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Amazon AWS Certified Machine Learning - Specialty Sample Questions (Q306-Q311):

NEW QUESTION # 306
A wildlife research company has a set of images of lions and cheetahs. The company created a dataset of the images. The company labeled each image with a binary label that indicates whether an image contains a lion or cheetah. The company wants to train a model to identify whether new images contain a lion or cheetah.
.... Dh Amazon SageMaker algorithm will meet this requirement?

A. Object Detection - TensorFlow
B. XGBoost
C. Semantic segmentation - MXNet
D. Image Classification - TensorFlow

Answer: D

Explanation:
The best Amazon SageMaker algorithm for this task is Image Classification - TensorFlow. This algorithm is a supervised learning algorithm that supports transfer learning with many pretrained models from the TensorFlow Hub. Transfer learning allows the company to fine-tune one of the available pretrained models on their own dataset, even if a large amount of image data is not available. The image classification algorithm takes an image as input and outputs a probability for each provided class label. The company can choose from a variety of models, such as MobileNet, ResNet, or Inception, depending on their accuracy and speed requirements. The algorithm also supports distributed training, data augmentation, and hyperparameter tuning.
Image Classification - TensorFlow - Amazon SageMaker
Amazon SageMaker Provides New Built-in TensorFlow Image Classification Algorithm Image Classification with ResNet :: Amazon SageMaker Workshop Image classification on Amazon SageMaker | by Julien Simon - Medium

NEW QUESTION # 307
A pharmaceutical company performs periodic audits of clinical trial sites to quickly resolve critical findings.
The company stores audit documents in text format. Auditors have requested help from a data science team to quickly analyze the documents. The auditors need to discover the 10 main topics within the documents to prioritize and distribute the review work among the auditing team members. Documents that describe adverse events must receive the highest priority.
A data scientist will use statistical modeling to discover abstract topics and to provide a list of the top words for each category to help the auditors assess the relevance of the topic.
Which algorithms are best suited to this scenario? (Choose two.)

A. Linear support vector machine
B. Linear regression
C. Random Forest classifier
D. Neural topic modeling (NTM)
E. Latent Dirichlet allocation (LDA)

Answer: D,E

Explanation:
The algorithms that are best suited to this scenario are latent Dirichlet allocation (LDA) and neural topic modeling (NTM), as they are both unsupervised learning methods that can discover abstract topics from a collection of text documents. LDA and NTM can provide a list of the top words for each topic, as well as the topic distribution for each document, which can help the auditors assess the relevance and priority of the topic12.
The other options are not suitable because:
* Option B: A random forest classifier is a supervised learning method that can perform classification or regression tasks by using an ensemble of decision trees. A random forest classifier is not suitable for discovering abstract topics from text documents, as it requires labeled data and predefined classes3.
* Option D: A linear support vector machine is a supervised learning method that can perform classification or regression tasks by using a linear function that separates the data into different classes. A linear support vector machine is not suitable for discovering abstract topics from text documents, as it requires labeled data and predefined classes4.
* Option E: A linear regression is a supervised learning method that can perform regression tasks by using a linear function that models the relationship between a dependent variable and one or more independent variables. A linear regression is not suitable for discovering abstract topics from text documents, as it requires labeled data and a continuous output variable5.
1: Latent Dirichlet Allocation
2: Neural Topic Modeling
3: Random Forest Classifier
4: Linear Support Vector Machine
5: Linear Regression

NEW QUESTION # 308
An aircraft engine manufacturing company is measuring 200 performance metrics in a time-series. Engineers want to detect critical manufacturing defects in near-real time during testing. All of the data needs to be stored for offline analysis.
What approach would be the MOST effective to perform near-real time defect detection?

A. Use Amazon S3 for ingestion, storage, and further analysis. Use an Amazon EMR cluster to carry out Apache Spark ML k-means clustering to determine anomalies.
B. 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.
C. Use AWS IoT Analytics for ingestion, storage, and further analysis. Use Jupyter notebooks from within AWS IoT Analytics to carry out analysis for anomalies.
D. Use Amazon S3 for ingestion, storage, and further analysis. Use the Amazon SageMaker Random Cut Forest (RCF) algorithm to determine anomalies.

Answer: B

Explanation:
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 built-in 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.
Therefore, the company can use the following architecture to build the near-real time defect detection solution:
Use Amazon Kinesis Data Firehose for ingestion: The company can use Kinesis Data Firehose to capture the streaming data from the aircraft engine testing, and deliver it to two destinations:
Amazon S3 and Amazon Kinesis Data Analytics. The company can configure the Kinesis Data Firehose delivery stream to specify the source, the buffer size and interval, the compression and encryption options, the error handling and retry logic, and the destination details.
Use Amazon Kinesis Data Analytics Random Cut Forest (RCF) to perform anomaly detection:
The company can use Kinesis Data Analytics to create a SQL application that can read the streaming data from the Kinesis Data Firehose delivery stream, and apply the RCF algorithm to detect anomalies. The company can use the RANDOM_CUT_FOREST or RANDOM_CUT_FOREST_WITH_EXPLANATION functions to compute the anomaly scores and attributions for each data point, and use the WHERE clause to filter out the normal data points. The company can also use the CURSOR function to specify the input stream, and the PUMP function to write the output stream to another destination, such as Amazon Kinesis Data Streams or AWS Lambda.
Use Kinesis Data Firehose to store data in Amazon S3 for further analysis: The company can use Kinesis Data Firehose to store the raw and processed data in Amazon S3 for offline analysis. The company can use the S3 destination of the Kinesis Data Firehose delivery stream to store the raw data, and use another Kinesis Data Firehose delivery stream to store the output of the Kinesis Data Analytics application. The company can also use AWS Glue or Amazon Athena to catalog, query, and analyze the data in Amazon S3.
References:
What Is Amazon Kinesis Data Firehose?
What Is Amazon Kinesis Data Analytics for SQL Applications?
DeepAR Forecasting Algorithm - Amazon SageMaker

NEW QUESTION # 309
A Machine Learning Specialist is working with a large company to leverage machine learning within its products. The company wants to group its customers into categories based on which customers will and will not churn within the next 6 months. The company has labeled the data available to the Specialist.
Which machine learning model type should the Specialist use to accomplish this task?

A. Reinforcement learning
B. Linear regression
C. Clustering
D. Classification

Answer: D

Explanation:
The goal of classification is to determine to which class or category a data point (customer in our case) belongs to. For classification problems, data scientists would use historical data with predefined target variables AKA labels (churner/non-churner) - answers that need to be predicted - to train an algorithm. With classification, businesses can answer the following questions:
Will this customer churn or not?
Will a customer renew their subscription?
Will a user downgrade a pricing plan?
Are there any signs of unusual customer behavior?
Reference: https://www.kdnuggets.com/2019/05/churn-prediction-machine-learning.html

NEW QUESTION # 310
A Machine Learning Specialist deployed a model that provides product recommendations on a company's website. Initially, the model was performing very well and resulted in customers buying more products on average. However, within the past few months, the Specialist has noticed that the effect of product recommendations has diminished and customers are starting to return to their original habits of spending less.
The Specialist is unsure of what happened, as the model has not changed from its initial deployment over a year ago.
Which method should the Specialist try to improve model performance?

A. The model needs to be completely re-engineered because it is unable to handle product inventory changes.
B. The model should be periodically retrained from scratch using the original data while adding a regularization term to handle product inventory changes
C. The model should be periodically retrained using the original training data plus new data as product inventory changes.
D. The model's hyperparameters should be periodically updated to prevent drift.

Answer: C

NEW QUESTION # 311
......

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Amazon AWS Certified Machine Learning - Specialty Sample Questions (Q306-Q311):

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