AWS-Certified-Machine-Learning-Specialty Free Practice Test

A manufacturer of car engines collects data from cars as they are being driven The data collected includes timestamp, engine temperature, rotations per minute (RPM), and other sensor readings The company wants to predict when an engine is going to have a problem so it can notify drivers in advance to get engine maintenance The engine data is loaded into a data lake for training
Which is the MOST suitable predictive model that can be deployed into production'?

1. A. Add labels over time to indicate which engine faults occur at what time in the future to turn this into a supervised learning problem Use a recurrent neural network (RNN) to train the model to recognize when an engine might need maintenance for a certain fault.
2. B. This data requires an unsupervised learning algorithm Use Amazon SageMaker k-means to cluster the data
3. C. Add labels over time to indicate which engine faults occur at what time in the future to turn this into a supervised learning problem Use a convolutional neural network (CNN) to train the model to recognize when an engine might need maintenance for a certain fault.
4. D. This data is already formulated as a time series Use Amazon SageMaker seq2seq to model the time series.

Correct Answer: B

A machine learning (ML) specialist must develop a classification model for a financial services company. A domain expert provides the dataset, which is tabular with 10,000 rows and 1,020 features. During exploratory data analysis, the specialist finds no missing values and a small percentage of duplicate rows. There are correlation scores of > 0.9 for 200 feature pairs. The mean value of each feature is similar to its 50th percentile.
Which feature engineering strategy should the ML specialist use with Amazon SageMaker?

1. A. Apply dimensionality reduction by using the principal component analysis (PCA) algorithm.
2. B. Drop the features with low correlation scores by using a Jupyter notebook.
3. C. Apply anomaly detection by using the Random Cut Forest (RCF) algorithm.
4. D. Concatenate the features with high correlation scores by using a Jupyter notebook.

Correct Answer: C

A Machine Learning Specialist previously trained a logistic regression model using scikit-learn on a local machine, and the Specialist now wants to deploy it to production for inference only.
What steps should be taken to ensure Amazon SageMaker can host a model that was trained locally?

1. A. Build the Docker image with the inference cod
2. B. Tag the Docker image with the registry hostname and upload it to Amazon ECR.
3. C. Serialize the trained model so the format is compressed for deploymen
4. D. Tag the Docker image with the registry hostname and upload it to Amazon S3.
5. E. Serialize the trained model so the format is compressed for deploymen
6. F. Build the image and upload it to Docker Hub.
7. G. Build the Docker image with the inference cod
8. H. Configure Docker Hub and upload the image to AmazonECR.

Correct Answer: D

A company is launching a new product and needs to build a mechanism to monitor comments about the company and its new product on social media. The company needs to be able to evaluate the sentiment expressed in social media posts, and visualize trends and configure alarms based on various thresholds.
The company needs to implement this solution quickly, and wants to minimize the infrastructure and data science resources needed to evaluate the messages. The company already has a solution in place to collect posts and store them within an Amazon S3 bucket.
What services should the data science team use to deliver this solution?

1. A. Train a model in Amazon SageMaker by using the BlazingText algorithm to detect sentiment in the corpus of social media post
2. B. Expose an endpoint that can be called by AWS Lambd
3. C. Trigger a Lambda function when posts are added to the S3 bucket to invoke the endpoint and record the sentiment in an Amazon DynamoDB table and in a custom Amazon CloudWatch metri
4. D. Use CloudWatch alarms to notify analysts of trends.
5. E. Train a model in Amazon SageMaker by using the semantic segmentation algorithm to model the semantic content in the corpus of social media post
6. F. Expose an endpoint that can be called by AWS Lambd
7. G. Trigger a Lambda function when objects are added to the S3 bucket to invoke the endpoint and record the sentiment in an Amazon DynamoDB tabl
8. H. Schedule a second Lambda function to query recently added records and send an Amazon Simple Notification Service (Amazon SNS) notification to notify analysts of trends.
9. I. Trigger an AWS Lambda function when social media posts are added to the S3 bucke
10. J. Call Amazon Comprehend for each post to capture the sentiment in the message and record the sentiment in an Amazon DynamoDB tabl
11. K. Schedule a second Lambda function to query recently added records and send an Amazon Simple Notification Service (Amazon SNS) notification to notify analysts of trends.
12. L. Trigger an AWS Lambda function when social media posts are added to the S3 bucke
13. M. Call Amazon Comprehend for each post to capture the sentiment in the message and record the sentiment in a custom Amazon CloudWatch metric and in S3. Use CloudWatch alarms to notify analysts of trends.

Correct Answer: A

A company wants to use automatic speech recognition (ASR) to transcribe messages that are less than 60 seconds long from a voicemail-style application. The company requires the correct identification of 200 unique product names, some of which have unique spellings or pronunciations.
The company has 4,000 words of Amazon SageMaker Ground Truth voicemail transcripts it can use to customize the chosen ASR model. The company needs to ensure that everyone can update their customizations multiple times each hour.
Which approach will maximize transcription accuracy during the development phase?

1. A. Use a voice-driven Amazon Lex bot to perform the ASR customizatio
2. B. Create customer slots within the bot that specifically identify each of the required product name
3. C. Use the Amazon Lex synonym mechanism to provide additional variations of each product name as mis-transcriptions are identified in development.
4. D. Use Amazon Transcribe to perform the ASR customizatio
5. E. Analyze the word confidence scores in the transcript, and automatically create or update a custom vocabulary file with any word that has a confidence score below an acceptable threshold valu
6. F. Use this updated custom vocabulary file in all future transcription tasks.
7. G. Create a custom vocabulary file containing each product name with phonetic pronunciations, and use it with Amazon Transcribe to perform the ASR customizatio
8. H. 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.
9. I. Use the audio transcripts to create a training dataset and build an Amazon Transcribe custom language mode
10. J. Analyze the transcripts and update the training dataset with a manually corrected version of transcripts where product names are not being transcribed correctl
11. K. Create an updated custom language model.

Correct Answer: A