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Quiz MLS-C01 - Perfect AWS Certified Machine Learning - Specialty Examcollection Dumps

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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

Perform hyperparameter optimization.
- Regularization

Drop out
L1/L2

- Cross validation
- Model initialization
- Neural network architecture (layers/nodes), learning rate, activation functions
- Tree-based models (# of trees, # of levels)
- Linear models (learning rate)

Evaluate machine learning models.
- Avoid overfitting/underfitting (detect and handle bias and variance)
- Metrics (AUC-ROC, accuracy, precision, recall, RMSE, F1 score)
- Confusion matrix
- Offline and online model evaluation, A/B testing
- Compare models using metrics (time to train a model, quality of model, engineering costs)
- Cross validation

Machine Learning Implementation and Operations - 20%

Amazon MLS-C01 Certification Exam consists of 65 multiple-choice and multiple-response questions and has a duration of 180 minutes. It is a challenging exam that requires extensive knowledge and experience in machine learning concepts and technologies. Candidates are required to have a thorough understanding of AWS services and how to use them to build and deploy machine learning models.

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Amazon AWS Certified Machine Learning - Specialty Sample Questions (Q103-Q108):

NEW QUESTION # 103
A data scientist is building a forecasting model for a retail company by using the most recent 5 years of sales records that are stored in a data warehouse. The dataset contains sales records for each of the company's stores across five commercial regions The data scientist creates a working dataset with StorelD. Region. Date, and Sales Amount as columns. The data scientist wants to analyze yearly average sales for each region. The scientist also wants to compare how each region performed compared to average sales across all commercial regions.
Which visualization will help the data scientist better understand the data trend?

A. Create an aggregated dataset by using the Pandas GroupBy function to get average sales for each year for each region Create a bar plot of average sales for each region. Add an extra bar in each facet to represent average sales.
B. Create an aggregated dataset by using the Pandas GroupBy function to get average sales for each year for each store. Create a bar plot, faceted by year, of average sales for each store. Add an extra bar in each facet to represent average sales.
C. Create an aggregated dataset by using the Pandas GroupBy function to get average sales for each year for each store. Create a bar plot, colored by region and faceted by year, of average sales for each store.
Add a horizontal line in each facet to represent average sales.
D. Create an aggregated dataset by using the Pandas GroupBy function to get average sales for each year for each region Create a bar plot, faceted by year, of average sales for each region Add a horizontal line in each facet to represent average sales.

Answer: D

Explanation:
The best visualization for this task is to create a bar plot, faceted by year, of average sales for each region and add a horizontal line in each facet to represent average sales. This way, the data scientist can easily compare the yearly average sales for each region with the overall average sales and see the trends over time. The bar plot also allows the data scientist to see the relative performance of each region within each year and across years. The other options are less effective because they either do not show the yearly trends, do not show the overall average sales, or do not group the data by region.
pandas.DataFrame.groupby - pandas 2.1.4 documentation
pandas.DataFrame.plot.bar - pandas 2.1.4 documentation
Matplotlib - Bar Plot - Online Tutorials Library

NEW QUESTION # 104
A data scientist needs to identify fraudulent user accounts for a company's ecommerce platform. The company wants the ability to determine if a newly created account is associated with a previously known fraudulent user. The data scientist is using AWS Glue to cleanse the company's application logs during ingestion.
Which strategy will allow the data scientist to identify fraudulent accounts?

A. Create an AWS Glue crawler to infer duplicate accounts in the source data.
B. Create a FindMatches machine learning transform in AWS Glue.
C. Search for duplicate accounts in the AWS Glue Data Catalog.
D. Execute the built-in FindDuplicates Amazon Athena query.

Answer: B

Explanation:
The best strategy to identify fraudulent accounts is to create a FindMatches machine learning transform in AWS Glue. The FindMatches transform enables you to identify duplicate or matching records in your dataset, even when the records do not have a common unique identifier and no fields match exactly. This can help you improve fraud detection by finding accounts that are associated with a previously known fraudulent user. You can teach the FindMatches transform your definition of a "duplicate" or a "match" through examples, and it will use machine learning to identify other potential duplicates or matches in your dataset. You can then use the FindMatches transform in your AWS Glue ETL jobs to cleanse your data.
Option A is incorrect because there is no built-in FindDuplicates Amazon Athena query. Amazon Athena is an interactive query service that makes it easy to analyze data in Amazon S3 using standard SQL. However, Amazon Athena does not provide a predefined query to find duplicate records in a dataset. You would have to write your own SQL query to perform this task, which might not be as effective or accurate as using the FindMatches transform.
Option C is incorrect because creating an AWS Glue crawler to infer duplicate accounts in the source data is not a valid strategy. An AWS Glue crawler is a program that connects to a data store, progresses through a prioritized list of classifiers to determine the schema for your data, and then creates metadata tables in the AWS Glue Data Catalog. A crawler does not perform any data cleansing or record matching tasks.
Option D is incorrect because searching for duplicate accounts in the AWS Glue Data Catalog is not a feasible strategy. The AWS Glue Data Catalog is a central repository to store structural and operational metadata for your data assets. The Data Catalog does not store the actual data, but rather the metadata that describes where the data is located, how it is formatted, and what it contains. Therefore, you cannot search for duplicate records in the Data Catalog.
Record matching with AWS Lake Formation FindMatches - AWS Glue
Amazon Athena - Interactive SQL Queries for Data in Amazon S3
AWS Glue Crawlers - AWS Glue
AWS Glue Data Catalog - AWS Glue

NEW QUESTION # 105
A Machine Learning Specialist at a company sensitive to security is preparing a dataset for model training. The dataset is stored in Amazon S3 and contains Personally Identifiable Information (Pll). The dataset:
* Must be accessible from a VPC only.
* Must not traverse the public internet.
How can these requirements be satisfied?

A. Create a VPC endpoint and apply a bucket access policy that allows access from the given VPC endpoint and an Amazon EC2 instance.
B. Create a VPC endpoint and apply a bucket access policy that restricts access to the given VPC endpoint and the VPC.
C. Create a VPC endpoint and use security groups to restrict access to the given VPC endpoint and an Amazon EC2 instance.
D. Create a VPC endpoint and use Network Access Control Lists (NACLs) to allow traffic between only the given VPC endpoint and an Amazon EC2 instance.

Answer: A

NEW QUESTION # 106
A manufacturing company wants to use machine learning (ML) to automate quality control in its facilities. The facilities are in remote locations and have limited internet connectivity. The company has 20 TB of training data that consists of labeled images of defective product parts. The training data is in the corporate on-premises data center.
The company will use this data to train a model for real-time defect detection in new parts as the parts move on a conveyor belt in the facilities. The company needs a solution that minimizes costs for compute infrastructure and that maximizes the scalability of resources for training. The solution also must facilitate the company's use of an ML model in the low-connectivity environments.
Which solution will meet these requirements?

A. Train and evaluate the model on premises. Upload the model to an Amazon S3 bucket. Deploy the model on an Amazon SageMaker hosting services endpoint.
B. Move the training data to an Amazon S3 bucket. Train and evaluate the model by using Amazon SageMaker. Optimize the model by using SageMaker Neo. Deploy the model on a SageMaker hosting services endpoint.
C. Move the training data to an Amazon S3 bucket. Train and evaluate the model by using Amazon SageMaker. Optimize the model by using SageMaker Neo. Set up an edge device in the manufacturing facilities with AWS IoT Greengrass. Deploy the model on the edge device.
D. Train the model on premises. Upload the model to an Amazon S3 bucket. Set up an edge device in the manufacturing facilities with AWS IoT Greengrass. Deploy the model on the edge device.

Answer: B

NEW QUESTION # 107
A Data Science team within a large company uses Amazon SageMaker notebooks to access data stored in Amazon S3 buckets. The IT Security team is concerned that internet-enabled notebook instances create a security vulnerability where malicious code running on the instances could compromise data privacy. The company mandates that all instances stay within a secured VPC with no internet access, and data communication traffic must stay within the AWS network.
How should the Data Science team configure the notebook instance placement to meet these requirements?

A. Associate the Amazon SageMaker notebook with a private subnet in a VPC. Ensure the VPC has S3 VPC endpoints and Amazon SageMaker VPC endpoints attached to it.
B. Associate the Amazon SageMaker notebook with a private subnet in a VPC. Use IAM policies to grant access to Amazon S3 and Amazon SageMaker.
C. Associate the Amazon SageMaker notebook with a private subnet in a VPC. Ensure the VPC has a NAT gateway and an associated security group allowing only outbound connections to Amazon S3 and Amazon SageMaker.
D. Associate the Amazon SageMaker notebook with a private subnet in a VPC. Place the Amazon SageMaker endpoint and S3 buckets within the same VPC.

Answer: C

NEW QUESTION # 108
......

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