metric to Amazon CloudWatch. Create an alarm in CloudWatch with Amazon SNS to receive a notification when the model is overfitting.

D.Use AWS CloudTrail to log Amazon SageMaker API calls to Amazon S3. Set up Amazon SNS to receive a notification when the model is overfitting.

Answer: B

A Machine Learning Specialist is building a prediction model for a large number of features using linear models, such as linear regression and logistic regression During exploratory data analysis the Specialist observes that many features are highly correlated with each other This may make the model unstable

What should be done to reduce the impact of having such a large number of features?

A.Perform one-hot encoding on highly correlated features

B.Use matrix multiplication on highly correlated features.

C.Create a new feature space using principal component analysis (PCA)

D.Apply the Pearson correlation coefficient

Answer: C