Amazon MLS-C01 Practice Test - Questions Answers, Page 6

The best approach to maximize transcription accuracy during the development phase is to create a custom vocabulary file containing each product name with phonetic pronunciations, and use it with Amazon Transcribe to perform the ASR customization. A custom vocabulary is a list of words and phrases that are likely to appear in your audio input, along with optional information about how to pronounce them. By using a custom vocabulary, you can improve the transcription accuracy of domain-specific terms, such as product names, that may not be recognized by the general vocabulary of Amazon Transcribe. You can also analyze the transcripts and manually update the custom vocabulary file to include updated or additional entries for those names that are not being correctly identified.

The other options are not as effective as option C for the following reasons:

Option A is not suitable because Amazon Lex is a service for building conversational interfaces, not for transcribing voicemail messages. Amazon Lex also has a limit of 100 slots per bot, which is not enough to accommodate the 200 unique product names required by the company.

Option B is not optimal because it relies on the word confidence scores in the transcript, which may not be accurate enough to identify all the mis-transcribed product names. Moreover, automatically creating or updating a custom vocabulary file may introduce errors or inconsistencies in the pronunciation or display of the words.

Option D is not feasible because it requires a large amount of training data to build a custom language model. The company only has 4,000 words of Amazon SageMaker Ground Truth voicemail transcripts, which is not enough to train a robust and reliable custom language model. Additionally, creating and updating a custom language model is a time-consuming and resource-intensive process, which may not be suitable for the development phase where frequent changes are expected.

References:

Amazon Transcribe -- Custom Vocabulary

Amazon Transcribe -- Custom Language Models

[Amazon Lex -- Limits]

The best solution to reduce the cost of the notebook instance and the data preprocessing job is to change the notebook instance type to a smaller general-purpose instance, stop the notebook when it is not in use, and run data preprocessing on an ml.r5 instance with the same memory size as the ml.m5.4xlarge instance by using Amazon SageMaker Processing. This solution will result in the most cost savings because:

Changing the notebook instance type to a smaller general-purpose instance will reduce the hourly cost of running the notebook, since the feature engineering development does not require high CPU and memory resources.For example, an ml.t3.medium instance costs $0.0464 per hour, while an ml.m5.4xlarge instance costs $0.888 per hour1.

Stopping the notebook when it is not in use will also reduce the cost, since the notebook will only incur charges when it is running.For example, if the notebook is used for 8 hours per day, 5 days per week, then stopping it when it is not in use will save about 76% of the monthly cost compared to leaving it running all the time2.

Running data preprocessing on an ml.r5 instance with the same memory size as the ml.m5.4xlarge instance by using Amazon SageMaker Processing will reduce the cost of the data preprocessing job, since the ml.r5 instance is optimized for memory-intensive workloads and has a lower cost per GB of memory than the ml.m5 instance.For example, an ml.r5.4xlarge instance has 128 GB of memory and costs $1.008 per hour, while an ml.m5.4xlarge instance has 64 GB of memory and costs $0.888 per hour1. Therefore, the ml.r5.4xlarge instance can process the same amount of data in half the time and at a lower cost than the ml.m5.4xlarge instance. Moreover, using Amazon SageMaker Processing will allow the data preprocessing job to run on a separate, fully managed infrastructure that can be scaled up or down as needed, without affecting the notebook instance.

The other options are not as effective as option C for the following reasons:

Option A is not optimal because changing the notebook instance type to a memory optimized instance with the same vCPU number as the ml.m5.4xlarge instance has will not reduce the cost of the notebook, since the memory optimized instances have a higher cost per vCPU than the general-purpose instances.For example, an ml.r5.4xlarge instance has 16 vCPUs and costs $1.008 per hour, while an ml.m5.4xlarge instance has 16 vCPUs and costs $0.888 per hour1. Moreover, running both data preprocessing and feature engineering development on the same instance will not take advantage of the scalability and flexibility of Amazon SageMaker Processing.

Option B is not suitable because running data preprocessing on a P3 instance type with the same memory as the ml.m5.4xlarge instance by using Amazon SageMaker Processing will not reduce the cost of the data preprocessing job, since the P3 instance type is optimized for GPU-based workloads and has a higher cost per GB of memory than the ml.m5 or ml.r5 instance types.For example, an ml.p3.2xlarge instance has 61 GB of memory and costs $3.06 per hour, while an ml.m5.4xlarge instance has 64 GB of memory and costs $0.888 per hour1. Moreover, the data preprocessing job does not require GPU, so using a P3 instance type will be wasteful and inefficient.

Option D is not feasible because running data preprocessing on an R5 instance with the same memory size as the ml.m5.4xlarge instance by using the Reserved Instance option will not reduce the cost of the data preprocessing job, since the Reserved Instance option requires a commitment to a consistent amount of usage for a period of 1 or 3 years3. However, the data preprocessing job only runs once a day on average and completes in only 2 hours, so it does not have a consistent or predictable usage pattern. Therefore, using the Reserved Instance option will not provide any cost savings and may incur additional charges for unused capacity.

References:

Amazon SageMaker Pricing

Manage Notebook Instances - Amazon SageMaker

Amazon EC2 Pricing - Reserved Instances

A collaborative filtering recommendation engine is a type of machine learning system that can improve sales for a company by using the large amount of information the company has on users' behavior and product preferences to predict which products users would like based on the users' similarity to other users. A collaborative filtering recommendation engine works by finding the users who have similar ratings or preferences for the products, and then recommending the products that the similar users have liked but the target user has not seen or rated. A collaborative filtering recommendation engine can leverage the collective wisdom of the users and discover the hidden patterns and associations among the products and the users. A collaborative filtering recommendation engine can be implemented using Apache Spark ML on Amazon EMR, which are two services that can handle large-scale data processing and machine learning tasks. Apache Spark ML is a library that provides various tools and algorithms for machine learning, such as classification, regression, clustering, recommendation, etc. Apache Spark ML can run on Amazon EMR, which is a service that provides a managed cluster platform that simplifies running big data frameworks, such as Apache Spark, on AWS. Apache Spark ML on Amazon EMR can build a collaborative filtering recommendation engine using the Alternating Least Squares (ALS) algorithm, which is a matrix factorization technique that can learn the latent factors that represent the users and the products, and then use them to predict the ratings or preferences of the users for the products.Apache Spark ML on Amazon EMR can also support both explicit feedback, such as ratings or reviews, and implicit feedback, such as views or clicks, for building a collaborative filtering recommendation engine12

AWS KMS is a service that provides encryption and key management for data stored in AWS services and applications. AWS KMS can generate and manage encryption keys that are used to encrypt and decrypt data at rest and in transit. AWS KMS can also integrate with other AWS services, such as Amazon S3 and Amazon SageMaker, to enable encryption of data using the keys stored in AWS KMS. Amazon S3 is a service that provides object storage for data in the cloud. Amazon S3 can use AWS KMS to encrypt data at rest using server-side encryption with AWS KMS-managed keys (SSE-KMS). Amazon SageMaker is a service that provides a platform for building, training, and deploying machine learning models. Amazon SageMaker can use AWS KMS to encrypt data at rest on the SageMaker instances and volumes, as well as data in transit between SageMaker and other AWS services. AWS Glue is a service that provides a serverless data integration platform for data preparation and transformation. AWS Glue can use AWS KMS to encrypt data at rest on the Glue Data Catalog and Glue ETL jobs.AWS Glue can also use built-in or custom classifiers to identify and redact sensitive data, such as credit card numbers, from the customer data1234

The other options are not valid or secure ways to encrypt the data and protect the credit card information. Using a custom encryption algorithm to encrypt the data and store the data on an Amazon SageMaker instance in a VPC is not a good practice, as custom encryption algorithms are not recommended for security and may have flaws or vulnerabilities. Using the SageMaker DeepAR algorithm to randomize the credit card numbers is not a good practice, as DeepAR is a forecasting algorithm that is not designed for data anonymization or encryption. Using an IAM policy to encrypt the data on the Amazon S3 bucket and Amazon Kinesis to automatically discard credit card numbers and insert fake credit card numbers is not a good practice, as IAM policies are not meant for data encryption, but for access control and authorization. Amazon Kinesis is a service that provides real-time data streaming and processing, but it does not have the capability to automatically discard or insert data values. Using an Amazon SageMaker launch configuration to encrypt the data once it is copied to the SageMaker instance in a VPC is not a good practice, as launch configurations are not meant for data encryption, but for specifying the instance type, security group, and user data for the SageMaker instance. Using the SageMaker principal component analysis (PCA) algorithm to reduce the length of the credit card numbers is not a good practice, as PCA is a dimensionality reduction algorithm that is not designed for data anonymization or encryption.

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

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 format1Amazon 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 tables45Using 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.

Collaborative filtering is a machine learning technique that recommends products or services to users based on the ratings or preferences of other users. This technique is well-suited for identifying customer shopping patterns and preferences because it takes into account the interactions between users and products.

The goal of classification is to determine to which class or category a data point (customer in our case) belongs to. For classification problems, data scientists would use historical data with predefined target variables AKA labels (churner/non-churner) -- answers that need to be predicted -- to train an algorithm. With classification, businesses can answer the following questions:

Will this customer churn or not?

Will a customer renew their subscription?

Will a user downgrade a pricing plan?

Are there any signs of unusual customer behavior?