Question 1

This graph shows the training and validation loss against the epochs for a neural network The network being trained is as follows

- Two dense layers one output neuron
- 100 neurons in each layer
- 100 epochs
- Random initialization of weights

Which technique can be used to improve model performance in terms of accuracy in the validation set?

Accepted Answer

Correct answer: D

Question 2

A machine learning (ML) specialist is using Amazon SageMaker hyperparameter optimization (HPO) to improve a model's accuracy. The learning rate paramete is specified in the following HPO configuration:

```
11 Name earning rate MaxValue 0.0001 "MinValue 0 1
```

During the results analysis, the ML specialist determines that most of the training jobs had a learning rate between 0.01 and 0.1. The best result had a rate of less than 0.01. Training jobs need to run regularly over a changing dataset. The ML specialist needs to find a tuning mechanism that uses differ rates more evenly from the provided range between MinValue and MaxValue. Which solution provides the MOST accurate result?

A. Modify the HPO configuration as follows: C:\Users\Admin\Desktop\Data\Odt data\Untitled.jpgSelect the most accurate hyperparameter configuration form HPO job.

```
"Name" "learning_rate" MaxValue 0.0001" "MinValue 0.1 ScalingType "ReverseLogarithmic"
```

B. Run three different HPO jobs that use different learning rates form the following intervals for MinValue and MaxValue while using the same number of jobs for each HPO job:[0.01, 0.1][0.001, 0.01][0.0001, 0.001]Select the most accurate hyperparameter configuration form these three HPO jobs. C. Modify the HPO configuration as follows: C:\Users\Admin\Desktop\Data\Odt data\Untitled.jpg

Select the most accurate hyperparameter configuration form this training job.

Accepted Answer

Correct answer: C

Question 3

A company will use Amazon SageMaker to train and host a machine learning (ML) model for a marketing campaign. The majority of data is sensitive customer data. The data must be encrypted at rest. The company wants AWS to maintain the root of trust for the master keys and wants encryption key usage to be l Which implementation will meet these requirements?

Accepted Answer

Correct answer: C

Question 4

A manufacturer of car engines collects data from cars as they are being driven The data collected includes timestamp, engine temperature, rotations per (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 engin 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'?

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 network (RNN) to train the model to recognize when an engine might need maintenance for a certain fault.

Accepted Answer

Correct answer: B

Question 5

A bank wants to launch a low-rate credit promotion. The bank is located in a town that recently experienced economic hardship. Only some of the bank's customers were affected by the crisis, so the bank's credit team must identify which customers to target with the promotion. However, the credit team wa make sure that loyal customers' full credit history is considered when the decision is made.

The bank's data science team developed a model that classifies account transactions and understands credit eligibility. The data science team used the X algorithm to train the model. The team used 7 years of bank transaction historical data for training and hyperparameter tuning over the course of severa The accuracy of the model is sufficient, but the credit team is struggling to explain accurately why the model denies credit to some customers. The cred almost no skill in data science.

What should the data science team do to address this issue in the MOST operationally efficient manner?

Accepted Answer

Correct answer: C

Question 6

```
"Name": learning_rate" "MaxValue FF 0.0001" "MinValue "0.1" 14 ScalingType "Logarithmic"
```

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## https://www.certshared.com/exam/AWS-Certified-Machine-Learning-Specialty/ (208 Q&As)

An e-commerce company needs a customized training model to classify images of its shirts and pants products The company needs a proof of concept in 2 to days with good accuracy Which compute choice should the Machine Learning Specialist select to train and achieve good accuracy on the model quickly?

A. m5 4xlarge (general purpose)

Accepted Answer

Correct answer: C

Question 7

A Machine Learning team runs its own training algorithm on Amazon SageMaker. The training algorithm requires external assets. The team needs to submit b its own algorithm code and algorithm-specific parameters to Amazon SageMaker.

What combination of services should the team use to build a custom algorithm in Amazon SageMaker? (Choose two.)

Accepted Answer

Correct answer: CE

Question 8

A company needs to quickly make sense of a large amount of data and gain insight from it. The data is in different formats, the schemas change frequentl new data sources are added regularly. The company wants to use AWS services to explore multiple data sources, suggest schemas, and enrich and transform data. The solution should require the least possible coding effort for the data flows and the least possible infrastructure management. Which combination of AWS services will meet these requirements?

Accepted Answer

Correct answer: A

Question 9

A financial services company wants to adopt Amazon SageMaker as its default data science environment. The company's data scientists run machine learning (ML) models on confidential financial data. The company is worried about data egress and wants an ML engineer to secure the environment. Which mechanisms can the ML engineer use to control data egress from SageMaker? (Choose three.)

Accepted Answer

https://aws.amazon.com/blogs/machine-learning/millennium-management-secure-machine-learning-using-amaz

Question 10

A Data Scientist needs to migrate an existing on-premises ETL process to the cloud The current process runs at regular time intervals and uses PySpark t combine and format multiple large data sources into a single consolidated output for downstream processing

The Data Scientist has been given the following requirements for the cloud solution

* Combine multiple data sources
* Reuse existing PySpark logic
* Run the solution on the existing schedule
* Minimize the number of servers that will need to be managed

Which architecture should the Data Scientist use to build this solution?

Accepted Answer

Correct answer: A