MLS-C01 TEST QUESTIONS: AWS CERTIFIED MACHINE LEARNING - SPECIALTY & MLS-C01 PASS FOR SURE

MLS-C01 test questions: AWS Certified Machine Learning - Specialty & MLS-C01 pass for sure

MLS-C01 test questions: AWS Certified Machine Learning - Specialty & MLS-C01 pass for sure

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Tags: MLS-C01 Valid Test Test, MLS-C01 Exam Guide Materials, MLS-C01 Vce Files, Test MLS-C01 Dumps, MLS-C01 Exam Reference

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Achieving the AWS Certified Machine Learning - Specialty certification can help individuals advance their careers in the field of machine learning and increase their earning potential. AWS Certified Machine Learning - Specialty certification is recognized by industry leaders and can open up new opportunities for professionals in various industries, including healthcare, finance, and retail, among others.

To earn the AWS Certified Machine Learning - Specialty certification, candidates must pass the MLS-C01 Exam, which consists of 65 multiple-choice and multiple-response questions. MLS-C01 exam covers a broad range of topics, including data engineering, feature engineering, model selection and training, and deployment and monitoring of ML models. MLS-C01 exam also tests the candidate's knowledge of AWS's various ML services, such as Amazon SageMaker, Amazon Rekognition, and Amazon Comprehend.

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MLS-C01 Exam Guide Materials - MLS-C01 Vce Files

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Amazon AWS Certified Machine Learning - Specialty Sample Questions (Q276-Q281):

NEW QUESTION # 276
A company needs to quickly make sense of a large amount of data and gain insight from it. The data is in different formats, the schemas change frequently, and new data sources are added regularly. The company wants to use AWS services to explore multiple data sources, suggest schemas, and enrich and transform the data. The solution should require the least possible coding effort for the data flows and the least possible infrastructure management.
Which combination of AWS services will meet these requirements?

  • A. Amazon Kinesis Data Analytics for data ingestion
    Amazon EMR for data discovery, enrichment, and transformation
    Amazon Redshift for querying and analyzing the results in Amazon S3
  • B. AWS Data Pipeline for data transferAWS Step Functions for orchestrating AWS Lambda jobs for data discovery, enrichment, and transformation Amazon Athena for querying and analyzing the results in Amazon S3 using standard SQL Amazon QuickSight for reporting and getting insights
  • C. Amazon EMR for data discovery, enrichment, and transformation
    Amazon Athena for querying and analyzing the results in Amazon S3 using standard SQL Amazon QuickSight for reporting and getting insights
  • D. AWS Glue for data discovery, enrichment, and transformation
    Amazon Athena for querying and analyzing the results in Amazon S3 using standard SQL Amazon QuickSight for reporting and getting insights

Answer: D

Explanation:
The best combination of AWS services to meet the requirements of data discovery, enrichment, transformation, querying, analysis, and reporting with the least coding and infrastructure management is AWS Glue, Amazon Athena, and Amazon QuickSight. These services are:
* AWS Glue for data discovery, enrichment, and transformation. AWS Glue is a serverless data integration service that automatically crawls, catalogs, and prepares data from various sources and formats. It also provides a visual interface called AWS Glue DataBrew that allows users to apply over
250 transformations to clean, normalize, and enrich data without writing code1
* Amazon Athena for querying and analyzing the results in Amazon S3 using standard SQL. Amazon Athena is a serverless interactive query service that allows users to analyze data in Amazon S3 using standard SQL. It supports a variety of data formats, such as CSV, JSON, ORC, Parquet, and Avro. It also integrates with AWS Glue Data Catalog to provide a unified view of the data sources and schemas2
* Amazon QuickSight for reporting and getting insights. Amazon QuickSight is a serverless business intelligence service that allows users to create and share interactive dashboards and reports. It also provides ML-powered features, such as anomaly detection, forecasting, and natural language queries, to help users discover hidden insights from their data3 The other options are not suitable because they either require more coding effort, more infrastructure management, or do not support the desired use cases. For example:
* Option A uses Amazon EMR for data discovery, enrichment, and transformation. Amazon EMR is a managed cluster platform that runs Apache Spark, Apache Hive, and other open-source frameworks for big data processing. It requires users to write code in languages such as Python, Scala, or SQL to perform data integration tasks. It also requires users to provision, configure, and scale the clusters according to their needs4
* Option B uses Amazon Kinesis Data Analytics for data ingestion. Amazon Kinesis Data Analytics is a service that allows users to process streaming data in real time using SQL or Apache Flink. It is not suitable for data discovery, enrichment, and transformation, which are typically batch-oriented tasks. It also requires users to write code to define the data processing logic and the output destination5
* Option D uses AWS Data Pipeline for data transfer and AWS Step Functions for orchestrating AWS Lambda jobs for data discovery, enrichment, and transformation. AWS Data Pipeline is a service that helps users move data between AWS services and on-premises data sources. AWS Step Functions is a service that helps users coordinate multiple AWS services into workflows. AWS Lambda is a service that lets users run code without provisioning or managing servers. These services require users to write code to define the data sources, destinations, transformations, and workflows. They also require users to manage the scalability, performance, and reliability of the data pipelines.
References:
* 1: AWS Glue - Data Integration Service - Amazon Web Services
* 2: Amazon Athena - Interactive SQL Query Service - AWS
* 3: Amazon QuickSight - Business Intelligence Service - AWS
* 4: Amazon EMR - Amazon Web Services
* 5: Amazon Kinesis Data Analytics - Amazon Web Services
* : AWS Data Pipeline - Amazon Web Services
* : AWS Step Functions - Amazon Web Services
* : AWS Lambda - Amazon Web Services


NEW QUESTION # 277
The displayed graph is from a foresting model for testing a time series.
Considering the graph only, which conclusion should a Machine Learning Specialist make about the behavior of the model?

  • A. The model predicts both the trend and the seasonality well.
  • B. The model predicts the trend well, but not the seasonality.
  • C. The model does not predict the trend or the seasonality well.
  • D. The model predicts the seasonality well, but not the trend.

Answer: C


NEW QUESTION # 278
A Machine Learning Specialist kicks off a hyperparameter tuning job for a tree-based ensemble model using Amazon SageMaker with Area Under the ROC Curve (AUC) as the objective metric. This workflow will eventually be deployed in a pipeline that retrains and tunes hyperparameters each night to model click-through on data that goes stale every 24 hours.
With the goal of decreasing the amount of time it takes to train these models, and ultimately to decrease costs, the Specialist wants to reconfigure the input hyperparameter range(s).
Which visualization will accomplish this?

  • A. A histogram showing whether the most important input feature is Gaussian.
  • B. A scatter plot showing the performance of the objective metric over each training iteration.
  • C. A scatter plot with points colored by target variable that uses t-Distributed Stochastic Neighbor Embedding (t-SNE) to visualize the large number of input variables in an easier-to-read dimension.
  • D. A scatter plot showing the correlation between maximum tree depth and the objective metric.

Answer: C


NEW QUESTION # 279
An agency collects census information within a country to determine healthcare and social program needs by province and city. The census form collects responses for approximately 500 questions from each citizen Which combination of algorithms would provide the appropriate insights? (Select TWO )

  • A. The Latent Dirichlet Allocation (LDA) algorithm
  • B. The factorization machines (FM) algorithm
  • C. The principal component analysis (PCA) algorithm
  • D. The k-means algorithm
  • E. The Random Cut Forest (RCF) algorithm

Answer: C,D

Explanation:
The PCA and K-means algorithms are useful in collection of data using census form.


NEW QUESTION # 280
A machine learning specialist is running an Amazon SageMaker endpoint using the built-in object detection algorithm on a P3 instance for real-time predictions in a company's production application. When evaluating the model's resource utilization, the specialist notices that the model is using only a fraction of the GPU.
Which architecture changes would ensure that provisioned resources are being utilized effectively?

  • A. Redeploy the model on a P3dn instance.
  • B. Deploy the model onto an Amazon Elastic Container Service (Amazon ECS) cluster using a P3 instance.
  • C. Redeploy the model on an M5 instance. Attach Amazon Elastic Inference to the instance.
  • D. Redeploy the model as a batch transform job on an M5 instance.

Answer: C

Explanation:
The best way to ensure that provisioned resources are being utilized effectively is to redeploy the model on an M5 instance and attach Amazon Elastic Inference to the instance. Amazon Elastic Inference allows you to attach low-cost GPU-powered acceleration to Amazon EC2 and Amazon SageMaker instances to reduce the cost of running deep learning inference by up to 75%. By using Amazon Elastic Inference, you can choose the instance type that is best suited to the overall CPU and memory needs of your application, and then separately configure the amount of inference acceleration that you need with no code changes. This way, you can avoid wasting GPU resources and pay only for what you use.
Option A is incorrect because a batch transform job is not suitable for real-time predictions. Batch transform is a high-performance and cost-effective feature for generating inferences using your trained models. Batch transform manages all of the compute resources required to get inferences. Batch transform is ideal for scenarios where you're working with large batches of data, don't need sub-second latency, or need to process data that is stored in Amazon S3.
Option C is incorrect because redeploying the model on a P3dn instance would not improve the resource utilization. P3dn instances are designed for distributed machine learning and high performance computing applications that need high network throughput and packet rate performance. They are not optimized for inference workloads.
Option D is incorrect because deploying the model onto an Amazon ECS cluster using a P3 instance would not ensure that provisioned resources are being utilized effectively. Amazon ECS is a fully managed container orchestration service that allows you to run and scale containerized applications on AWS. However, using Amazon ECS would not address the issue of underutilized GPU resources. In fact, it might introduce additional overhead and complexity in managing the cluster.
Amazon Elastic Inference - Amazon SageMaker
Batch Transform - Amazon SageMaker
Amazon EC2 P3 Instances
Amazon EC2 P3dn Instances
Amazon Elastic Container Service


NEW QUESTION # 281
......

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