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The AWS Certified Machine Learning - Specialty certification exam is a challenging and rigorous exam that requires a significant amount of preparation and study. To prepare for the exam, candidates can take advantage of a wide range of resources, including online courses, study guides, and practice exams. They can also attend training courses and workshops offered by AWS or other training providers.

AWS Machine Learning Specialty Exam Syllabus Topics:

Section
Objectives

Data Engineering - 20%

Create data repositories for machine learning.
- Identify data sources (e.g., content and location, primary sources such as user data)
- Determine storage mediums (e.g., DB, Data Lake, S3, EFS, EBS)

Identify and implement a data ingestion solution.
- Data job styles/types (batch load, streaming)

Kinesis
Kinesis Analytics
Kinesis Firehose
EMR
Glue

- Data ingestion pipelines (Batch-based ML workloads and streaming-based ML workloads)
- Job scheduling

Identify and implement a data transformation solution.
- Transforming data transit (ETL: Glue, EMR, AWS Batch)
- Handle ML-specific data using map reduce (Hadoop, Spark, Hive)

Exploratory Data Analysis - 24%

Sanitize and prepare data for modeling.
- Identify and handle missing data, corrupt data, stop words, etc.
- Formatting, normalizing, augmenting, and scaling data
- Labeled data (recognizing when you have enough labeled data and identifying mitigation strategies [Data labeling tools (Mechanical Turk, manual labor)])

Perform feature engineering.
- Identify and extract features from data sets, including from data sources such as text, speech, image, public datasets, etc.
- Analyze/evaluate feature engineering concepts (binning, tokenization, outliers, synthetic features, 1 hot encoding, reducing dimensionality of data)

Analyze and visualize data for machine learning.
- Graphing (scatter plot, time series, histogram, box plot)
- Interpreting descriptive statistics (correlation, summary statistics, p value)
- Clustering (hierarchical, diagnosing, elbow plot, cluster size)

Modeling - 36%

Frame business problems as machine learning problems.
- Determine when to use/when not to use ML
- Know the difference between supervised and unsupervised learning
- Selecting from among classification, regression, forecasting, clustering, recommendation, etc.

Select the appropriate model(s) for a given machine learning problem.
- Xgboost, logistic regression, K-means, linear regression, decision trees, random forests, RNN, CNN, Ensemble, Transfer learning
- Express intuition behind models

Train machine learning models.
- Train validation test split, cross-validation
- Optimizer, gradient descent, loss functions, local minima, convergence, batches, probability, etc.
- Compute choice (GPU vs. CPU, distributed vs. non-distributed, platform [Spark vs. non-Spark])
- Model updates and retraining

Batch vs. real-time/online

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Amazon AWS Certified Machine Learning - Specialty Sample Questions (Q142-Q147):

NEW QUESTION # 142
A gaming company has launched an online game where people can start playing for free but they need to pay if they choose to use certain features The company needs to build an automated system to predict whether or not a new user will become a paid user within 1 year The company has gathered a labeled dataset from 1 million users The training dataset consists of 1.000 positive samples (from users who ended up paying within 1 year) and
999.000 negative samples (from users who did not use any paid features) Each data sample consists of 200 features including user age, device, location, and play patterns Using this dataset for training, the Data Science team trained a random forest model that converged with over
99% accuracy on the training set However, the prediction results on a test dataset were not satisfactory.
Which of the following approaches should the Data Science team take to mitigate this issue? (Select TWO.)

A. Add more deep trees to the random forest to enable the model to learn more features.
B. Change the cost function so that false positives have a higher impact on the cost value than false negatives
C. Change the cost function so that false negatives have a higher impact on the cost value than false positives
D. Generate more positive samples by duplicating the positive samples and adding a small amount of noise to the duplicated data.
E. indicate a copy of the samples in the test database in the training dataset

Answer: C,D

Explanation:
The Data Science team is facing a problem of imbalanced data, where the positive class (paid users) is much less frequent than the negative class (non-paid users). This can cause the random forest model to be biased towards the majority class and have poor performance on the minority class. To mitigate this issue, the Data Science team can try the following approaches:
* C. Generate more positive samples by duplicating the positive samples and adding a small amount of noise to the duplicated data. This is a technique called data augmentation, which can help increase the size and diversity of the training data for the minority class. This can help the random forest model learn more features and patterns from the positive class and reduce the imbalance ratio.
* D. Change the cost function so that false negatives have a higher impact on the cost value than false positives. This is a technique called cost-sensitive learning, which can assign different weights or costs to different classes or errors. By assigning a higher cost to false negatives (predicting non-paid when the user is actually paid), the random forest model can be more sensitive to the minority class and try to minimize the misclassification of the positive class.
References:
* Bagging and Random Forest for Imbalanced Classification
* Surviving in a Random Forest with Imbalanced Datasets
* machine learning - random forest for imbalanced data? - Cross Validated
* Biased Random Forest For Dealing With the Class Imbalance Problem

NEW QUESTION # 143
A large JSON dataset for a project has been uploaded to a private Amazon S3 bucket The Machine Learning Specialist wants to securely access and explore the data from an Amazon SageMaker notebook instance A new VPC was created and assigned to the Specialist How can the privacy and integrity of the data stored in Amazon S3 be maintained while granting access to the Specialist for analysis?

A. Launch the SageMaker notebook instance within the VPC with SageMaker-provided internet access enabled. Generate an S3 pre-signed URL for access to data in the bucket
B. Launch the SageMaker notebook instance within the VPC and create an S3 VPC endpoint for the notebook to access the data Define a custom S3 bucket policy to only allow requests from your VPC to access the S3 bucket
C. Launch the SageMaker notebook instance within the VPC and create an S3 VPC endpoint for the notebook to access the data Copy the JSON dataset from Amazon S3 into the ML storage volume on the SageMaker notebook instance and work against the local dataset
D. Launch the SageMaker notebook instance within the VPC with SageMaker-provided internet access enabled Use an S3 ACL to open read privileges to the everyone group

Answer: C

NEW QUESTION # 144
A data scientist has a dataset of machine part images stored in Amazon Elastic File System (Amazon EFS). The data scientist needs to use Amazon SageMaker to create and train an image classification machine learning model based on this dataset. Because of budget and time constraints, management wants the data scientist to create and train a model with the least number of steps and integration work required.
How should the data scientist meet these requirements?

A. Mount the EFS file system to an Amazon EC2 instance and use the AWS CLI to copy the data to an Amazon S3 bucket. Run the SageMaker training job with Amazon S3 as the data source.
B. Run a SageMaker training job with an EFS file system as the data source.
C. Mount the EFS file system to a SageMaker notebook and run a script that copies the data to an Amazon FSx for Lustre file system. Run the SageMaker training job with the FSx for Lustre file system as the data source.
D. Launch a transient Amazon EMR cluster. Configure steps to mount the EFS file system and copy the data to an Amazon S3 bucket by using S3DistCp. Run the SageMaker training job with Amazon S3 as the data source.

Answer: C

NEW QUESTION # 145
A machine learning specialist works for a fruit processing company and needs to build a system that categorizes apples into three types. The specialist has collected a dataset that contains 150 images for each type of apple and applied transfer learning on a neural network that was pretrained on ImageNet with this dataset.
The company requires at least 85% accuracy to make use of the model.
After an exhaustive grid search, the optimal hyperparameters produced the following:
68% accuracy on the training set
67% accuracy on the validation set
What can the machine learning specialist do to improve the system's accuracy?

A. Train a new model using the current neural network architecture.
B. Add more data to the training set and retrain the model using transfer learning to reduce the bias.
C. Upload the model to an Amazon SageMaker notebook instance and use the Amazon SageMaker HPO feature to optimize the model's hyperparameters.
D. Use a neural network model with more layers that are pretrained on ImageNet and apply transfer learning to increase the variance.

Answer: B

NEW QUESTION # 146
A company needs to quickly make sense of a large amount of data and gain insight from it. The data is in different formats, the schemas change frequently, and new data sources are added regularly. The company wants to use AWS services to explore multiple data sources, suggest schemas, and enrich and transform the data. The solution should require the least possible coding effort for the data flows and the least possible infrastructure management.
Which combination of AWS services will meet these requirements?

A. Amazon Kinesis Data Analytics for data ingestionAmazon EMR for data discovery, enrichment, and transformationAmazon Redshift for querying and analyzing the results in Amazon S3
B. Amazon EMR for data discovery, enrichment, and transformationAmazon Athena for querying and analyzing the results in Amazon S3 using standard SQLAmazon QuickSight for reporting and getting insights
C. AWS Data Pipeline for data transferAWS Step Functions for orchestrating AWS Lambda jobs for data discovery, enrichment, and transformationAmazon Athena for querying and analyzing the results in Amazon S3 using standard SQLAmazon QuickSight for reporting and getting insights
D. AWS Glue for data discovery, enrichment, and transformationAmazon Athena for querying and analyzing the results in Amazon S3 using standard SQLAmazon QuickSight for reporting and getting insights

Answer: D

Explanation:
The best combination of AWS services to meet the requirements of data discovery, enrichment, transformation, querying, analysis, and reporting with the least coding and infrastructure management is AWS Glue, Amazon Athena, and Amazon QuickSight. These services are:
* AWS Glue for data discovery, enrichment, and transformation. AWS Glue is a serverless data integration service that automatically crawls, catalogs, and prepares data from various sources and formats. It also provides a visual interface called AWS Glue DataBrew that allows users to apply over
250 transformations to clean, normalize, and enrich data without writing code1
* Amazon Athena for querying and analyzing the results in Amazon S3 using standard SQL. Amazon Athena is a serverless interactive query service that allows users to analyze data in Amazon S3 using standard SQL. It supports a variety of data formats, such as CSV, JSON, ORC, Parquet, and Avro. It also integrates with AWS Glue Data Catalog to provide a unified view of the data sources and schemas2
* Amazon QuickSight for reporting and getting insights. Amazon QuickSight is a serverless business intelligence service that allows users to create and share interactive dashboards and reports. It also provides ML-powered features, such as anomaly detection, forecasting, and natural language queries, to help users discover hidden insights from their data3 The other options are not suitable because they either require more coding effort, more infrastructure management, or do not support the desired use cases. For example:
* Option A uses Amazon EMR for data discovery, enrichment, and transformation. Amazon EMR is a managed cluster platform that runs Apache Spark, Apache Hive, and other open-source frameworks for big data processing. It requires users to write code in languages such as Python, Scala, or SQL to perform data integration tasks. It also requires users to provision, configure, and scale the clusters according to their needs4
* Option B uses Amazon Kinesis Data Analytics for data ingestion. Amazon Kinesis Data Analytics is a service that allows users to process streaming data in real time using SQL or Apache Flink. It is not suitable for data discovery, enrichment, and transformation, which are typically batch-oriented tasks. It also requires users to write code to define the data processing logic and the output destination5
* Option D uses AWS Data Pipeline for data transfer and AWS Step Functions for orchestrating AWS Lambda jobs for data discovery, enrichment, and transformation. AWS Data Pipeline is a service that helps users move data between AWS services and on-premises data sources. AWS Step Functions is a service that helps users coordinate multiple AWS services into workflows. AWS Lambda is a service that lets users run code without provisioning or managing servers. These services require users to write code to define the data sources, destinations, transformations, and workflows. They also require users to manage the scalability, performance, and reliability of the data pipelines.
1: AWS Glue - Data Integration Service - Amazon Web Services
2: Amazon Athena - Interactive SQL Query Service - AWS
3: Amazon QuickSight - Business Intelligence Service - AWS
4: Amazon EMR - Amazon Web Services
5: Amazon Kinesis Data Analytics - Amazon Web Services
AWS Data Pipeline - Amazon Web Services
AWS Step Functions - Amazon Web Services
AWS Lambda - Amazon Web Services

NEW QUESTION # 147
......

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