Explanation: A convolutional neural network (CNN) is a type of deep learning model that can learn to extract features from images and perform tasks such as classification, segmentation, and detection1. ResNet is a popular CNN architecture that uses residual connections to overcome the problem of vanishing gradients and enable very deep networks2. For the task of predicting product quality based on sensor data, a CNN and ResNet approach can leverage the spatial structure of the data and learn complex patterns that distinguish different quality levels.

Explanation: To access an Amazon S3 bucket in another account that is encrypted using SSE-KMS, the following are required:

• A. An AWS KMS key policy that allows access to the customer master key (CMK). The CMK is the encryption key that is used to encrypt and decrypt the data in the S3 bucket. The KMS key policy defines who can use and manage the CMK. To allow access to the CMK from another account, the key policy must include a statement that grants the necessary permissions (such as kms:Decrypt) to the principal from the other account (such as the SageMaker notebook IAM role).
• B. A SageMaker notebook security group that allows access to Amazon S3. A security group is a virtual firewall that controls the inbound and outbound traffic for the SageMaker notebook instance. To allow the notebook instance to access the S3 bucket, the security group must have a rule that allows outbound traffic to the S3 endpoint on port 443 (HTTPS).
• C. An IAM role that allows access to the specific S3 bucket. An IAM role is an identity that can be assumed by the SageMaker notebook instance to access AWS resources. The IAM role must have a policy that grants the necessary permissions (such as s3:GetObject) to access the specific S3 bucket. The policy must also include a condition that allows access to the CMK in the other account.

• D. A permissive S3 bucket policy. A bucket policy is a resource-based policy that defines who can access the S3 bucket and what actions they can perform. A permissive bucket policy is not required and not recommended, as it can expose the bucket to unauthorized access. A bucket policy should follow the principle of least privilege and grant the minimum permissions necessary to the specific principals that need access.
• E. An S3 bucket owner that matches the notebook owner. The S3 bucket owner and the notebook owner do not need to match, as long as the bucket owner grants cross-account access to the notebook owner through the KMS key policy and the bucket policy (if applicable).
• F. A SegaMaker notebook subnet ACL that allow traffic to Amazon S3. A subnet ACL is a network access control list that acts as an optional layer of security for the SageMaker notebook instance’s subnet. A subnet ACL is not required to access the S3 bucket, as the security group is sufficient to control the traffic. However, if a subnet ACL is used, it must not block the traffic to the S3 endpoint.

Explanation: A residual plot is a type of plot that displays the values of a predictor variable in a regression model along the x-axis and the values of the residuals along the y-axis. This plot is used to assess whether or not the residuals in a regression model are normally distributed and whether or not they exhibit heteroscedasticity. Heteroscedasticity means that the variance of the residuals is not constant across different values of the predictor variable. This violates one of the assumptions of linear regression and can lead to biased estimates and unreliable predictions. The displayed residual plot shows a clear pattern of heteroscedasticity, as the residuals spread out as the fitted values increase. This indicates that linear regression is inappropriate for this data and a different model should be used.

Explanation: A pronunciation lexicon is a file that defines how words or phrases should be pronounced by Amazon Polly. A lexicon can help customize the speech output for words that are uncommon, foreign, or have multiple pronunciations. A lexicon must conform to the Pronunciation Lexicon Specification (PLS) standard and can be stored in an AWS region using the Amazon Polly API. To use a lexicon for synthesizing speech, the lexicon name must be specified in the SSML tag. For example, the following lexicon defines how to pronounce the acronym W3C:

• W3C World Wide Web Consortium

Explanation:
Data augmentation is a technique to increase the size and diversity of the training data by applying random transformations such as rotation, translation, scaling, flipping, etc. This can help reduce overfitting and improve the generalization of the model. Data augmentation can be done using the Amazon SageMaker image classification algorithm, which supports various augmentation options such as horizontal_flip, vertical_flip, rotate, brightness, contrast, etc1.
The Amazon SageMaker k-nearest neighbors (k-NN) algorithm is a supervised learning algorithm that can be used to label unlabeled data based on the similarity to the labeled data. The k-NN algorithm assigns a label to an unlabeled instance by finding the k closest labeled instances in the feature space and taking a majority vote among their labels. This can help increase the size and diversity of the training data and reduce overfitting. The k-NN algorithm can be used with the Amazon SageMaker image classification algorithm by extracting features from the images using a pre-trained model and then applying the k-NN algorithm on the feature vectors2.
Using Amazon SageMaker Ground Truth to label the unlabeled images is not a good option because it is a manual and costly process that requires human annotators. Moreover, it does not address the issue of overfitting on the existing labeled data.
Using image preprocessing to transform the images into grayscale images is not a good option because it reduces the amount of information and variation in the images, which can degrade the performance of the model. Moreover, it does not address the issue of overfitting on the existing labeled data.
Replacing the activation of the last layer with a sigmoid is not a good option because it is not suitable for a multi-class classification problem. A sigmoid activation function outputs a value between 0 and 1, which can be interpreted as a probability of belonging to a single class. However, for a multi-class classification problem, the output should be a vector of probabilities that sum up to 1, which can be achieved by using a softmax activation function.

Explanation: The best solution to reduce the cost of the notebook instance and the data preprocessing job is to change the notebook instance type to a smaller general-purpose instance, stop the notebook when it is not in use, and run data preprocessing on an ml.r5 instance with the same memory size as the ml.m5.4xlarge instance by using Amazon SageMaker Processing. This solution will result in the most cost savings because:

• Changing the notebook instance type to a smaller general-purpose instance will reduce the hourly cost of running the notebook, since the feature engineering development does not require high CPU and memory resources. For example, an ml.t3.medium instance costs $0.0464 per hour, while an ml.m5.4xlarge instance costs $0.888 per hour1.
• Stopping the notebook when it is not in use will also reduce the cost, since the notebook will only incur charges when it is running. For example, if the notebook is used for 8 hours per day, 5 days per week, then stopping it when it is not in use will save about 76% of the monthly cost compared to leaving it running all the time2.
• Running data preprocessing on an ml.r5 instance with the same memory size as the ml.m5.4xlarge instance by using Amazon SageMaker Processing will reduce the cost of the data preprocessing job, since the ml.r5 instance is optimized for memory-intensive workloads and has a lower cost per GB of memory than the ml.m5 instance. For example, an ml.r5.4xlarge instance has 128 GB of memory and costs $1.008 per hour, while an ml.m5.4xlarge instance has 64 GB of memory and costs $0.888 per hour1.

Explanation:

• The company wants to perform near-real time defect detection on a time-series of 200 performance metrics, and store all the data for offline analysis. The best approach for this scenario is to use Amazon Kinesis Data Firehose for ingestion and Amazon Kinesis Data Analytics Random Cut Forest (RCF) to perform anomaly detection. Use Kinesis Data Firehose to store data in Amazon S3 for further analysis.
• Amazon Kinesis Data Firehose is a service that can capture, transform, and deliver streaming data to destinations such as Amazon S3, Amazon Redshift, Amazon OpenSearch Service, and Splunk. Kinesis Data Firehose can handle any amount and frequency of data, and automatically scale to match the throughput. Kinesis Data Firehose can also compress, encrypt, and batch the data before delivering it to the destination, reducing the storage cost and enhancing the security.
• Amazon Kinesis Data Analytics is a service that can analyze streaming data in real time using SQL or Apache Flink applications. Kinesis Data Analytics can use builtin functions and algorithms to perform various analytics tasks, such as aggregations, joins, filters, windows, and anomaly detection. One of the built-in algorithms that Kinesis Data Analytics supports is Random Cut Forest (RCF), which is a supervised learning algorithm for forecasting scalar time series using recurrent neural networks. RCF can detect anomalies in streaming data by assigning an anomaly score to each data point, based on how distant it is from the rest of the data. RCF can handle multiple related time series, such as the performance metrics of the aircraft engine, and learn a global model that captures the common patterns and trends across the time series.

Explanation: The SageMaker CreatePresignedDomainUrl API is the best option to meet the requirements of the company. This API creates a URL for a specified UserProfile in a Domain. When accessed in a web browser, the user will be automatically signed in to the domain, and granted access to all of the Apps and files associated with the Domain’s Amazon Elastic File System (EFS) volume. This API can only be called when the authentication mode equals IAM, which means the company can use its existing IdP to authenticate users.
The company can use the DynamoDB table to store the domain IDs and user profile names for each department, and use the proxy application to query the table and generate the presigned URL for the appropriate domain according to the user’s credentials. The presigned URL is valid only for a specified duration, which can be set by the SessionExpirationDurationInSeconds parameter. This can help enhance the security and prevent unauthorized access to the domains.
The other options are not suitable for the company’s requirements. The SageMaker CreateHumanTaskUi API is used to define the settings for the human review workflow user interface, which is not related to accessing the SageMaker Studio domains. The SageMaker ListHumanTaskUis API is used to return information about the human task user interfaces in the account, which is also not relevant to the company’s use case. The SageMaker CreatePresignedNotebookInstanceUrl API is used to create a URL to connect to the Jupyter server from a notebook instance, which is different from accessing the SageMaker Studio domain.

Explanation: Amazon Comprehend is a natural language processing (NLP) service that can perform part-of-speech tagging and key phrase extraction tasks. AWS Deep Learning Containers are Docker images that are pre-installed with popular deep learning frameworks such as Apache MXNet. Amazon SageMaker is a fully managed service that can help build, train, and deploy machine learning models. Using Amazon Comprehend for the text preprocessing tasks and AWS Deep Learning Containers with Amazon SageMaker to build the custom classifier is the solution that can be built most quickly to meet the requirements.

Explanation: The problem of detecting specific types of weeds and their location within the field is an example of object detection, which is a type of machine learning model that identifies and localizes objects in an image. Amazon SageMaker provides a built-in object detection algorithm that uses a single-shot multibox detector (SSD) to perform real-time inference on streaming images. The SSD algorithm can handle multiple objects of varying sizes and scales in an image, and generate bounding boxes and scores for each object category. Therefore, option C is the best approach to obtain accurate predictions.

• Option A is incorrect because image classification is a type of machine learning model that assigns a label to an image based on predefined categories. Image classification is not suitable for localizing objects within an image, as it does not provide bounding boxes or scores for each object.
• Option B is incorrect because Apache Parquet is a columnar storage format that is optimized for analytical queries. Apache Parquet is not suitable for storing images, as it does not preserve the spatial information of the pixels.
• Option D is incorrect because it combines the wrong format (Apache Parquet) and the wrong algorithm (image classification) for the given problem, as explained in options A and B.

Explanation: 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.

Explanation: 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.