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Pass Your MLS-C01 Certification Questions & Practice Tests, AWS Certified Machine Learning | SPOTO

The AWS Certified Machine Learning—Specialty (MLS-C01) exam targets professionals in Development or Data Science roles, validating their skills in ML model creation, training, optimization, and deployment on AWS infrastructure.By leveraging SPOTO's practice tests and MLS-C01 certification questions, candidates can familiarize themselves with the exam format, test their knowledge, and identify areas for improvement. Accessing these resources enhances exam readiness, increases confidence, and improves the likelihood of passing the MLS-C01 certification exam. SPOTO offers a comprehensive MLS-C01 exam preparation package, including practice tests and MLS-C01 certification questions. These resources cover a range of topics such as exam questions, sample questions, and exam dumps, ensuring thorough preparation for the MLS-C01 exam.
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Question #1
A Machine Learning Specialist is developing a custom video recommendation model for an application The dataset used to train this model is very large with millions of data points and is hosted in an Amazon S3 bucket The Specialist wants to avoid loading all of this data onto an Amazon SageMaker notebook instance because it would take hours to move and will exceed the attached 5 GB Amazon EBS volume on the notebook instance. Which approach allows the Specialist to use all the data to train the model?
A. Load a smaller subset of the data into the SageMaker notebook and train locall
B. Confirm that the training code is executing and the model parameters seem reasonabl
C. Initiate a SageMaker training job using the full dataset from the S3 bucket using Pipe input mode
D. Launch an Amazon EC2 instance with an AWS Deep Learning AMI and attach the S3 bucket to theinstanc
E. Train on a small amount of the data to verify the training code and hyperparameter
F. Go back toAmazon SageMaker and train using the full dataset G
View answer
Correct Answer: C
Question #2
A Data Scientist wants to gain real-time insights into a data stream of GZIP files. Which solution would allow the use of SQL to query the stream with the LEAST latency?
A. Amazon Kinesis Data Analytics with an AWS Lambda function to transform the data
B. AWS Glue with a custom ETL script to transform the data
C. An Amazon Kinesis Client Library to transform the data and save it to an Amazon ES cluster
D. Amazon Kinesis Data Firehose to transform the data and put it into an Amazon S3 bucket
View answer
Correct Answer: A
Question #3
Q22: A retail chain has been ingesting purchasing records from its network of 20,000 stores to Amazon S3 using Amazon Kinesis Data Firehose. To support training an improved machine learning model, training records will require new but simple transformations, and some attributes will be combined. The model needs to be retrained daily. Given the large number of stores and the legacy data ingestion, which change will require the LEAST amount of development effort?
A. Require that the stores to switch to capturing their data locally on AWS Storage Gateway for loading into Amazon S3, then use AWS Glue to do the transformation
B. Deploy an Amazon EMR cluster running Apache Spark with the transformation logic, and have the cluster run each day on the accumulating records in Amazon S3, outputting new/transformed records to Amazon S3
C. Spin up a fleet of Amazon EC2 instances with the transformation logic, have them transform the data records accumulating on Amazon S3, and output the transformed records to Amazon S3
D. Insert an Amazon Kinesis Data Analytics stream downstream of the Kinesis Data Firehose stream that transforms raw record attributes into simple transformed values using SQL
View answer
Correct Answer: D
Question #4
A Machine Learning Specialist must build out a process to query a dataset on Amazon S3 using Amazon Athena The dataset contains more than 800.000 records stored as plaintext CSV files Each record contains 200 columns and is approximately 1 5 MB in size Most queries will span 5 to 10 columns only How should the Machine Learning Specialist transform the dataset to minimize query runtime?
A. Convert the records to Apache Parquet format
B. Convert the records to JSON format
C. Convert the records to GZIP CSV format
D. Convert the records to XML format
View answer
Correct Answer: A
Question #5
A large consumer goods manufacturer has the following products on sale ? 34 different toothpaste variants ? 48 different toothbrush variants ? 43 different mouthwash variants The entire sales history of all these products is available in Amazon S3 Currently, the company is using custom-built autoregressive integrated moving average (ARIMA) models to forecast demand for these products The company wants to predict the demand for a new product that will soon be launched Which solution should a Machine Learning
A. Train a custom ARIMA model to forecast demand for the new product
B. Train an Amazon SageMaker DeepAR algorithm to forecast demand for the new product
C. Train an Amazon SageMaker k-means clustering algorithm to forecast demand for the new product
D. Train a custom XGBoost model to forecast demand for the new product
View answer
Correct Answer: C
Question #6
Q21: A Machine Learning Specialist is configuring Amazon SageMaker so multiple Data Scientists can access notebooks, train models, and deploy endpoints. To ensure the best operational performance, the Specialist needs to be able to track how often the Scientists are deploying models, GPU and CPU utilization on the deployed SageMaker endpoints, and all errors that are generated when an endpoint is invoked. Which services are integrated with Amazon SageMaker to track this information? (Choose two.)
A. AWS CloudTrail
B. AWS Health
C. AWS Trusted Advisor
D. Amazon CloudWatch
E. AWS Config
View answer
Correct Answer: AD
Question #7
A financial services company is building a robust serverless data lake on Amazon S3. The data lake should be flexible and meet the following requirements: * Support querying old and new data on Amazon S3 through Amazon Athena and Amazon Redshift Spectrum. * Support event-driven ETL pipelines. * Provide a quick and easy way to understand metadata. Which approach meets trfese requirements?
A. Use an AWS Glue crawler to crawl S3 data, an AWS Lambda function to trigger an AWS Glue ETL job, and an AWS Glue Data catalog to search and discover metadata
B. Use an AWS Glue crawler to crawl S3 data, an AWS Lambda function to trigger an AWS Batch job, and an external Apache Hive metastore to search and discover metadata
C. Use an AWS Glue crawler to crawl S3 data, an Amazon CloudWatch alarm to trigger an AWS Batch job, and an AWS Glue Data Catalog to search and discover metadata
D. Use an AWS Glue crawler to crawl S3 data, an Amazon CloudWatch alarm to trigger an AWS Glue ETL job, and an external Apache Hive metastore to search and discover metadata
View answer
Correct Answer: C
Question #8
A Machine Learning Specialist is building a logistic regression model that will predict whether or not a person will order a pizza. The Specialist is trying to build the optimal model with an ideal classification threshold. What model evaluation technique should the Specialist use to understand how different classification thresholds will impact the model's performance?
A. Receiver operating characteristic (ROC) curve
B. Misclassification rate
C. Root Mean Square Error (RM&)
D. L1 norm
View answer
Correct Answer: B

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