Free Amazon Web Services MLS-C01 Practice Exam with Questions & Answers | Set: 4

AWS IoT Greengrass is a service that extends AWS to edge devices, such as sensors and machines, so they can act locally on the data they generate, while still using the cloud for management, analytics, and durable storage. AWS IoT Greengrass enables local device messaging, secure data transfer, and local computing using AWS Lambda functions and machine learning models. AWS IoT Greengrass can run machine learning inference locally on devices using models that are created and trained in the cloud. This allows devices to respond quickly to local events, even when they are offline or have intermittent connectivity. Therefore, option B is the best deployment architecture for the model to address the business requirements of the manufacturer.

Option A is incorrect because deploying the model in Amazon SageMaker would require sending the sensor data to the cloud for inference, which would not work well for factory locations that do not have reliable or high-speed internet connectivity. Moreover, this option would not provide near-real-time inference capabilities, as there would be latency and bandwidth issues involved in transferring the data to and from the cloud. Option C is incorrect because deploying the model to an Amazon SageMaker batch transformation job would not provide near-real-time inference capabilities, as batch transformation is an asynchronous process that operates on large datasets. Batch transformation is not suitable for streaming data that requires low-latency responses. Option D is incorrect because deploying the model in Amazon SageMaker and using an IoT rule to write data to an Amazon DynamoDB table would also require sending the sensor data to the cloud for inference, which would have the same drawbacks as option A. Moreover, this option would introduce additional complexity and cost by involving multiple services, such as IoT Core, DynamoDB, and Lambda.

AWS Greengrass Machine Learning Inference - Amazon Web Services

Machine learning components - AWS IoT Greengrass

What is AWS Greengrass? | AWS IoT Core | Onica

GitHub - aws-samples/aws-greengrass-ml-deployment-sample

AWS IoT Greengrass Architecture and Its Benefits | Quick Guide - XenonStack

The solution D meets the requirements with the least operational overhead because it uses Amazon SageMaker Autopilot, which is a fully managed service that automates the end-to-end process of building, training, and deploying machine learning models. Amazon SageMaker Autopilot can handle data preprocessing, feature engineering, algorithm selection, hyperparameter tuning, and model deployment. The company only needs to create an IAM role for Amazon SageMaker with access to the S3 bucket, create a SageMaker AutoML job pointing to the bucket with the dataset, specify the price as the target attribute, and wait for the job to complete. Amazon SageMaker Autopilot will generate a list of candidate models with different configurations and performance metrics, and the company can deploy the best model for predictions1.

The other options are not suitable because:

Option A: Creating a service-linked role for Amazon Elastic Container Service (Amazon ECS) with access to the S3 bucket, creating an ECS cluster based on an AWS Deep Learning Containers image, writing the code to perform the feature engineering, training a logistic regression model for predicting the price, and performing the inferences will incur more operational overhead than using Amazon SageMaker Autopilot. The company will have to manage the ECS cluster, the container image, the code, the model, and the inference endpoint. Moreover, logistic regression may not be the best algorithm for predicting the price, as it is more suitable for binary classification tasks2.

Option B: Creating an Amazon SageMaker notebook with a new IAM role that is associated with the notebook, pulling the dataset from the S3 bucket, exploring different combinations of feature engineering transformations, regression algorithms, and hyperparameters, comparing all the results in the notebook, and deploying the most accurate configuration in an endpoint for predictions will incur more operational overhead than using Amazon SageMaker Autopilot. The company will have to write the code for the feature engineering, the model training, the model evaluation, and the model deployment. The company will also have to manually compare the results and select the best configuration3.

Option C: Creating an IAM role with access to Amazon S3, Amazon SageMaker, and AWS Lambda, creating a training job with the SageMaker built-in XGBoost model pointing to the bucket with the dataset, specifying the price as the target feature, loading the model artifact to a Lambda function for inference on prices of new houses will incur more operational overhead than using Amazon SageMaker Autopilot. The company will have to create and manage the Lambda function, the model artifact, and the inference endpoint. Moreover, XGBoost may not be the best algorithm for predicting the price, as it is more suitable for classification and ranking tasks4.

1: Amazon SageMaker Autopilot

2: Amazon Elastic Container Service

3: Amazon SageMaker Notebook Instances

4: Amazon SageMaker XGBoost Algorithm

Amazon Personalize is a fully managed machine learning service that makes it easy for developers to create personalized user experiences at scale. It uses the same recommender system technology that Amazon uses to create its own personalized recommendations. Amazon Personalize provides several pre-built recipes that can be used to train models for different use cases. The USER_PERSONALIZATION recipe is designed to provide personalized recommendations for existing users based on their past interactions with items. The PERSONALIZED_RANKING recipe is designed to re-rank a list of items for a user based on their preferences. The USER_PERSONALIZATION recipe is more suitable for this use case because it can generate recommendations for each user without requiring a list of candidate items. To filter out the items that the user previously purchased, a real-time filter can be created and applied to the campaign. A real-time filter is a dynamic filter that uses the latest interaction data to exclude items from the recommendations. By using Amazon Personalize, the development effort is minimized because it handles the data processing, model training, and deployment automatically. The web application can use the GetRecommendations API operation to get the real-time recommendations from the campaign. References:

Amazon Personalize

What is Amazon Personalize?

USER_PERSONALIZATION recipe

PERSONALIZED_RANKING recipe

Filtering recommendations

GetRecommendations API operation

Amazon Comprehend is a natural language processing (NLP) service that uses machine learning to find insights and relationships in text. It can analyze text documents and identify the key topics, entities, sentiments, languages, and more. In this case, Amazon Comprehend can be used with the transcribed files from Amazon Transcribe to extract the main topics that are being discussed by the call operators. This can help to understand the common issues and concerns of the customers, and provide insights for further analysis and improvement. References:

Amazon Comprehend - Amazon Web Services

AWS Certified Machine Learning - Specialty Sample Questions

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

Collaborative filtering is a machine learning technique that recommends products or services to users based on the ratings or preferences of other users. This technique is well-suited for identifying customer shopping patterns and preferences because it takes into account the interactions between users and products.

 The best approach to maximize transcription accuracy during the development phase is to create a custom vocabulary file containing each product name with phonetic pronunciations, and use it with Amazon Transcribe to perform the ASR customization. A custom vocabulary is a list of words and phrases that are likely to appear in your audio input, along with optional information about how to pronounce them. By using a custom vocabulary, you can improve the transcription accuracy of domain-specific terms, such as product names, that may not be recognized by the general vocabulary of Amazon Transcribe. You can also analyze the transcripts and manually update the custom vocabulary file to include updated or additional entries for those names that are not being correctly identified.

The other options are not as effective as option C for the following reasons:

Option A is not suitable because Amazon Lex is a service for building conversational interfaces, not for transcribing voicemail messages. Amazon Lex also has a limit of 100 slots per bot, which is not enough to accommodate the 200 unique product names required by the company.

Option B is not optimal because it relies on the word confidence scores in the transcript, which may not be accurate enough to identify all the mis-transcribed product names. Moreover, automatically creating or updating a custom vocabulary file may introduce errors or inconsistencies in the pronunciation or display of the words.

Option D is not feasible because it requires a large amount of training data to build a custom language model. The company only has 4,000 words of Amazon SageMaker Ground Truth voicemail transcripts, which is not enough to train a robust and reliable custom language model. Additionally, creating and updating a custom language model is a time-consuming and resource-intensive process, which may not be suitable for the development phase where frequent changes are expected.

Amazon Transcribe – Custom Vocabulary

Amazon Transcribe – Custom Language Models

[Amazon Lex – Limits]

Amazon Athena is an interactive query service that allows you to analyze data stored in Amazon S3 using standard SQL. Athena is serverless, so you only pay for the queries that you run and there is no infrastructure to manage.

To optimize the query performance of Athena, one of the best practices is to convert the data into a columnar format, such as Apache Parquet or Apache ORC. Columnar formats store data by columns rather than by rows, which allows Athena to scan only the columns that are relevant to the query, reducing the amount of data read and improving the query speed. Columnar formats also support compression and encoding schemes that can reduce the storage space and the data scanned per query, further enhancing the performance and reducing the cost.

In contrast, plaintext CSV files store data by rows, which means that Athena has to scan the entire row even if only a few columns are needed for the query. This increases the amount of data read and the query latency. Moreover, plaintext CSV files do not support compression or encoding, which means that they take up more storage space and incur higher query costs.

Therefore, the Machine Learning Specialist should transform the dataset to Apache Parquet format to minimize query runtime.

Top 10 Performance Tuning Tips for Amazon Athena

Columnar Storage Formats

Using compressions will reduce the amount of data scanned by Amazon Athena, and also reduce your S3 bucket storage. It’s a Win-Win for your AWS bill. Supported formats: GZIP, LZO, SNAPPY (Parquet) and ZLIB.

 The issue is that the notebook instances’ security group allows inbound traffic from any source IP address, which means that anyone with the authorized URL can access the notebook instances over the internet. To fix this issue, the data science team should modify the security group to allow traffic only from the CIDR ranges of the VPC, which are the IP addresses assigned to the resources within the VPC. This way, only the VPC interface endpoints and the resources within the VPC can communicate with the notebook instances. The data science team should apply this security group to all of the notebook instances’ VPC interfaces, which are the network interfaces that connect the notebook instances to the VPC.

The other options are not correct because:

Option B: Creating an IAM policy that allows the sagemaker:CreatePresignedNotebookInstanceUrl and sagemaker:DescribeNotebookInstance actions from only the VPC endpoints does not prevent individuals outside the VPC from accessing the notebook instances. These actions are used to generate and retrieve the authorized URL for the notebook instances, but they do not control who can use the URL to access the notebook instances. The URL can still be shared or leaked to unauthorized users, who can then access the notebook instances over the internet.

Option C: Adding a NAT gateway to the VPC and converting the subnets where the notebook instances are hosted to private subnets does not solve the issue either. A NAT gateway is used to enable outbound internet access from a private subnet, but it does not affect inbound internet access. The notebook instances can still be accessed over the internet if their security group allows inbound traffic from any source IP address. Moreover, stopping and starting the notebook instances to reassign only private IP addresses is not necessary, because the notebook instances already have private IP addresses assigned by the VPC interface endpoints.

Option D: Changing the network ACL of the subnet the notebook is hosted in to restrict access to anyone outside the VPC is not a good practice, because network ACLs are stateless and apply to the entire subnet. This means that the data science team would have to specify both the inbound and outbound rules for each IP address range that they want to allow or deny. This can be cumbersome and error-prone, especially if the VPC has multiple subnets and resources. It is better to use security groups, which are stateful and apply to individual resources, to control the access to the notebook instances.

Connect to SageMaker Within your VPC - Amazon SageMaker

Security Groups for Your VPC - Amazon Virtual Private Cloud

VPC Interface Endpoints - Amazon Virtual Private Cloud

The data scientist should remove the stop words from the blog post data by using the Count Vectorizer function in the scikit-learn library, and replace the blog post data in the S3 bucket with the results of the vectorizer. This is because:

The Count Vectorizer function is a tool that can convert a collection of text documents to a matrix of token counts 1. It also enables the pre-processing of text data prior to generating the vector representation, such as removing accents, converting to lowercase, and filtering out stop words 1. By using this function, the data scientist can remove the stop words such as “a,” “an,” and “the” from the blog post data, and obtain a numerical representation of the text that can be used as input for the NTM algorithm.

The NTM algorithm is a neural network-based topic modeling technique that can learn latent topics from a corpus of documents 2. It can be used to recommend tags from blog posts by finding the most probable topics for each document, and ranking the words associated with each topic 3. However, the NTM algorithm does not perform any text pre-processing by itself, so it relies on the quality of the input data. Therefore, the data scientist should replace the blog post data in the S3 bucket with the results of the vectorizer, to ensure that the NTM algorithm does not include the stop words in the tag recommendations.

The other options are not suitable for the following reasons:

Option A is not relevant because the Amazon Comprehend entity recognition API operations are used to detect and extract named entities from text, such as people, places, organizations, dates, etc4. This is not the same as removing stop words, which are common words that do not carry much meaning or information. Moreover, removing the detected entities from the blog post data may reduce the quality and diversity of the tag recommendations, as some entities may be relevant and useful as tags.

Option B is not optimal because the SageMaker built-in principal component analysis (PCA) algorithm is used to reduce the dimensionality of a dataset by finding the most important features that capture the maximum amount of variance in the data 5. This is not the same as removing stop words, which are words that have low variance and high frequency in the data. Moreover, replacing the blog post data in the S3 bucket with the results of the PCA algorithm may not be compatible with the input format expected by the NTM algorithm, which requires a bag-of-words representation of the text 2.

Option C is not suitable because the SageMaker built-in Object Detection algorithm is used to detect and localize objects in images 6. This is not related to the task of recommending tags from blog posts, which are text documents. Moreover, using the Object Detection algorithm instead of the NTM algorithm would require a different type of input data (images instead of text), and a different type of output data (bounding boxes and labels instead of topics and words).

Neural Topic Model (NTM) Algorithm

Introduction to the Amazon SageMaker Neural Topic Model

Amazon Comprehend - Entity Recognition

sklearn.feature_extraction.text.CountVectorizer

Principal Component Analysis (PCA) Algorithm

Object Detection Algorithm