Free MLS-C01 Exam Braindumps

A Machine Learning Specialist is building a prediction model for a large number of features using linear models, such as linear regression and logistic regression During exploratory data analysis the Specialist observes that many features are highly correlated with each other This may make the model unstable
What should be done to reduce the impact of having such a large number of features?

1. A. Perform one-hot encoding on highly correlated features
2. B. Use matrix multiplication on highly correlated features.
3. C. Create a new feature space using principal component analysis (PCA)
4. D. Apply the Pearson correlation coefficient

Correct Answer: C

A company's Machine Learning Specialist needs to improve the training speed of a time-series forecasting model using TensorFlow. The training is currently implemented on a single-GPU machine and takes approximately 23 hours to complete. The training needs to be run daily.
The model accuracy js acceptable, but the company anticipates a continuous increase in the size of the training data and a need to update the model on an hourly, rather than a daily, basis. The company also wants to minimize coding effort and infrastructure changes
What should the Machine Learning Specialist do to the training solution to allow it to scale for future demand?

1. A. Do not change the TensorFlow cod
2. B. Change the machine to one with a more powerful GPU to speed up the training.
3. C. Change the TensorFlow code to implement a Horovod distributed framework supported by Amazon SageMake
4. D. Parallelize the training to as many machines as needed to achieve the business goals.
5. E. Switch to using a built-in AWS SageMaker DeepAR mode
6. F. Parallelize the training to as many machines as needed to achieve the business goals.
7. G. Move the training to Amazon EMR and distribute the workload to as many machines as needed to achieve the business goals.

Correct Answer: B

A Machine Learning Specialist is developing recommendation engine for a photography blog Given a picture, the recommendation engine should show a picture that captures similar objects The Specialist would like to create a numerical representation feature to perform nearest-neighbor searches
What actions would allow the Specialist to get relevant numerical representations?

1. A. Reduce image resolution and use reduced resolution pixel values as features
2. B. Use Amazon Mechanical Turk to label image content and create a one-hot representation indicating the presence of specific labels
3. C. Run images through a neural network pie-trained on ImageNet, and collect the feature vectors from the penultimate layer
4. D. Average colors by channel to obtain three-dimensional representations of images.

Correct Answer: A

A Machine Learning Specialist is designing a system for improving sales for a company. The objective is to use the large amount of information the company has on users' behavior and product preferences to predict which products users would like based on the users' similarity to other users.
What should the Specialist do to meet this objective?

1. A. Build a content-based filtering recommendation engine with Apache Spark ML on Amazon EMR.
2. B. Build a collaborative filtering recommendation engine with Apache Spark ML on Amazon EMR.
3. C. Build a model-based filtering recommendation engine with Apache Spark ML on Amazon EMR.
4. D. Build a combinative filtering recommendation engine with Apache Spark ML on Amazon EMR.

Correct Answer: B
Many developers want to implement the famous Amazon model that was used to power the “People who bought this also bought these items” feature on Amazon.com. This model is based on a method called Collaborative Filtering. It takes items such as movies, books, and products that were rated highly by a set of users and recommending them to other users who also gave them high ratings. This method works well in domains where explicit ratings or implicit user actions can be gathered and analyzed.

A Machine Learning Specialist has completed a proof of concept for a company using a small data sample and now the Specialist is ready to implement an end-to-end solution in AWS using Amazon SageMaker The historical training data is stored in Amazon RDS
Which approach should the Specialist use for training a model using that data?

1. A. Write a direct connection to the SQL database within the notebook and pull data in
2. B. Push the data from Microsoft SQL Server to Amazon S3 using an AWS Data Pipeline and provide the S3 location within the notebook.
3. C. Move the data to Amazon DynamoDB and set up a connection to DynamoDB within the notebook to pull data in
4. D. Move the data to Amazon ElastiCache using AWS DMS and set up a connection within the notebook to pull data in for fast access.

Correct Answer: B