Amazon AWS-Certified-Machine-Learning-Specialty Test Dumps Free & Valid AWS-Certified-Machine-Learning-Specialty Mock Test

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To become an AWS Certified Machine Learning - Specialty, you need to have a deep understanding of machine learning concepts, algorithms, and tools. You should also have practical experience in building and deploying machine learning models using AWS services such as Amazon SageMaker, AWS Lambda, Amazon Redshift, and Amazon Athena. AWS-Certified-Machine-Learning-Specialty Exam covers various topics such as data preparation, feature engineering, model training and deployment, optimization and tuning, and security and compliance. It consists of multiple-choice and multiple-response questions, and you have 170 minutes to complete it. Passing the exam requires a score of at least 750 out of 1000. By earning the AWS Certified Machine Learning - Specialty certification, you demonstrate your ability to design and deliver cutting-edge machine learning solutions on the AWS platform, which can open up new career opportunities and increase your earning potential.

The MLS-C01 exam covers a range of topics, including machine learning and deep learning algorithms, data modeling and evaluation, and data visualization. Candidates will be tested on their ability to design, implement, and maintain scalable, cost-effective, and highly available machine learning solutions on AWS. Candidates will also be expected to demonstrate their knowledge of AWS services such as Amazon SageMaker, Amazon Rekognition, Amazon Comprehend, and Amazon Personalize.

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To be eligible for the exam, candidates should have at least one year of experience in designing and deploying ML solutions on AWS. They should also have a solid understanding of the AWS ecosystem, including various services such as Amazon SageMaker, Amazon S3, and AWS Lambda. Passing the Amazon MLS-C01 exam demonstrates a candidate's proficiency in using AWS services to build scalable and reliable ML solutions, which can be used to solve complex business problems. With the increasing demand for ML specialists, this certification can provide a significant career advantage to professionals looking to advance in this field.

Amazon AWS Certified Machine Learning - Specialty Sample Questions (Q271-Q276):

NEW QUESTION # 271
A company uses a long short-term memory (LSTM) model to evaluate the risk factors of a particular energy sector. The model reviews multi-page text documents to analyze each sentence of the text and categorize it as either a potential risk or no risk. The model is not performing well, even though the Data Scientist has experimented with many different network structures and tuned the corresponding hyperparameters.
Which approach will provide the MAXIMUM performance boost?

A. Initialize the words by word2vec embeddings pretrained on a large collection of news articles related to the energy sector.
B. Initialize the words by term frequency-inverse document frequency (TF-IDF) vectors pretrained on a large collection of news articles related to the energy sector.
C. Reduce the learning rate and run the training process until the training loss stops decreasing.
D. Use gated recurrent units (GRUs) instead of LSTM and run the training process until the validation loss stops decreasing.

Answer: A

Explanation:
Initializing the words by word2vec embeddings pretrained on a large collection of news articles related to the energy sector will provide the maximum performance boost for the LSTM model. Word2vec is a technique that learns distributed representations of words based on their co-occurrence in a large corpus of text. These representations capture semantic and syntactic similarities between words, which can help the LSTM model better understand the meaning and context of the sentences in the text documents. Using word2vec embeddings that are pretrained on a relevant domain (energy sector) can further improve the performance by reducing the vocabulary mismatch and increasing the coverage of the words in the text documents. References:
AWS Machine Learning Specialty Exam Guide
AWS Machine Learning Training - Text Classification with TF-IDF, LSTM, BERT: a comparison of performance AWS Machine Learning Training - Machine Learning - Exam Preparation Path

NEW QUESTION # 272
The chief editor for a product catalog wants the research and development team to build a machine learning system that can be used to detect whether or not individuals in a collection of images are wearing the company's retail brand. The team has a set of training data.
Which machine learning algorithm should the researchers use that BEST meets their requirements?

A. K-means
B. Latent Dirichlet Allocation (LDA)
C. Recurrent neural network (RNN)
D. Convolutional neural network (CNN)

Answer: D

Explanation:
The problem of detecting whether or not individuals in a collection of images are wearing the company's retail brand is an example of image recognition, which is a type of machine learning task that identifies and classifies objects in an image. Convolutional neural networks (CNNs) are a type of machine learning algorithm that are well-suited for image recognition, as they can learn to extract features from images and handle variations in size, shape, color, and orientation of the objects. CNNs consist of multiple layers that perform convolution, pooling, and activation operations on the input images, resulting in a high-level representation that can be used for classification or detection. Therefore, option D is the best choice for the machine learning algorithm that meets the requirements of the chief editor.
Option A is incorrect because latent Dirichlet allocation (LDA) is a type of machine learning algorithm that is used for topic modeling, which is a task that discovers the hidden themes or topics in a collection of text documents. LDA is not suitable for image recognition, as it does not preserve the spatial information of the pixels. Option B is incorrect because recurrent neural networks (RNNs) are a type of machine learning algorithm that are used for sequential data, such as text, speech, or time series. RNNs can learn from the temporal dependencies and patterns in the input data, and generate outputs that depend on the previous states. RNNs are not suitable for image recognition, as they do not capture the spatial dependencies and patterns in the input images. Option C is incorrect because k-means is a type of machine learning algorithm that is used for clustering, which is a task that groups similar data points together based on their features. K-means is not suitable for image recognition, as it does not perform classification or detection of the objects in the images.
References:
Image Recognition Software - ML Image & Video Analysis - Amazon ...
Image classification and object detection using Amazon Rekognition ...
AWS Amazon Rekognition - Deep Learning Face and Image Recognition ...
GitHub - awslabs/aws-ai-solution-kit: Machine Learning APIs for common ...
Meet iNaturalist, an AWS-powered nature app that helps you identify ...

NEW QUESTION # 273
A data engineer needs to provide a team of data scientists with the appropriate dataset to run machine learning training jobs. The data will be stored in Amazon S3. The data engineer is obtaining the data from an Amazon Redshift database and is using join queries to extract a single tabular dataset. A portion of the schema is as follows:
...traction Timestamp (Timeslamp)
...JName(Varchar)
...JNo (Varchar)
Th data engineer must provide the data so that any row with a CardNo value of NULL is removed. Also, the TransactionTimestamp column must be separated into a TransactionDate column and a isactionTime column Finally, the CardName column must be renamed to NameOnCard.
The data will be extracted on a monthly basis and will be loaded into an S3 bucket. The solution must minimize the effort that is needed to set up infrastructure for the ingestion and transformation. The solution must be automated and must minimize the load on the Amazon Redshift cluster Which solution meets these requirements?

A. Set up an AWS Glue job that has the Amazon Redshift cluster as the source and the S3 bucket as the destination Use the built-in transforms Filter, Map. and RenameField to perform the required transformations. Schedule the job to run monthly.
B. Set up an Amazon EC2 instance with a SQL client tool, such as SQL Workbench/J. to query the data from the Amazon Redshift cluster directly. Export the resulting dataset into a We. Upload the file into the S3 bucket. Perform these tasks monthly.
C. Use Amazon Redshift Spectrum to run a query that writes the data directly to the S3 bucket. Create an AWS Lambda function to run the query monthly
D. Set up an Amazon EMR cluster Create an Apache Spark job to read the data from the Amazon Redshift cluster and transform the data. Load the data into the S3 bucket. Schedule the job to run monthly.

Answer: A

Explanation:
The best solution for this scenario is to set up an AWS Glue job that has the Amazon Redshift cluster as the source and the S3 bucket as the destination, and use the built-in transforms Filter, Map, and RenameField to perform the required transformations. This solution has the following advantages:
It minimizes the effort that is needed to set up infrastructure for the ingestion and transformation, as AWS Glue is a fully managed service that provides a serverless Apache Spark environment, a graphical interface to define data sources and targets, and a code generation feature to create and edit scripts1.
It automates the extraction and transformation process, as AWS Glue can schedule the job to run monthly, and handle the connection, authentication, and configuration of the Amazon Redshift cluster and the S3 bucket2.
It minimizes the load on the Amazon Redshift cluster, as AWS Glue can read the data from the cluster in parallel and use a JDBC connection that supports SSL encryption3.
It performs the required transformations, as AWS Glue can use the built-in transforms Filter, Map, and RenameField to remove the rows with NULL values, split the timestamp column into date and time columns, and rename the card name column, respectively4.
The other solutions are not optimal or suitable, because they have the following drawbacks:
A: Setting up an Amazon EMR cluster and creating an Apache Spark job to read the data from the Amazon Redshift cluster and transform the data is not the most efficient or convenient solution, as it requires more effort and resources to provision, configure, and manage the EMR cluster, and to write and maintain the Spark code5.
B: Setting up an Amazon EC2 instance with a SQL client tool to query the data from the Amazon Redshift cluster directly and export the resulting dataset into a CSV file is not a scalable or reliable solution, as it depends on the availability and performance of the EC2 instance, and the manual execution and upload of the SQL queries and the CSV file6.
D: Using Amazon Redshift Spectrum to run a query that writes the data directly to the S3 bucket and creating an AWS Lambda function to run the query monthly is not a feasible solution, as Amazon Redshift Spectrum does not support writing data to external tables or S3 buckets, only reading data from them7.
References:
1: What Is AWS Glue? - AWS Glue
2: Populating the Data Catalog - AWS Glue
3: Best Practices When Using AWS Glue with Amazon Redshift - AWS Glue
4: Built-In Transforms - AWS Glue
5: What Is Amazon EMR? - Amazon EMR
6: Amazon EC2 - Amazon Web Services (AWS)
7: Using Amazon Redshift Spectrum to Query External Data - Amazon Redshift

NEW QUESTION # 274
An interactive online dictionary wants to add a widget that displays words used in similar contexts. A Machine Learning Specialist is asked to provide word features for the downstream nearest neighbor model powering the widget.
What should the Specialist do to meet these requirements?

A. Create one-hot word encoding vectors.
B. Download word embedding's pre-trained on a large corpus.
C. Produce a set of synonyms for every word using Amazon Mechanical Turk.
D. Create word embedding factors that store edit distance with every other word.

Answer: B

Explanation:
Word embeddings are a type of dense representation of words, which encode semantic meaning in a vector form. These embeddings are typically pre-trained on a large corpus of text data, such as a large set of books, news articles, or web pages, and capture the context in which words are used. Word embeddings can be used as features for a nearest neighbor model, which can be used to find words used in similar contexts. Downloading pre-trained word embeddings is a good way to get started quickly and leverage the strengths of these representations, which have been optimized on a large amount of data. This is likely to result in more accurate and reliable features than other options like one-hot encoding, edit distance, or using Amazon Mechanical Turk to produce synonyms.

NEW QUESTION # 275
A machine learning (ML) developer for an online retailer recently uploaded a sales dataset into Amazon SageMaker Studio. The ML developer wants to obtain importance scores for each feature of the dataset. The ML developer will use the importance scores to feature engineer the dataset.
Which solution will meet this requirement with the LEAST development effort?

A. Use a SageMaker notebook instance to perform principal component analysis (PCA).
B. Use a SageMaker notebook instance to perform a singular value decomposition analysis.
C. Use the multicollinearity feature to perform a lasso feature selection to perform an importance scores analysis.
D. Use SageMaker Data Wrangler to perform a Gini importance score analysis.

Answer: D

Explanation:
SageMaker Data Wrangler is a feature of SageMaker Studio that provides an end-to-end solution for importing, preparing, transforming, featurizing, and analyzing data. Data Wrangler includes built-in analyses that help generate visualizations and data insights in a few clicks. One of the built-in analyses is the Quick Model visualization, which can be used to quickly evaluate the data and produce importance scores for each feature. A feature importance score indicates how useful a feature is at predicting a target label. The feature importance score is between [0, 1] and a higher number indicates that the feature is more important to the whole dataset. The Quick Model visualization uses a random forest model to calculate the feature importance for each feature using the Gini importance method. This method measures the total reduction in node impurity (a measure of how well a node separates the classes) that is attributed to splitting on a particular feature. The ML developer can use the Quick Model visualization to obtain the importance scores for each feature of the dataset and use them to feature engineer the dataset. This solution requires the least development effort compared to the other options.
References:
*Analyze and Visualize
*Detect multicollinearity, target leakage, and feature correlation with Amazon SageMaker Data Wrangler

NEW QUESTION # 276
......

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