AWS Machine Learning Specialty Exam
Solved 100% Correct
2 Techniques to Address Outliers in Data - ANSWER-Robust standardization : Ignore
the outliers from the calculation of the mean and standard deviation, then use the
calculated values to scale the variable.
This is called robust standardization or robust data scaling.
Logarithm transformation and Robust Standardization are the correct techniques to
address the outliers in data.
& Log transformation : The Log Transform decreases the effect of the outliers, due to
the normalization of magnitude differences and the model become more robust.
The logarithm, x to log base 10 of x, or x to log base e of x (ln x), or x to log base 2 of x,
is a strong transformation with a major effect on distribution shape. It is commonly used
for reducing right skewness and is often appropriate for measured variables. It can not
be applied to zero or negative values. One unit on a logarithmic scale means a
multiplication by the base of logarithms being used.
2 ways to reduce cost of automatic hyperparameter tuning job - ANSWER-Use less
concurrency while tuning : Since the tuning process learns from each incremental step,
too much concurrency can actually hinder that learning. Use logarithmic scales on
parameter ranges: Logarithmic ranges tend to find optimal values more quickly than
linear ranges.
3 Steps to SageMaker Deployment - ANSWER-1) Create model 2) Create HTTPS
endpoint 3) Create HTTPS endpoint config
3 Visualizations to Spot Outliers - ANSWER-1) Box and whisker 2) Scatter plot 3)
Histogram
4 Methods for Encrypting Objects in S3 - ANSWER-SSE-S3: encrypts objects using
keys managed by AWS. SSE-KMS: Key Mgmt Service manages encryption keys (addtl
security and audit trails). SSE-C: manage your own encryption keys. Client Side
Encryption.
A Hyperparameter tuning job in SageMaker is using more time and resources than you
are willing to spend. What might be one way to make it more efficient? - ANSWER-
Restrict the search space by limiting hyperparameter ranges as much as possible.
Accuracy - ANSWER-TP + TN / TP + TN + FP + FN . How many times the model
predicted correctly over all total preditions right + wrong.
Activation Functions - ANSWER-Define the output of a node/neuron given its input
signals.
,Amazon API Gateway - ANSWER-A fully managed service that makes it easy for
developers to create, publish, maintain, monitor, and secure APIs at any scale. API
Gateway can be used to present an external-facing, single point of entry for Amazon
SageMaker endpoints. API Gateway can be used to front an Amazon SageMaker
inference endpoint as (part of) a REST API, by making use of an API Gateway feature
called mapping templates. This feature makes it possible for the REST API to be
integrated directly with an Amazon SageMaker runtime endpoint, thereby avoiding the
use of any intermediate compute resource (such as AWS Lambda or Amazon ECS
containers) to invoke the endpoint. The result is a solution that is simpler, faster, and
cheaper to run.
Amazon Athena - ANSWER-Amazon Athena is an interactive query service that makes
it easy to analyze data in Amazon S3
using standard SQL
serverless
pay for only the queries you run.
Amazon Augmented AI (Amazon A2I) - ANSWER-Enables you to build the workflows
that are required for human review of machine learning predictions. Amazon Textract is
directly integrated with Amazon A2I so that you can easily get low-confidence results
from Amazon Textract's AnalyzeDocument API operation reviewed by humans.
Amazon CloudWatch - ANSWER-You can monitor Amazon SageMaker using Amazon
CloudWatch, which collects raw data and processes it into readable, near real-time
metrics. These statistics are kept for 15 months so that you can access historical
information and gain a better perspective on how your web application or service is
performing. To help you debug your processing jobs, training jobs, endpoints, transform
jobs, notebook instances, and notebook instance lifecycle configurations, anything an
algorithm container, a model container, or a notebook instance lifecycle configuration
sends to stdout or stderr is also sent to Amazon CloudWatch Logs. In addition to
debugging, you can use these for progress analysis. You can also use Amazon
CloudWatch Events to react to different SageMaker Events.
Amazon CloudWatch API Operations - ANSWER-Amazon CloudWatch API operations
sends training metrics to CloudWatch and creates a dashboard of those metrics. Lastly,
use Amazon Simple Notification Service (Amazon SNS) to send a notification when the
model is overfitting. You can optionally add a Lambda function to the architecture if you
wish to run some mitigation plan (e.g., stop the training when overfitting occurs). In the
scenario, Amazon SNS would suffice as we only need to send notifications.
Amazon Comprehend - ANSWER-(NLP) service that uses machine learning to find
insights and relationships in a text. No machine learning experience required.The
service identifies the language of the text; extracts key phrases, places, people, brands,
or events; understands how positive or negative the text is; analyzes text using
tokenization and parts of speech, and automatically organizes a collection of text files
by topic. You can also use AutoML capabilities in Amazon Comprehend to build a
, custom set of entities or text classification models that are tailored uniquely to your
organization's needs.
Amazon DMS - ANSWER-Database Migration Service to quickly migrate databases to
AWS. Source DB remains available during migration. Must create an EC2 instance to
perform data replication tasks between source and target DBs. No data transformation,
migration only.
Amazon Elastic Inference - ANSWER-Allows you to attach low-cost GPU-powered
acceleration to Amazon EC2 and Sagemaker instances or Amazon ECS tasks, to
reduce the cost of running deep learning inference by up to 75%. Amazon Elastic
Inference supports TensorFlow, Apache MXNet, PyTorch, and ONNX models.
Amazon FSx for Lustre - ANSWER-High-performance distributed file system integrated
with S3. Speeds up ML training. Amazon FSx for Lustre speeds up your training jobs by
serving your Amazon S3 data to Amazon SageMaker at high speeds. The first time you
run a training job, Amazon FSx for Lustre automatically copies data from Amazon S3
and makes it available to Amazon SageMaker. Additionally, the same Amazon FSx file
system can be used for subsequent iterations of training jobs on Amazon SageMaker,
preventing repeated downloads of common Amazon S3 objects. Because of this,
Amazon FSx has the most benefit to training jobs that have training sets in Amazon S3
and in workflows where training jobs must be run several times using different training
algorithms or parameters to see which gives the best result.
Amazon Inspector - ANSWER-An assessment tool used to improve the security of your
applications on EC2 instances.
Amazon Lex - ANSWER-Amazon Lex is a service for building conversational interfaces
into any application using voice
and text. NLP chatbot engine. Built around intents. Utterances invoke intents. Lambda
functions invoked to fulfill intents. Slots specitfy extra info needed by intent.
Amazon Mechanical Turk - ANSWER-A crowdsourced marketplace where anyone can
request, and pay for, individuals to complete tasks that computers are unable to.
Amazon Polly - ANSWER-Amazon Polly is a service that turns text into lifelike speech.
Lexicons to customize pronunciation of specific words & phrases. SSML Speech
Synthesis Markup Language for control over emphasis, pronunciation, breathing,
whispering, speech rate, pitch, and pauses. Speech Marks to encode when
sentence/word starts and ends in the audio stream, useful for lip-synching animation.
Amazon Redshift - ANSWER-Fast, Simple, Cost-Effective Data Warehousing where
large data workloads are required. Amazon Redshift is an enterprise-level, petabyte
scale, fully managed data warehousing service. Amazon Redshift achieves efficient
storage and optimum query performance through a combination of massively parallel
Solved 100% Correct
2 Techniques to Address Outliers in Data - ANSWER-Robust standardization : Ignore
the outliers from the calculation of the mean and standard deviation, then use the
calculated values to scale the variable.
This is called robust standardization or robust data scaling.
Logarithm transformation and Robust Standardization are the correct techniques to
address the outliers in data.
& Log transformation : The Log Transform decreases the effect of the outliers, due to
the normalization of magnitude differences and the model become more robust.
The logarithm, x to log base 10 of x, or x to log base e of x (ln x), or x to log base 2 of x,
is a strong transformation with a major effect on distribution shape. It is commonly used
for reducing right skewness and is often appropriate for measured variables. It can not
be applied to zero or negative values. One unit on a logarithmic scale means a
multiplication by the base of logarithms being used.
2 ways to reduce cost of automatic hyperparameter tuning job - ANSWER-Use less
concurrency while tuning : Since the tuning process learns from each incremental step,
too much concurrency can actually hinder that learning. Use logarithmic scales on
parameter ranges: Logarithmic ranges tend to find optimal values more quickly than
linear ranges.
3 Steps to SageMaker Deployment - ANSWER-1) Create model 2) Create HTTPS
endpoint 3) Create HTTPS endpoint config
3 Visualizations to Spot Outliers - ANSWER-1) Box and whisker 2) Scatter plot 3)
Histogram
4 Methods for Encrypting Objects in S3 - ANSWER-SSE-S3: encrypts objects using
keys managed by AWS. SSE-KMS: Key Mgmt Service manages encryption keys (addtl
security and audit trails). SSE-C: manage your own encryption keys. Client Side
Encryption.
A Hyperparameter tuning job in SageMaker is using more time and resources than you
are willing to spend. What might be one way to make it more efficient? - ANSWER-
Restrict the search space by limiting hyperparameter ranges as much as possible.
Accuracy - ANSWER-TP + TN / TP + TN + FP + FN . How many times the model
predicted correctly over all total preditions right + wrong.
Activation Functions - ANSWER-Define the output of a node/neuron given its input
signals.
,Amazon API Gateway - ANSWER-A fully managed service that makes it easy for
developers to create, publish, maintain, monitor, and secure APIs at any scale. API
Gateway can be used to present an external-facing, single point of entry for Amazon
SageMaker endpoints. API Gateway can be used to front an Amazon SageMaker
inference endpoint as (part of) a REST API, by making use of an API Gateway feature
called mapping templates. This feature makes it possible for the REST API to be
integrated directly with an Amazon SageMaker runtime endpoint, thereby avoiding the
use of any intermediate compute resource (such as AWS Lambda or Amazon ECS
containers) to invoke the endpoint. The result is a solution that is simpler, faster, and
cheaper to run.
Amazon Athena - ANSWER-Amazon Athena is an interactive query service that makes
it easy to analyze data in Amazon S3
using standard SQL
serverless
pay for only the queries you run.
Amazon Augmented AI (Amazon A2I) - ANSWER-Enables you to build the workflows
that are required for human review of machine learning predictions. Amazon Textract is
directly integrated with Amazon A2I so that you can easily get low-confidence results
from Amazon Textract's AnalyzeDocument API operation reviewed by humans.
Amazon CloudWatch - ANSWER-You can monitor Amazon SageMaker using Amazon
CloudWatch, which collects raw data and processes it into readable, near real-time
metrics. These statistics are kept for 15 months so that you can access historical
information and gain a better perspective on how your web application or service is
performing. To help you debug your processing jobs, training jobs, endpoints, transform
jobs, notebook instances, and notebook instance lifecycle configurations, anything an
algorithm container, a model container, or a notebook instance lifecycle configuration
sends to stdout or stderr is also sent to Amazon CloudWatch Logs. In addition to
debugging, you can use these for progress analysis. You can also use Amazon
CloudWatch Events to react to different SageMaker Events.
Amazon CloudWatch API Operations - ANSWER-Amazon CloudWatch API operations
sends training metrics to CloudWatch and creates a dashboard of those metrics. Lastly,
use Amazon Simple Notification Service (Amazon SNS) to send a notification when the
model is overfitting. You can optionally add a Lambda function to the architecture if you
wish to run some mitigation plan (e.g., stop the training when overfitting occurs). In the
scenario, Amazon SNS would suffice as we only need to send notifications.
Amazon Comprehend - ANSWER-(NLP) service that uses machine learning to find
insights and relationships in a text. No machine learning experience required.The
service identifies the language of the text; extracts key phrases, places, people, brands,
or events; understands how positive or negative the text is; analyzes text using
tokenization and parts of speech, and automatically organizes a collection of text files
by topic. You can also use AutoML capabilities in Amazon Comprehend to build a
, custom set of entities or text classification models that are tailored uniquely to your
organization's needs.
Amazon DMS - ANSWER-Database Migration Service to quickly migrate databases to
AWS. Source DB remains available during migration. Must create an EC2 instance to
perform data replication tasks between source and target DBs. No data transformation,
migration only.
Amazon Elastic Inference - ANSWER-Allows you to attach low-cost GPU-powered
acceleration to Amazon EC2 and Sagemaker instances or Amazon ECS tasks, to
reduce the cost of running deep learning inference by up to 75%. Amazon Elastic
Inference supports TensorFlow, Apache MXNet, PyTorch, and ONNX models.
Amazon FSx for Lustre - ANSWER-High-performance distributed file system integrated
with S3. Speeds up ML training. Amazon FSx for Lustre speeds up your training jobs by
serving your Amazon S3 data to Amazon SageMaker at high speeds. The first time you
run a training job, Amazon FSx for Lustre automatically copies data from Amazon S3
and makes it available to Amazon SageMaker. Additionally, the same Amazon FSx file
system can be used for subsequent iterations of training jobs on Amazon SageMaker,
preventing repeated downloads of common Amazon S3 objects. Because of this,
Amazon FSx has the most benefit to training jobs that have training sets in Amazon S3
and in workflows where training jobs must be run several times using different training
algorithms or parameters to see which gives the best result.
Amazon Inspector - ANSWER-An assessment tool used to improve the security of your
applications on EC2 instances.
Amazon Lex - ANSWER-Amazon Lex is a service for building conversational interfaces
into any application using voice
and text. NLP chatbot engine. Built around intents. Utterances invoke intents. Lambda
functions invoked to fulfill intents. Slots specitfy extra info needed by intent.
Amazon Mechanical Turk - ANSWER-A crowdsourced marketplace where anyone can
request, and pay for, individuals to complete tasks that computers are unable to.
Amazon Polly - ANSWER-Amazon Polly is a service that turns text into lifelike speech.
Lexicons to customize pronunciation of specific words & phrases. SSML Speech
Synthesis Markup Language for control over emphasis, pronunciation, breathing,
whispering, speech rate, pitch, and pauses. Speech Marks to encode when
sentence/word starts and ends in the audio stream, useful for lip-synching animation.
Amazon Redshift - ANSWER-Fast, Simple, Cost-Effective Data Warehousing where
large data workloads are required. Amazon Redshift is an enterprise-level, petabyte
scale, fully managed data warehousing service. Amazon Redshift achieves efficient
storage and optimum query performance through a combination of massively parallel
