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Amazon AWS Certified Machine Learning - Specialty Sample Questions (Q213-Q218):

NEW QUESTION # 213
A monitoring service generates 1 TB of scale metrics record data every minute A Research team performs queries on this data using Amazon Athena The queries run slowly due to the large volume of data, and the team requires better performance How should the records be stored in Amazon S3 to improve query performance?

A. RecordIO
B. CSV files
C. Compressed JSON
D. Parquet files

Answer: D

Explanation:
Parquet is a columnar storage format that can store data in a compressed and efficient way. Parquet files can improve query performance by reducing the amount of data that needs to be scanned, as only the relevant columns are read from the files. Parquet files can also support predicate pushdown, which means that the filtering conditions are applied at the storage level, further reducing the data that needs to be processed.
Parquet files are compatible with Amazon Athena, which can leverage the benefits of the columnar format and provide faster and cheaper queries. Therefore, the records should be stored in Parquet files in Amazon S3 to improve query performance.
Columnar Storage Formats - Amazon Athena
Parquet SerDe - Amazon Athena
Optimizing Amazon Athena Queries - Amazon Athena
Parquet - Apache Software Foundation

NEW QUESTION # 214
A manufacturing company wants to monitor its devices for anomalous behavior. A data scientist has trained an Amazon SageMaker scikit-learn model that classifies a device as normal or anomalous based on its 4-day telemetry. The 4-day telemetry of each device is collected in a separate file and is placed in an Amazon S3 bucket once every hour. The total time to run the model across the telemetry for all devices is 5 minutes.
What is the MOST cost-effective solution for the company to use to run the model across the telemetry for all the devices?

A. SageMaker Asynchronous Inference
B. SageMaker Processing
C. SageMaker Batch Transform
D. A SageMaker multi-container endpoint

Answer: C

Explanation:
The task involves periodic inference (hourly batches of telemetry data) that can be processed in bulk, with the total processing time under 5 minutes. The most cost-effective solution for this batch prediction scenario is SageMaker Batch Transform, as it avoids the need for always-on endpoints.
"Use Batch Transform when you: ... Don't need a persistent endpoint and want to process batches of data at once. This is typically the most cost-efficient way to process large volumes of input data periodically." Since the use case doesn't require low latency or asynchronous real-time response, and processing takes only minutes, Batch Transform is the optimal fit.

NEW QUESTION # 215
An online delivery company wants to choose the fastest courier for each delivery at the moment an order is placed. The company wants to implement this feature for existing users and new users of its application. Data scientists have trained separate models with XGBoost for this purpose, and the models are stored in Amazon S3. There is one model fof each city where the company operates.
The engineers are hosting these models in Amazon EC2 for responding to the web client requests, with one instance for each model, but the instances have only a 5% utilization in CPU and memory, ....operation engineers want to avoid managing unnecessary resources.
Which solution will enable the company to achieve its goal with the LEAST operational overhead?

A. Prepare an Amazon SageMaker Docker container based on the open-source multi-model server.
Remove the existing instances and create a multi-model endpoint in SageMaker instead, pointing to the S3 bucket containing all the models Invoke the endpoint from the web client at runtime, specifying the TargetModel parameter according to the city of each request.
B. Keep only a single EC2 instance for hosting all the models. Install a model server in the instance and load each model by pulling it from Amazon S3. Integrate the instance with the web client using Amazon API Gateway for responding to the requests in real time, specifying the target resource according to the city of each request.
C. Create an Amazon SageMaker notebook instance for pulling all the models from Amazon S3 using the boto3 library. Remove the existing instances and use the notebook to perform a SageMaker batch transform for performing inferences offline for all the possible users in all the cities. Store the results in different files in Amazon S3. Point the web client to the files.
D. Prepare a Docker container based on the prebuilt images in Amazon SageMaker. Replace the existing instances with separate SageMaker endpoints. one for each city where the company operates. Invoke the endpoints from the web client, specifying the URL and EndpomtName parameter according to the city of each request.

Answer: A

Explanation:
The best solution for this scenario is to use a multi-model endpoint in Amazon SageMaker, which allows hosting multiple models on the same endpoint and invoking them dynamically at runtime. This way, the company can reduce the operational overhead of managing multiple EC2 instances and model servers, and leverage the scalability, security, and performance of SageMaker hosting services. By using a multi-model endpoint, the company can also save on hosting costs by improving endpoint utilization and paying only for the models that are loaded in memory and the API calls that are made. To use a multi-model endpoint, the company needs to prepare a Docker container based on the open-source multi-model server, which is a framework-agnostic library that supports loading and serving multiple models from Amazon S3. The company can then create a multi-model endpoint in SageMaker, pointing to the S3 bucket containing all the models, and invoke the endpoint from the web client at runtime, specifying the TargetModel parameter according to the city of each request. This solution also enables the company to add or remove models from the S3 bucket without redeploying the endpoint, and to use different versions of the same model for different cities if needed. References:
* Use Docker containers to build models
* Host multiple models in one container behind one endpoint
* Multi-model endpoints using Scikit Learn
* Multi-model endpoints using XGBoost

NEW QUESTION # 216
A Machine Learning Specialist has built a model using Amazon SageMaker built-in algorithms and is not getting expected accurate results The Specialist wants to use hyperparameter optimization to increase the model's accuracy Which method is the MOST repeatable and requires the LEAST amount of effort to achieve this?

A. Launch multiple training jobs in parallel with different hyperparameters
B. Create an AWS Step Functions workflow that monitors the accuracy in Amazon CloudWatch Logs and relaunches the training job with a defined list of hyperparameters
C. Create a hyperparameter tuning job and set the accuracy as an objective metric.
D. Create a random walk in the parameter space to iterate through a range of values that should be used for each individual hyperparameter

Answer: C

Explanation:
A hyperparameter tuning job is a feature of Amazon SageMaker that allows automatically finding the best combination of hyperparameters for a machine learning model. Hyperparameters are high-level parameters that influence the learning process and the performance of the model, such as the learning rate, the number of layers, the regularization factor, etc. A hyperparameter tuning job works by launching multiple training jobs with different hyperparameters, evaluating the results using an objective metric, and choosing the next set of hyperparameters to try based on a search strategy. The objective metric is a measure of the quality of the model, such as accuracy, precision, recall, etc. The search strategy is a method of exploring the hyperparameter space, such as random search, grid search, or Bayesian optimization.
Among the four options, option C is the most repeatable and requires the least amount of effort to use hyperparameter optimization to increase the model's accuracy. This option involves the following steps:
* Create a hyperparameter tuning job: Amazon SageMaker provides an easy-to-use interface for creating a hyperparameter tuning job, either through the AWS Management Console, the AWS CLI, or the AWS SDKs. To create a hyperparameter tuning job, the Machine Learning Specialist needs to specify the following information:
* The name and type of the algorithm to use, either a built-in algorithm or a custom algorithm.
* The ranges and types of the hyperparameters to tune, such as categorical, continuous, or integer.
* The name and type of the objective metric to optimize, such as accuracy, and whether to maximize or minimize it.
* The resource limits for the tuning job, such as the maximum number of training jobs and the maximum parallel training jobs.
* The input data channels and the output data location for the training jobs.
* The configuration of the training instances, such as the instance type, the instance count, the volume size, etc.
* Set the accuracy as an objective metric: To use accuracy as an objective metric, the Machine Learning Specialist needs to ensure that the training algorithm writes the accuracy value to a file called metric_definitions in JSON format and prints it to stdout or stderr. For example, the file can contain the following content:
This means that the training algorithm prints a line like this:
* Amazon SageMaker reads the accuracy value from the line and uses it to evaluate and compare the training jobs.
The other options are not as repeatable and require more effort than option C for the following reasons:
* Option A: This option requires manually launching multiple training jobs in parallel with different hyperparameters, which can be tedious and error-prone. It also requires manually monitoring and comparing the results of the training jobs, which can be time-consuming and subjective.
* Option B: This option requires writing code to create an AWS Step Functions workflow that monitors the accuracy in Amazon CloudWatch Logs and relaunches the training job with a defined list of hyperparameters, which can be complex and challenging. It also requires maintaining and updating the list of hyperparameters, which can be inefficient and suboptimal.
* Option D: This option requires writing code to create a random walk in the parameter space to iterate through a range of values that should be used for each individual hyperparameter, which can be unreliable and unpredictable. It also requires defining and implementing a stopping criterion, which can be arbitrary and inconsistent.
References:
* Automatic Model Tuning - Amazon SageMaker
* Define Metrics to Monitor Model Performance

NEW QUESTION # 217
A Machine Learning Specialist is working with multiple data sources containing billions of records that need to be joined. What feature engineering and model development approach should the Specialist take with a dataset this large?

A. Use Amazon EMR for feature engineering and Amazon SageMaker SDK for model development
B. Use an Amazon SageMaker notebook for both feature engineering and model development
C. Use Amazon ML for both feature engineering and model development.
D. Use an Amazon SageMaker notebook for feature engineering and Amazon ML for model development

Answer: A

Explanation:
Amazon EMR is a service that can process large amounts of data efficiently and cost-effectively. It can run distributed frameworks such as Apache Spark, which can perform feature engineering on big data. Amazon SageMaker SDK is a Python library that can interact with Amazon SageMaker service to train and deploy machine learning models. It can also use Amazon EMR as a data source for training data. References:
Amazon EMR
Amazon SageMaker SDK

NEW QUESTION # 218
......

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