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The AWS Certified Machine Learning - Specialty certification exam is intended for individuals who have a strong understanding of machine learning concepts, algorithms, and techniques. AWS-Certified-Machine-Learning-Specialty Exam covers a wide range of topics, including data engineering, data pre-processing, machine learning algorithms, and model evaluation. Candidates are also tested on their ability to use AWS services and tools such as Amazon SageMaker, Amazon S3, Amazon EC2, and Amazon Kinesis.

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Earning the AWS Certified Machine Learning - Specialty certification demonstrates to employers and clients that an individual has the necessary skills and knowledge to design, build, and deploy machine learning models on the AWS platform. AWS Certified Machine Learning - Specialty certification can also help individuals advance their careers and increase their earning potential within the field of machine learning.

Amazon AWS Certified Machine Learning - Specialty Sample Questions (Q114-Q119):

NEW QUESTION # 114
A data scientist obtains a tabular dataset that contains 150 correlated features with different ranges to build a regression model. The data scientist needs to achieve more efficient model training by implementing a solution that minimizes impact on the model's performance. The data scientist decides to perform a principal component analysis (PCA) preprocessing step to reduce the number of features to a smaller set of independent features before the data scientist uses the new features in the regression model.
Which preprocessing step will meet these requirements?

• A. Reduce the dimensionality of the dataset by removing the features that have the highest correlation Load the data into Amazon SageMaker Data Wrangler Perform a Standard Scaler transformation step to scale the data Use the SageMaker built-in algorithm for PCA on the scaled dataset to transform the data
• B. Load the data into Amazon SageMaker Data Wrangler. Scale the data with a Min Max Scaler transformation step Use the SageMaker built-in algorithm for PCA on the scaled dataset to transform the data.
• C. Use the Amazon SageMaker built-in algorithm for PCA on the dataset to transform the data
• D. Reduce the dimensionality of the dataset by removing the features that have the lowest correlation. Load the data into Amazon SageMaker Data Wrangler. Perform a Min Max Scaler transformation step to scale the data. Use the SageMaker built-in algorithm for PCA on the scaled dataset to transform the data.

Answer: B

Explanation:
Explanation
Principal component analysis (PCA) is a technique for reducing the dimensionality of datasets, increasing interpretability but at the same time minimizing information loss. It does so by creating new uncorrelated variables that successively maximize variance. PCA is useful when dealing with datasets that have a large number of correlated features. However, PCA is sensitive to the scale of the features, so it is important to standardize or normalize the data before applying PCA. Amazon SageMaker provides a built-in algorithm for PCA that can be used to transform the data into a lower-dimensional representation. Amazon SageMaker Data Wrangler is a tool that allows data scientists to visually explore, clean, and prepare data for machine learning.
Data Wrangler provides various transformation steps that can be applied to the data, such as scaling, encoding, imputing, etc. Data Wrangler also integrates with SageMaker built-in algorithms, such as PCA, to enable feature engineering and dimensionality reduction. Therefore, option B is the correct answer, as it involves scaling the data with a Min Max Scaler transformation step, which rescales the data to a range of [0, 1], and then using the SageMaker built-in algorithm for PCA on the scaled dataset to transform the data. Option A is incorrect, as it does not involve scaling the data before applying PCA, which can affect the results of the dimensionality reduction. Option C is incorrect, as it involves removing the features that have the highest correlation, which can lead to information loss and reduce the performance of the regression model. Option D is incorrect, as it involves removing the features that have the lowest correlation, which can also lead to information loss and reduce the performance of the regression model. References:
Principal Component Analysis (PCA) - Amazon SageMaker
Scale data with a Min Max Scaler - Amazon SageMaker Data Wrangler
Use Amazon SageMaker built-in algorithms - Amazon SageMaker Data Wrangler

NEW QUESTION # 115
A company provisions Amazon SageMaker notebook instances for its data science team and creates Amazon VPC interface endpoints to ensure communication between the VPC and the notebook instances. All connections to the Amazon SageMaker API are contained entirely and securely using the AWS network.
However, the data science team realizes that individuals outside the VPC can still connect to the notebook instances across the internet.
Which set of actions should the data science team take to fix the issue?

• A. Create an IAM policy that allows the sagemaker:CreatePresignedNotebooklnstanceUrl and sagemaker:
  DescribeNotebooklnstance actions from only the VPC endpoints. Apply this policy to all IAM users, groups, and roles used to access the notebook instances.
• B. Add a NAT gateway to the VPC. Convert all of the subnets where the Amazon SageMaker notebook instances are hosted to private subnets. Stop and start all of the notebook instances to reassign only private IP addresses.
• C. Change the network ACL of the subnet the notebook is hosted in to restrict access to anyone outside the VPC.
• D. Modify the notebook instances' security group to allow traffic only from the CIDR ranges of the VPC.
  Apply this security group to all of the notebook instances' VPC interfaces.

Answer: D

Explanation:
The issue is that the notebook instances' security group allows inbound traffic from any source IP address, which means that anyone with the authorized URL can access the notebook instances over the internet. To fix this issue, the data science team should modify the security group to allow traffic only from the CIDR ranges of the VPC, which are the IP addresses assigned to the resources within the VPC. This way, only the VPC interface endpoints and the resources within the VPC can communicate with the notebook instances. The data science team should apply this security group to all of the notebook instances' VPC interfaces, which are the network interfaces that connect the notebook instances to the VPC.
The other options are not correct because:
* Option B: Creating an IAM policy that allows the sagemaker:CreatePresignedNotebookInstanceUrl and sagemaker:DescribeNotebookInstance actions from only the VPC endpoints does not prevent individuals outside the VPC from accessing the notebook instances. These actions are used to generate and retrieve the authorized URL for the notebook instances, but they do not control who can use the URL to access the notebook instances. The URL can still be shared or leaked to unauthorized users, who can then access the notebook instances over the internet.
* Option C: Adding a NAT gateway to the VPC and converting the subnets where the notebook instances are hosted to private subnets does not solve the issue either. A NAT gateway is used to enable outbound internet access from a private subnet, but it does not affect inbound internet access. The notebook instances can still be accessed over the internet if their security group allows inbound traffic from any source IP address. Moreover, stopping and starting the notebook instances to reassign only private IP addresses is not necessary, because the notebook instances already have private IP addresses assigned by the VPC interface endpoints.
* Option D: Changing the network ACL of the subnet the notebook is hosted in to restrict access to anyone outside the VPC is not a good practice, because network ACLs are stateless and apply to the entire subnet. This means that the data science team would have to specify both the inbound and outbound rules for each IP address range that they want to allow or deny. This can be cumbersome and error-prone, especially if the VPC has multiple subnets and resources. It is better to use security groups, which are stateful and apply to individual resources, to control the access to the notebook instances.
Connect to SageMaker Within your VPC - Amazon SageMaker
Security Groups for Your VPC - Amazon Virtual Private Cloud
VPC Interface Endpoints - Amazon Virtual Private Cloud

NEW QUESTION # 116
A Data Science team is designing a dataset repository where it will store a large amount of training data commonly used in its machine learning models. As Data Scientists may create an arbitrary number of new datasets every day the solution has to scale automatically and be cost-effective. Also, it must be possible to explore the data using SQL.
Which storage scheme is MOST adapted to this scenario?

• A. Store datasets as files in Amazon S3.
• B. Store datasets as tables in a multi-node Amazon Redshift cluster.
• C. Store datasets as files in an Amazon EBS volume attached to an Amazon EC2 instance.
• D. Store datasets as global tables in Amazon DynamoDB.

Answer: A

Explanation:
The best storage scheme for this scenario is to store datasets as files in Amazon S3. Amazon S3 is a scalable, cost-effective, and durable object storage service that can store any amount and type of data. Amazon S3 also supports querying data using SQL with Amazon Athena, a serverless interactive query service that can analyze data directly in S3. This way, the Data Science team can easily explore and analyze their datasets without having to load them into a database or a compute instance.
The other options are not as suitable for this scenario because:
* Storing datasets as files in an Amazon EBS volume attached to an Amazon EC2 instance would limit the scalability and availability of the data, as EBS volumes are only accessible within a single availability zone and have a maximum size of 16 TiB. Also, EBS volumes are more expensive than S3 buckets and require provisioning and managing EC2 instances.
* Storing datasets as tables in a multi-node Amazon Redshift cluster would incur higher costs and complexity than using S3 and Athena. Amazon Redshift is a data warehouse service that is optimized for analytical queries over structured or semi-structured data. However, it requires setting up and maintaining a cluster of nodes, loading data into tables, and choosing the right distribution and sort keys for optimal performance. Moreover, Amazon Redshift charges for both storage and compute, while S3 and Athena only charge for the amount of data stored and scanned, respectively.
* Storing datasets as global tables in Amazon DynamoDB would not be feasible for large amounts of data, as DynamoDB is a key-value and document database service that is designed for fast and consistent performance at any scale. However, DynamoDB has a limit of 400 KB per item and 25 GB per partition key value, which may not be enough for storing large datasets. Also, DynamoDB does not support SQL queries natively, and would require using a service like Amazon EMR or AWS Glue to run SQL queries over DynamoDB data.
Amazon S3 - Cloud Object Storage
Amazon Athena - Interactive SQL Queries for Data in Amazon S3
Amazon EBS - Amazon Elastic Block Store (EBS)
Amazon Redshift - Data Warehouse Solution - AWS
Amazon DynamoDB - NoSQL Cloud Database Service

NEW QUESTION # 117
A Machine Learning Specialist discover the following statistics while experimenting on a model.

What can the Specialist from the experiments?

• A. The model In Experiment 1 had a high variance error lhat was reduced in Experiment 3 by regularization Experiment 2 shows that there is minimal bias error in Experiment 1
• B. The model in Experiment 1 had a high bias error that was reduced in Experiment 3 by regularization Experiment 2 shows that there is minimal variance error in Experiment 1
• C. The model in Experiment 1 had a high bias error and a high variance error that were reduced in Experiment 3 by regularization Experiment 2 shows thai high bias cannot be reduced by increasing layers and neurons in the model
• D. The model in Experiment 1 had a high random noise error that was reduced in Experiment 3 by regularization Experiment 2 shows that random noise cannot be reduced by increasing layers and neurons in the model

Answer: A

Explanation:
The model in Experiment 1 had a high variance error because it performed well on the training data (train error = 5%) but poorly on the test data (test error = 8%). This indicates that the model was overfitting the training data and not generalizing well to new data. The model in Experiment 3 had a lower variance error because it performed similarly on the training data (train error = 5.1%) and the test data (test error = 5.4%). This indicates that the model was more robust and less sensitive to the fluctuations in the training data. The model in Experiment 3 achieved this improvement by implementing regularization, which is a technique that reduces the complexity of the model and prevents overfitting by adding a penalty term to the loss function. The model in Experiment 2 had a minimal bias error because it performed similarly on the training data (train error = 5.2%) and the test data (test error = 5.7%) as the model in Experiment 1. This indicates that the model was not underfitting the data and capturing the true relationship between the input and output variables. The model in Experiment 2 increased the number of layers and neurons in the model, which is a way to increase the complexity and flexibility of the model. However, this did not improve the performance of the model, as the variance error remained high. This shows that increasing the complexity of the model is not always the best way to reduce the bias error, and may even increase the variance error if the model becomes too complex for the data. References:
Bias Variance Tradeoff - Clearly Explained - Machine Learning Plus
The Bias-Variance Trade-off in Machine Learning - Stack Abuse

NEW QUESTION # 118
An e commerce company wants to launch a new cloud-based product recommendation feature for its web application. Due to data localization regulations, any sensitive data must not leave its on-premises data center, and the product recommendation model must be trained and tested using nonsensitive data only. Data transfer to the cloud must use IPsec. The web application is hosted on premises with a PostgreSQL database that contains all the dat a. The company wants the data to be uploaded securely to Amazon S3 each day for model retraining.
How should a machine learning specialist meet these requirements?

• A. Create an AWS Glue job to connect to the PostgreSQL DB instance. Ingest all data through an AWS Site- to-Site VPN connection into Amazon S3 while removing sensitive data using a PySpark job.
• B. Use PostgreSQL logical replication to replicate all data to PostgreSQL in Amazon EC2 through AWS Direct Connect with a VPN connection. Use AWS Glue to move data from Amazon EC2 to Amazon S3.
• C. Use AWS Database Migration Service (AWS DMS) with table mapping to select PostgreSQL tables with no sensitive data through an SSL connection. Replicate data directly into Amazon S3.
• D. Create an AWS Glue job to connect to the PostgreSQL DB instance. Ingest tables without sensitive data through an AWS Site-to-Site VPN connection directly into Amazon S3.

Answer: C

Explanation:
The best option is to use AWS Database Migration Service (AWS DMS) with table mapping to select PostgreSQL tables with no sensitive data through an SSL connection. Replicate data directly into Amazon S3. This option meets the following requirements:
It ensures that only nonsensitive data is transferred to the cloud by using table mapping to filter out the tables that contain sensitive data1.
It uses IPsec to secure the data transfer by enabling SSL encryption for the AWS DMS endpoint2.
It uploads the data to Amazon S3 each day for model retraining by using the ongoing replication feature of AWS DMS3.
The other options are not as effective or feasible as the option above. Creating an AWS Glue job to connect to the PostgreSQL DB instance and ingest data through an AWS Site-to-Site VPN connection directly into Amazon S3 is possible, but it requires more steps and resources than using AWS DMS. Also, it does not specify how to filter out the sensitive data from the tables. Creating an AWS Glue job to connect to the PostgreSQL DB instance and ingest all data through an AWS Site-to-Site VPN connection into Amazon S3 while removing sensitive data using a PySpark job is also possible, but it is more complex and error-prone than using AWS DMS. Also, it does not use IPsec as required. Using PostgreSQL logical replication to replicate all data to PostgreSQL in Amazon EC2 through AWS Direct Connect with a VPN connection, and then using AWS Glue to move data from Amazon EC2 to Amazon S3 is not feasible, because PostgreSQL logical replication does not support replicating only a subset of data4. Also, it involves unnecessary data movement and additional costs.
References:
Table mapping - AWS Database Migration Service
Using SSL to encrypt a connection to a DB instance - AWS Database Migration Service Ongoing replication - AWS Database Migration Service Logical replication - PostgreSQL

NEW QUESTION # 119
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