MLS-C01 Exam Dumps - AWS Certified Machine Learning - Specialty

Using AWS Glue to catalogue the data and Amazon Athena to run queries is the solution that requires the least effort to be able to query the data stored in an Amazon S3 bucket using SQL. AWS Glue is a service that provides a serverless data integration platform for data preparation and transformation. AWS Glue can automatically discover, crawl, and catalogue the data stored in various sources, such as Amazon S3, Amazon RDS, Amazon Redshift, etc. AWS Glue can also use AWS KMS to encrypt the data at rest on the Glue Data Catalog and Glue ETL jobs. AWS Glue can handle both structured and unstructured data, and support various data formats, such as CSV, JSON, Parquet, etc. AWS Glue can also use built-in or custom classifiers to identify and parse the data schema and format1 Amazon Athena is a service that provides an interactive query engine that can run SQL queries directly on data stored in Amazon S3. Amazon Athena can integrate with AWS Glue to use the Glue Data Catalog as a central metadata repository for the data sources and tables. Amazon Athena can also use AWS KMS to encrypt the data at rest on Amazon S3 and the query results. Amazon Athena can query both structured and unstructured data, and support various data formats, such as CSV, JSON, Parquet, etc. Amazon Athena can also use partitions and compression to optimize the query performance and reduce the query cost23

The other options are not valid or require more effort to query the data stored in an Amazon S3 bucket using SQL. Using AWS Data Pipeline to transform the data and Amazon RDS to run queries is not a good option, as it involves moving the data from Amazon S3 to Amazon RDS, which can incur additional time and cost. AWS Data Pipeline is a service that can orchestrate and automate data movement and transformation across various AWS services and on-premises data sources. AWS Data Pipeline can be integrated with Amazon EMR to run ETL jobs on the data stored in Amazon S3. Amazon RDS is a service that provides a managed relational database service that can run various database engines, such as MySQL, PostgreSQL, Oracle, etc. Amazon RDS can use AWS KMS to encrypt the data at rest and in transit. Amazon RDS can run SQL queries on the data stored in the database tables45 Using AWS Batch to run ETL on the data and Amazon Aurora to run the queries is not a good option, as it also involves moving the data from Amazon S3 to Amazon Aurora, which can incur additional time and cost. AWS Batch is a service that can run batch computing workloads on AWS. AWS Batch can be integrated with AWS Lambda to trigger ETL jobs on the data stored in Amazon S3. Amazon Aurora is a service that provides a compatible and scalable relational database engine that can run MySQL or PostgreSQL. Amazon Aurora can use AWS KMS to encrypt the data at rest and in transit. Amazon Aurora can run SQL queries on the data stored in the database tables. Using AWS Lambda to transform the data and Amazon Kinesis Data Analytics to run queries is not a good option, as it is not suitable for querying data stored in Amazon S3 using SQL. AWS Lambda is a service that can run serverless functions on AWS. AWS Lambda can be integrated with Amazon S3 to trigger data transformation functions on the data stored in Amazon S3. Amazon Kinesis Data Analytics is a service that can analyze streaming data using SQL or Apache Flink. Amazon Kinesis Data Analytics can be integrated with Amazon Kinesis Data Streams or Amazon Kinesis Data Firehose to ingest streaming data sources, such as web logs, social media, IoT devices, etc. Amazon Kinesis Data Analytics is not designed for querying data stored in Amazon S3 using SQL.

To access an Amazon S3 bucket in another account that is encrypted using SSE-KMS, the following are required:

A. An AWS KMS key policy that allows access to the customer master key (CMK). The CMK is the encryption key that is used to encrypt and decrypt the data in the S3 bucket. The KMS key policy defines who can use and manage the CMK. To allow access to the CMK from another account, the key policy must include a statement that grants the necessary permissions (such as kms:Decrypt) to the principal from the other account (such as the SageMaker notebook IAM role).

B. A SageMaker notebook security group that allows access to Amazon S3. A security group is a virtual firewall that controls the inbound and outbound traffic for the SageMaker notebook instance. To allow the notebook instance to access the S3 bucket, the security group must have a rule that allows outbound traffic to the S3 endpoint on port 443 (HTTPS).

C. An IAM role that allows access to the specific S3 bucket. An IAM role is an identity that can be assumed by the SageMaker notebook instance to access AWS resources. The IAM role must have a policy that grants the necessary permissions (such as s3:GetObject) to access the specific S3 bucket. The policy must also include a condition that allows access to the CMK in the other account.

The following are not required or correct:

D. A permissive S3 bucket policy. A bucket policy is a resource-based policy that defines who can access the S3 bucket and what actions they can perform. A permissive bucket policy is not required and not recommended, as it can expose the bucket to unauthorized access. A bucket policy should follow the principle of least privilege and grant the minimum permissions necessary to the specific principals that need access.

E. An S3 bucket owner that matches the notebook owner. The S3 bucket owner and the notebook owner do not need to match, as long as the bucket owner grants cross-account access to the notebook owner through the KMS key policy and the bucket policy (if applicable).

F. A SegaMaker notebook subnet ACL that allow traffic to Amazon S3. A subnet ACL is a network access control list that acts as an optional layer of security for the SageMaker notebook instance’s subnet. A subnet ACL is not required to access the S3 bucket, as the security group is sufficient to control the traffic. However, if a subnet ACL is used, it must not block the traffic to the S3 endpoint.

 To find the most discussed topics in the comments that are in either English or Spanish, the machine learning specialist needs to perform two steps: first, translate the comments from Spanish to English if necessary, and second, apply a topic modeling algorithm to the comments. The following options are valid ways to accomplish these steps using AWS services:

Option C: Use Amazon Translate to translate from Spanish to English, if necessary. Use Amazon Comprehend topic modeling to find the topics. Amazon Translate is a neural machine translation service that delivers fast, high-quality, and affordable language translation. Amazon Comprehend is a natural language processing (NLP) service that uses machine learning to find insights and relationships in text. Amazon Comprehend topic modeling is a feature that automatically organizes a collection of text documents into topics that contain commonly used words and phrases.

Option E: Use Amazon Translate to translate from Spanish to English, if necessary. Use Amazon SageMaker Neural Topic Model (NTM) to find the topics. Amazon SageMaker is a fully managed service that provides every developer and data scientist with the ability to build, train, and deploy machine learning (ML) models quickly. Amazon SageMaker Neural Topic Model (NTM) is an unsupervised learning algorithm that is used to organize a corpus of documents into topics that contain word groupings based on their statistical distribution.

The other options are not valid because:

Option A: Amazon SageMaker BlazingText algorithm is not a topic modeling algorithm, but a text classification and word embedding algorithm. It cannot find the topics independently from language, as different languages have different word distributions and semantics.

Option B: Amazon SageMaker seq2seq algorithm is not a translation algorithm, but a sequence-to-sequence learning algorithm that can be used for tasks such as summarization, chatbot, and question answering. Amazon SageMaker Latent Dirichlet Allocation (LDA) algorithm is a topic modeling algorithm, but it requires the input documents to be in the same language and preprocessed into a bag-of-words format.

Option D: Amazon Lex is not a topic modeling algorithm, but a service for building conversational interfaces into any application using voice and text. It cannot extract topics from the content, but only intents and slots based on a predefined bot configuration. References:

Amazon Translate

Amazon Comprehend

Amazon SageMaker

Amazon SageMaker Neural Topic Model (NTM) Algorithm

Amazon SageMaker BlazingText

Amazon SageMaker Seq2Seq

Amazon SageMaker Latent Dirichlet Allocation (LDA) Algorithm

Amazon Lex

 In this scenario, the specialist should use one-hot encoding and RGB encoding to allow the regression model to learn from the Wall_Color data. One-hot encoding is a technique used to convert categorical data into numerical data. It creates new columns that store one-hot representation of colors. For example, a variable named color has three categories: red, green, and blue. After one-hot encoding, the new variables should be like this:

One-hot encoding can capture the presence or absence of a color, but it cannot capture the intensity or hue of a color. RGB encoding is a technique used to represent colors in a digital image. It creates three columns to encode the color in RGB format. For example, a variable named color has three categories: red, green, and blue. After RGB encoding, the new variables should be like this:

RGB encoding can capture the intensity and hue of a color, but it may also introduce correlation among the three columns. Therefore, using both one-hot encoding and RGB encoding can provide more information to the regression model than using either one alone.

Feature Engineering for Categorical Data

How to Perform Feature Selection with Categorical Data

The best approaches to decrease the training time of the model are C and D, because they can improve the computational efficiency and parallelization of the training process. These approaches have the following benefits:

C: Replacing CPU-based EC2 instances with GPU-based EC2 instances can speed up the training of the DeepAR algorithm, as it can leverage the parallel processing power of GPUs to perform matrix operations and gradient computations faster than CPUs12. The DeepAR algorithm supports GPU-based EC2 instances such as ml.p2 and ml.p33.

D: Using multiple training instances can also reduce the training time of the DeepAR algorithm, as it can distribute the workload across multiple nodes and perform data parallelism4. The DeepAR algorithm supports distributed training with multiple CPU-based or GPU-based EC2 instances3.

The other options are not effective or relevant, because they have the following drawbacks:

A: Replacing On-Demand Instances with Spot Instances can reduce the cost of the training, but not necessarily the time, as Spot Instances are subject to interruption and availability5. Moreover, the DeepAR algorithm does not support checkpointing, which means that the training cannot resume from the last saved state if the Spot Instance is terminated3.

B: Configuring model auto scaling dynamically to adjust the number of instances automatically is not applicable, as this feature is only available for inference endpoints, not for training jobs6.

E: Using a pre-trained version of the model and running incremental training is not possible, as the DeepAR algorithm does not support incremental training or transfer learning3. The DeepAR algorithm requires a full retraining of the model whenever new data is added or the hyperparameters are changed7.

1: GPU vs CPU: What Matters Most for Machine Learning? | by Louis (What’s AI) Bouchard | Towards Data Science

2: How GPUs Accelerate Machine Learning Training | NVIDIA Developer Blog

3: DeepAR Forecasting Algorithm - Amazon SageMaker

4: Distributed Training - Amazon SageMaker

5: Managed Spot Training - Amazon SageMaker

6: Automatic Scaling - Amazon SageMaker

7: How the DeepAR Algorithm Works - Amazon SageMaker

The most cost-effective solution is to use an S3 event to trigger a Lambda function that uses SageMaker Data Wrangler to merge the data. This solution avoids the need to provision and manage any additional resources, such as Kinesis streams, Firehose delivery streams, Glue jobs, or Redshift clusters. SageMaker Data Wrangler provides a visual interface to import, prepare, transform, and analyze data from various sources, including AWS Data Exchange products. It can also export the data preparation workflow to a Python script that can be executed by a Lambda function. This solution can meet the time requirement of 30-60 minutes, depending on the size and complexity of the data.

Using Amazon S3 Event Notifications

Prepare ML Data with Amazon SageMaker Data Wrangler

AWS Lambda Function

Amazon Kinesis Data Firehose is the most operationally simple and fully managed service for real-time data ingestion and transformation. Firehose supports:

Direct ingestion from edge locations or public endpoints

Integration with AWS Lambda for transformation

Backup of raw source records

Error handling with separate S3 prefixes for failed transformations

“You can configure your delivery stream to use a Lambda function to transform incoming records, and to back up all or failed records to Amazon S3.”

This meets all requirements with minimal infrastructure management:

Real-time ingestion

Inline transformation

Raw data and failed transformation storage in S3

Amazon SageMaker notebook instances are fully managed environments that provide an integrated Jupyter notebook interface for data exploration, analysis, and machine learning. Amazon SageMaker notebook instances are based on EC2 instances that run within AWS service accounts, not within customer accounts. This means that the ML Specialist cannot find the Amazon SageMaker notebook instance’s EC2 instance or EBS volume within the VPC, as they are not visible or accessible to the customer. However, the ML Specialist can still take a snapshot of the EBS volume by using the Amazon SageMaker console or API. The ML Specialist can also use VPC interface endpoints to securely connect the Amazon SageMaker notebook instance to the resources within the VPC, such as Amazon S3 buckets, Amazon EFS file systems, or Amazon RDS databases