MLS-C01 RELIABLE PRACTICE MATERIALS, LATEST MLS-C01 TEST SAMPLE

MLS-C01 Reliable Practice Materials, Latest MLS-C01 Test Sample

MLS-C01 Reliable Practice Materials, Latest MLS-C01 Test Sample

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The AWS Certified Machine Learning - Specialty exam consists of 65 multiple-choice and multiple-response questions, and candidates have three hours to complete the exam. MLS-C01 exam tests the candidate's ability to design, implement, deploy, and maintain machine learning solutions on the AWS platform. To be eligible for the exam, candidates need to have a minimum of one year of experience in developing and maintaining machine learning solutions on the AWS platform. Upon successful completion of the exam, candidates will receive the AWS Certified Machine Learning - Specialty certification, which is recognized globally and demonstrates their expertise in the field of machine learning on the AWS platform.

Amazon MLS-C01 Exam covers a wide range of topics related to machine learning, including data preparation, feature engineering, model training and evaluation, and deployment. Candidates are required to have a strong understanding of machine learning algorithms, statistical modeling, and programming languages such as Python and R. In addition, candidates are expected to have experience working with AWS services such as Amazon SageMaker, Amazon Rekognition, and Amazon Comprehend.

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Amazon MLS-C01 (AWS Certified Machine Learning - Specialty) Exam is a certification exam designed for individuals who are seeking to validate their skills and knowledge in machine learning on the Amazon Web Services (AWS) platform. MLS-C01 Exam is aimed at professionals who have experience in designing, implementing, deploying, and maintaining machine learning solutions using AWS services.

Amazon AWS Certified Machine Learning - Specialty Sample Questions (Q107-Q112):

NEW QUESTION # 107
A Machine Learning Specialist is working for an online retailer that wants to run analytics on every customer visit, processed through a machine learning pipeline. The data needs to be ingested by Amazon Kinesis Data Streams at up to 100 transactions per second, and the JSON data blob is 100 KB in size.
What is the MINIMUM number of shards in Kinesis Data Streams the Specialist should use to successfully ingest this data?

  • A. 10 shards
  • B. 100 shards
  • C. 1,000 shards
  • D. 1 shards

Answer: D

Explanation:
According to the Amazon Kinesis Data Streams documentation, the maximum size of data blob (the data payload before Base64-encoding) per record is 1 MB. The maximum number of records that can be sent to a shard per second is 1,000. Therefore, the maximum throughput of a shard is 1 MB/sec for input and 2 MB/sec for output. In this case, the input throughput is 100 transactions per second * 100 KB per transaction = 10 MB
/sec. Therefore, the minimum number of shards required is 10 MB/sec / 1 MB/sec = 10 shards. However, the question asks for the minimum number of shards in Kinesis Data Streams, not the minimum number of shards per stream. A Kinesis Data Streams account can have multiple streams, each with its own number of shards.
Therefore, the minimum number of shards in Kinesis Data Streams is 1, which is the minimum number of shards per stream. References:
* Amazon Kinesis Data Streams Terminology and Concepts
* Amazon Kinesis Data Streams Limits


NEW QUESTION # 108
A company sells thousands of products on a public website and wants to automatically identify products with potential durability problems. The company has 1.000 reviews with date, star rating, review text, review summary, and customer email fields, but many reviews are incomplete and have empty fields. Each review has already been labeled with the correct durability result.
A machine learning specialist must train a model to identify reviews expressing concerns over product durability. The first model needs to be trained and ready to review in 2 days.
What is the MOST direct approach to solve this problem within 2 days?

  • A. Use a built-in seq2seq model in Amazon SageMaker.
  • B. Build a recurrent neural network (RNN) in Amazon SageMaker by using Gluon and Apache MXNet.
  • C. Train a built-in BlazingText model using Word2Vec mode in Amazon SageMaker.
  • D. Train a custom classifier by using Amazon Comprehend.

Answer: D

Explanation:
Explanation
The most direct approach to solve this problem within 2 days is to train a custom classifier by using Amazon Comprehend. Amazon Comprehend is a natural language processing (NLP) service that can analyze text and extract insights such as sentiment, entities, topics, and syntax. Amazon Comprehend also provides a custom classification feature that allows users to create and train a custom text classifier using their own labeled data.
The custom classifier can then be used to categorize any text document into one or more custom classes. For this use case, the custom classifier can be trained to identify reviews that express concerns over product durability as a class, and use the star rating, review text, and review summary fields as input features. The custom classifier can be created and trained using the Amazon Comprehend console or API, and does not require any coding or machine learning expertise. The training process is fully managed and scalable, and can handle large and complex datasets. The custom classifier can be trained and ready to review in 2 days or less, depending on the size and quality of the dataset.
The other options are not the most direct approaches because:
Option B: Building a recurrent neural network (RNN) in Amazon SageMaker by using Gluon and Apache MXNet is a more complex and time-consuming approach that requires coding and machine learning skills. RNNs are a type of deep learning models that can process sequential data, such as text, and learn long-term dependencies between tokens. Gluon is a high-level API for MXNet that simplifies the development of deep learning models. Amazon SageMaker is a fully managed service that provides tools and frameworks for building, training, and deploying machine learning models. However, to use this approach, the machine learning specialist would have to write custom code to preprocess the data, define the RNN architecture, train the model, and evaluate the results. This would likely take more than
2 days and involve more administrative overhead.
Option C: Training a built-in BlazingText model using Word2Vec mode in Amazon SageMaker is not a suitable approach for text classification. BlazingText is a built-in algorithm in Amazon SageMaker that provides highly optimized implementations of the Word2Vec and text classification algorithms. The Word2Vec algorithm is useful for generating word embeddings, which are dense vector representations of words that capture their semantic and syntactic similarities. However, word embeddings alone are not sufficient for text classification, as they do not account for the context and structure of the text documents. To use this approach, the machine learning specialist would have to combine the word embeddings with another classifier model, such as a logistic regression or a neural network, which would add more complexity and time to the solution.
Option D: Using a built-in seq2seq model in Amazon SageMaker is not a relevant approach for text classification. Seq2seq is a built-in algorithm in Amazon SageMaker that provides a sequence-to-sequence framework for neural machine translation based on MXNet. Seq2seq is a supervised learning algorithm that can generate an output sequence of tokens given an input sequence of tokens, such as translating a sentence from one language to another. However, seq2seq is not designed for text classification, which requires assigning a label or a category to a text document, not generating another text sequence. To use this approach, the machine learning specialist would have to modify the seq2seq algorithm to fit the text classification task, which would be challenging and inefficient.
References:
Custom Classification - Amazon Comprehend
Build a Text Classification Model with Amazon Comprehend - AWS Machine Learning Blog Recurrent Neural Networks - Gluon API BlazingText Algorithm - Amazon SageMaker Sequence-to-Sequence Algorithm - Amazon SageMaker


NEW QUESTION # 109
A company is creating an application to identify, count, and classify animal images that are uploaded to the company's website. The company is using the Amazon SageMaker image classification algorithm with an ImageNetV2 convolutional neural network (CNN). The solution works well for most animal images but does not recognize many animal species that are less common.
The company obtains 10,000 labeled images of less common animal species and stores the images in Amazon S3. A machine learning (ML) engineer needs to incorporate the images into the model by using Pipe mode in SageMaker.
Which combination of steps should the ML engineer take to train the model? (Choose two.)

  • A. Use an augmented manifest file in JSON Lines format.
  • B. Use an Inception model that is available with the SageMaker image classification algorithm.
  • C. Use a ResNet model. Initiate full training mode by initializing the network with random weights.
  • D. Initiate transfer learning. Train the model by using the images of less common species.
  • E. Create a .lst file that contains a list of image files and corresponding class labels. Upload the .lst file to Amazon S3.

Answer: D,E

Explanation:
Explanation
The combination of steps that the ML engineer should take to train the model are to create a .lst file that contains a list of image files and corresponding class labels, upload the .lst file to Amazon S3, and initiate transfer learning by training the model using the images of less common species. This approach will allow the ML engineer to leverage the existing ImageNetV2 CNN model and fine-tune it with the new data using Pipe mode in SageMaker.
A .lst file is a text file that contains a list of image files and corresponding class labels, separated by tabs. The
.lst file format is required for using the SageMaker image classification algorithm with Pipe mode. Pipe mode is a feature of SageMaker that enables streaming data directly from Amazon S3 to the training instances, without downloading the data first. Pipe mode can reduce the startup time, improve the I/O throughput, and enable training on large datasets that exceed the disk size limit. To use Pipe mode, the ML engineer needs to upload the .lst file to Amazon S3 and specify the S3 path as the input data channel for the training job1.
Transfer learning is a technique that enables reusing a pre-trained model for a new task by fine-tuning the model parameters with new data. Transfer learning can save time and computational resources, as well as improve the performance of the model, especially when the new task is similar to the original task. The SageMaker image classification algorithm supports transfer learning by allowing the ML engineer to specify the number of output classes and the number of layers to be retrained. The ML engineer can use the existing ImageNetV2 CNN model, which is trained on 1,000 classes of common objects, and fine-tune it with the new data of less common animal species, which is a similar task2.
The other options are either less effective or not supported by the SageMaker image classification algorithm.
Using a ResNet model and initiating full training mode would require training the model from scratch, which would take more time and resources than transfer learning. Using an Inception model is not possible, as the SageMaker image classification algorithm only supports ResNet and ImageNetV2 models. Using an augmented manifest file in JSON Lines format is not compatible with Pipe mode, as Pipe mode only supports
.lst files for image classification1.
References:
1: Using Pipe input mode for Amazon SageMaker algorithms | AWS Machine Learning Blog
2: Image Classification Algorithm - Amazon SageMaker


NEW QUESTION # 110
A company has raw user and transaction data stored in AmazonS3 a MySQL database, and Amazon RedShift A Data Scientist needs to perform an analysis by joining the three datasets from Amazon S3, MySQL, and Amazon RedShift, and then calculating the average-of a few selected columns from the joined data Which AWS service should the Data Scientist use?

  • A. AWS Glue
  • B. Amazon Redshift Spectrum
  • C. Amazon QuickSight
  • D. Amazon Athena

Answer: D


NEW QUESTION # 111
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. AWS Glue with a custom ETL script to transform the data.
  • B. Amazon Kinesis Data Analytics with an AWS Lambda function to transform the data.
  • C. Amazon Kinesis Data Firehose to transform the data and put it into an Amazon S3 bucket.
  • D. An Amazon Kinesis Client Library to transform the data and save it to an Amazon ES cluster.

Answer: B

Explanation:
* Amazon Kinesis Data Analytics is a service that enables you to analyze streaming data in real time using SQL or Apache Flink applications. You can use Kinesis Data Analytics to process and gain insights from data streams such as web logs, clickstreams, IoT data, and more.
* To use SQL to query a data stream of GZIP files, you need to first transform the data into a format that Kinesis Data Analytics can understand, such as JSON, CSV, or Apache Parquet. You can use an AWS Lambda function to perform this transformation and send the output to a Kinesis data stream that is connected to your Kinesis Data Analytics application. This way, you can use SQL to query the stream with the least latency, as Lambda functions are triggered in near real time by the incoming data and Kinesis Data Analytics can process the data as soon as it arrives.
* The other options are not optimal for this scenario, as they introduce more latency or complexity. AWS Glue is a serverless data integration service that can perform ETL (extract, transform, and load) tasks on data sources, but it is not designed for real-time streaming data analysis. An Amazon Kinesis Client Library is a Java library that enables you to build custom applications that process data from Kinesis data streams, but it requires more coding and configuration than using a Lambda function. Amazon Kinesis Data Firehose is a service that can deliver streaming data to destinations such as Amazon S3, Amazon Redshift, Amazon OpenSearch Service, and Splunk, but it does not support SQL queries on the data.
What Is Amazon Kinesis Data Analytics for SQL Applications?
Using AWS Lambda with Amazon Kinesis Data Streams
Using AWS Lambda with Amazon Kinesis Data Firehose


NEW QUESTION # 112
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