Free AWS-Certified-Machine-Learning-Specialty Exam Braindumps

A Data Scientist wants to gain real-time insights into a data stream of GZIP files. Which solution would allow the use of SQL to query the stream with the LEAST latency?

1. A. Amazon Kinesis Data Analytics with an AWS Lambda function to transform the data.
2. B. AWS Glue with a custom ETL script to transform the data.
3. C. An Amazon Kinesis Client Library to transform the data and save it to an Amazon ES cluster.
4. D. Amazon Kinesis Data Firehose to transform the data and put it into an Amazon S3 bucket.

Correct Answer: A

A Machine Learning Specialist is building a logistic regression model that will predict whether or not a person will order a pizza. The Specialist is trying to build the optimal model with an ideal classification threshold.
What model evaluation technique should the Specialist use to understand how different classification thresholds will impact the model's performance?

1. A. Receiver operating characteristic (ROC) curve
2. B. Misclassification rate
3. C. Root Mean Square Error (RM&)
4. D. L1 norm

Correct Answer: A

A health care company is planning to use neural networks to classify their X-ray images into normal and abnormal classes. The labeled data is divided into a training set of 1,000 images and a test set of 200 images. The initial training of a neural network model with 50 hidden layers yielded 99

1. A. Choose a higher number of layers
2. B. Choose a lower number of layers
3. C. Choose a smaller learning rate
4. D. Enable dropout
5. E. Include all the images from the test set in the training set
6. F. Enable early stopping

Correct Answer: ADE

A data scientist is using the Amazon SageMaker Neural Topic Model (NTM) algorithm to build a model that recommends tags from blog posts. The raw blog post data is stored in an Amazon S3 bucket in JSON format. During model evaluation, the data scientist discovered that the model recommends certain stopwords such as "a," "an,” and "the" as tags to certain blog posts, along with a few rare words that are present only in certain blog entries. After a few iterations of tag review with the content team, the data scientist notices that the rare words are unusual but feasible. The data scientist also must ensure that the tag recommendations of the generated model do not include the stopwords.
What should the data scientist do to meet these requirements?

1. A. Use the Amazon Comprehend entity recognition API operation
2. B. Remove the detected words from the blog post dat
3. C. Replace the blog post data source in the S3 bucket.
4. D. Run the SageMaker built-in principal component analysis (PCA) algorithm with the blog post data from the S3 bucket as the data sourc
5. E. Replace the blog post data in the S3 bucket with the results of the training job.
6. F. Use the SageMaker built-in Object Detection algorithm instead of the NTM algorithm for the training job to process the blog post data.
7. G. Remove the stopwords from the blog post data by using the Count Vectorizer function in the scikit-learnlibrar
8. H. Replace the blog post data in the S3 bucket with the results of the vectorizer.

Correct Answer: D

A Machine Learning Specialist needs to create a data repository to hold a large amount of time-based training data for a new model. In the source system, new files are added every hour Throughout a single 24-hour period, the volume of hourly updates will change significantly. The Specialist always wants to train on the last 24 hours of the data
Which type of data repository is the MOST cost-effective solution?

1. A. An Amazon EBS-backed Amazon EC2 instance with hourly directories
2. B. An Amazon RDS database with hourly table partitions
3. C. An Amazon S3 data lake with hourly object prefixes
4. D. An Amazon EMR cluster with hourly hive partitions on Amazon EBS volumes

Correct Answer: C