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The AWS Certified Machine Learning - Specialty certification is an excellent way for individuals to showcase their ML expertise and demonstrate their ability to design and implement ML solutions on the AWS platform. AWS Certified Machine Learning - Specialty certification is ideal for data scientists, machine learning engineers, and software developers who are looking to advance their career in the field of ML.
Amazon AWS Certified Machine Learning - Specialty Sample Questions (Q242-Q247):
NEW QUESTION # 242
A trucking company is collecting live image data from its fleet of trucks across the globe. The data is growing rapidly and approximately 100 GB of new data is generated every day. The company wants to explore machine learning uses cases while ensuring the data is only accessible to specific IAM users.
Which storage option provides the most processing flexibility and will allow access control with IAM?
- A. Use a database, such as Amazon DynamoDB, to store the images, and set the IAM policies to restrict access to only the desired IAM users.
- B. Use an Amazon S3-backed data lake to store the raw images, and set up the permissions using bucket policies.
- C. Configure Amazon EFS with IAM policies to make the data available to Amazon EC2 instances owned by the IAM users.
- D. Setup up Amazon EMR with Hadoop Distributed File System (HDFS) to store the files, and restrict access to the EMR instances using IAM policies.
Answer: B
Explanation:
The best storage option for the trucking company is to use an Amazon S3-backed data lake to store the raw images, and set up the permissions using bucket policies. A data lake is a centralized repository that allows you to store all your structured and unstructured data at any scale. Amazon S3 is the ideal choice for building a data lake because it offers high durability, scalability, availability, and security. You can store any type of data in Amazon S3, such as images, videos, audio, text, etc. You can also use AWS services such as Amazon Rekognition, Amazon SageMaker, and Amazon EMR to analyze and process the data in the data lake. To ensure the data is only accessible to specific IAM users, you can use bucket policies to grant or deny access to the S3 buckets based on the IAM user's identity or role. Bucket policies are JSON documents that specify the permissions for the bucket and the objects in it. You can use conditions to restrict access based on various factors, such as IP address, time, source, etc. By using bucket policies, you can control who can access the data in the data lake and what actions they can perform on it.
References:
AWS Machine Learning Specialty Exam Guide
AWS Machine Learning Training - Build a Data Lake Foundation with Amazon S3 AWS Machine Learning Training - Using Bucket Policies and User Policies
NEW QUESTION # 243
During mini-batch training of a neural network for a classification problem, a Data Scientist notices that training accuracy oscillates What is the MOST likely cause of this issue?
- A. The batch size is too big
- B. The class distribution in the dataset is imbalanced
- C. The learning rate is very high
- D. Dataset shuffling is disabled
Answer: C
NEW QUESTION # 244
A company is setting up a mechanism for data scientists and engineers from different departments to access an Amazon SageMaker Studio domain. Each department has a unique SageMaker Studio domain.
The company wants to build a central proxy application that data scientists and engineers can log in to by using their corporate credentials. The proxy application will authenticate users by using the company's existing Identity provider (IdP). The application will then route users to the appropriate SageMaker Studio domain.
The company plans to maintain a table in Amazon DynamoDB that contains SageMaker domains for each department.
How should the company meet these requirements?
- A. Use the SageMaker CreateHuman TaskUi API to generate a UI URL. Pass the URL to the proxy application.
- B. Use the SageMaker CreatePresignedDomainUrl API to generate a presigned URL for each domain according to the DynamoDB table. Pass the presigned URL to the proxy application.
- C. Use the Amazon SageMaker ListHumanTaskUis API to list all UI URLs. Pass the appropriate URL to the DynamoDB table so that the proxy application can use the URL.
- D. Use the SageMaker CreatePresignedNotebookInstanceUrl API to generate a presigned URL. Pass the presigned URL to the proxy application.
Answer: B
Explanation:
The SageMaker CreatePresignedDomainUrl API is the best option to meet the requirements of the company.
This API creates a URL for a specified UserProfile in a Domain. When accessed in a web browser, the user will be automatically signed in to the domain, and granted access to all of the Apps and files associated with the Domain's Amazon Elastic File System (EFS) volume. This API can only be called when the authentication mode equals IAM, which means the company can use its existing IdP to authenticate users. The company can use the DynamoDB table to store the domain IDs and user profile names for each department, and use the proxy application to query the table and generate the presigned URL for the appropriate domain according to the user's credentials. The presigned URL is valid only for a specified duration, which can be set by the SessionExpirationDurationInSeconds parameter. This can help enhance the security and prevent unauthorized access to the domains.
The other options are not suitable for the company's requirements. The SageMaker CreateHumanTaskUi API is used to define the settings for the human review workflow user interface, which is not related to accessing the SageMaker Studio domains. The SageMaker ListHumanTaskUis API is used to return information about the human task user interfaces in the account, which is also not relevant to the company's use case. The SageMaker CreatePresignedNotebookInstanceUrl API is used to create a URL to connect to the Jupyter server from a notebook instance, which is different from accessing the SageMaker Studio domain.
NEW QUESTION # 245
A Machine Learning Specialist is working for an online retailer that wants to run analytics on every customer visit, processed through a machine learning pipeline. The data needs to be ingested by Amazon Kinesis Data Streams at up to 100 transactions per second, and the JSON data blob is 100 KB in size.
What is the MINIMUM number of shards in Kinesis Data Streams the Specialist should use to successfully ingest this data?
- A. 1 shards
- B. 10 shards
- C. 1,000 shards
- D. 100 shards
Answer: A
Explanation:
According to the Amazon Kinesis Data Streams documentation, the maximum size of data blob (the data payload before Base64-encoding) per record is 1 MB. The maximum number of records that can be sent to a shard per second is 1,000. Therefore, the maximum throughput of a shard is 1 MB/sec for input and 2 MB/sec for output. In this case, the input throughput is 100 transactions per second * 100 KB per transaction = 10 MB
/sec. Therefore, the minimum number of shards required is 10 MB/sec / 1 MB/sec = 10 shards. However, the question asks for the minimum number of shards in Kinesis Data Streams, not the minimum number of shards per stream. A Kinesis Data Streams account can have multiple streams, each with its own number of shards.
Therefore, the minimum number of shards in Kinesis Data Streams is 1, which is the minimum number of shards per stream. References:
* Amazon Kinesis Data Streams Terminology and Concepts
* Amazon Kinesis Data Streams Limits
NEW QUESTION # 246
A manufacturing company asks its Machine Learning Specialist to develop a model that classifies defective parts into one of eight defect types. The company has provided roughly 100000 images per defect type for training During the injial training of the image classification model the Specialist notices that the validation accuracy is 80%, while the training accuracy is 90% It is known that human-level performance for this type of image classification is around 90% What should the Specialist consider to fix this issue1?
- A. Making the network larger
- B. A longer training time
- C. Using some form of regularization
- D. Using a different optimizer
Answer: C
NEW QUESTION # 247
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