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NEW QUESTION # 153
A Machine Learning Specialist at a company sensitive to security is preparing a dataset for model training.
The dataset is stored in Amazon S3 and contains Personally Identifiable Information (Pll). The dataset:
* Must be accessible from a VPC only.
* Must not traverse the public internet.
How can these requirements be satisfied?
Answer: A
NEW QUESTION # 154
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?
Answer: A
Explanation:
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.
References:
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 # 155
An insurance company is developing a new device for vehicles that uses a camera to observe drivers' behavior and alert them when they appear distracted The company created approximately 10,000 training images in a controlled environment that a Machine Learning Specialist will use to train and evaluate machine learning models During the model evaluation the Specialist notices that the training error rate diminishes faster as the number of epochs increases and the model is not accurately inferring on the unseen test images Which of the following should be used to resolve this issue? (Select TWO)
Answer: B,C
NEW QUESTION # 156
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 dat a. 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.)
Answer: A,B,F
Explanation:
To control data egress from SageMaker, the ML engineer can use the following mechanisms:
Connect to SageMaker by using a VPC interface endpoint powered by AWS PrivateLink. This allows the ML engineer to access SageMaker services and resources without exposing the traffic to the public internet. This reduces the risk of data leakage and unauthorized access1 Enable network isolation for training jobs and models. This prevents the training jobs and models from accessing the internet or other AWS services. This ensures that the data used for training and inference is not exposed to external sources2 Protect data with encryption at rest and in transit. Use AWS Key Management Service (AWS KMS) to manage encryption keys. This enables the ML engineer to encrypt the data stored in Amazon S3 buckets, SageMaker notebook instances, and SageMaker endpoints. It also allows the ML engineer to encrypt the data in transit between SageMaker and other AWS services. This helps protect the data from unauthorized access and tampering3 The other options are not effective in controlling data egress from SageMaker:
Use SCPs to restrict access to SageMaker. SCPs are used to define the maximum permissions for an organization or organizational unit (OU) in AWS Organizations. They do not control the data egress from SageMaker, but rather the access to SageMaker itself4 Disable root access on the SageMaker notebook instances. This prevents the users from installing additional packages or libraries on the notebook instances. It does not prevent the data from being transferred out of the notebook instances.
Restrict notebook presigned URLs to specific IPs used by the company. This limits the access to the notebook instances from certain IP addresses. It does not prevent the data from being transferred out of the notebook instances.
References:
1: Amazon SageMaker Interface VPC Endpoints (AWS PrivateLink) - Amazon SageMaker
2: Network Isolation - Amazon SageMaker
3: Encrypt Data at Rest and in Transit - Amazon SageMaker
4: Using Service Control Policies - AWS Organizations
5: Disable Root Access - Amazon SageMaker
6: Create a Presigned Notebook Instance URL - Amazon SageMaker
NEW QUESTION # 157
A Machine Learning Specialist prepared the following graph displaying the results of k-means for k = [1:10]
Considering the graph, what is a reasonable selection for the optimal choice of k?
Answer: C
Explanation:
The elbow method is a technique that we use to determine the number of centroids (k) to use in a k-means clustering algorithm. In this method, we plot the within-cluster sum of squares (WCSS) against the number of clusters (k) and look for the point where the curve bends sharply. This point is called the elbow point and it indicates that adding more clusters does not improve the model significantly. The graph in the question shows that the elbow point is at k = 4, which means that 4 is a reasonable choice for the optimal number of clusters. References:
Elbow Method for optimal value of k in KMeans: A tutorial on how to use the elbow method with Amazon SageMaker.
K-Means Clustering: A video that explains the concept and benefits of k-means clustering.
NEW QUESTION # 158
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