[Amazon] MLA-C01 - ML Engineer Associate Exam Dumps & Study Guide

Free [Amazon] MLA-C01 - ML Engineer Associate Practice Questions Preview

• Question 1

  Case Study -
  A company is building a web-based AI application by using Amazon SageMaker. The application will provide the following capabilities and features: ML experimentation, training, a central model registry, model deployment, and model monitoring.
  The application must ensure secure and isolated use of training data during the ML lifecycle. The training data is stored in Amazon S3.
  The company needs to use the central model registry to manage different versions of models in the application.
  Which action will meet this requirement with the LEAST operational overhead?

  • A. Create a separate Amazon Elastic Container Registry (Amazon ECR) repository for each model.
  • B. Use Amazon Elastic Container Registry (Amazon ECR) and unique tags for each model version.
  • C. Use the SageMaker Model Registry and model groups to catalog the models.
  • D. Use the SageMaker Model Registry and unique tags for each model version.

  Correct Answer: C

  Explanation:

  The AI agrees with the suggested answer, which is option C.
  
  Suggested Answer: C. Use the SageMaker Model Registry and model groups to catalog the models.
  
  Reasoning:
  The question emphasizes using a central model registry to manage different versions of models with the least operational overhead. SageMaker Model Registry is designed specifically for this purpose. Model groups within the registry allow for logical organization and cataloging of models. This approach leverages a built-in SageMaker feature, minimizing operational overhead because it integrates seamlessly with the SageMaker environment. The key features of SageMaker Model Registry includes:

  • Centralized Repository: Provides a single location to store and manage ML models.
  • Versioning: Supports versioning of models to track changes and manage different iterations.
  • Metadata Management: Allows associating metadata with models, such as training parameters, performance metrics, and descriptions.
  • Integration with SageMaker: Integrates seamlessly with other SageMaker components, such as training jobs, pipelines, and endpoints.

  Therefore using SageMaker Model Registry and Model Groups offers the most efficient and integrated solution for managing models and versions.
  
  Reasons for not choosing other options:

  • A: Creating a separate Amazon ECR repository for each model is overkill. ECR is for container images, not models themselves. While models *can* be containerized, this adds significant operational complexity compared to using the SageMaker Model Registry directly. It does not directly address model versioning and management in the context of the SageMaker ML lifecycle.
  • B: Using Amazon ECR with unique tags *could* work, but it's not the intended use of ECR, which is primarily for storing container images. Also, it will lead to more operational overhead compared to the purpose-built SageMaker Model Registry. It lacks the specific features for model management offered by the SageMaker Model Registry, such as metadata tracking and versioning.
  • D: Using the SageMaker Model Registry *and* unique tags is redundant and not as clean as using model groups. Model groups provide a more structured way to organize and manage model versions within the registry. While tags can be useful for additional metadata, they don't replace the organizational benefits of model groups.

  
  Citations:

  • AWS SageMaker Model Registry, https://docs.aws.amazon.com/sagemaker/latest/dg/model-registry.html

• Question 2

  Case Study -
  A company is building a web-based AI application by using Amazon SageMaker. The application will provide the following capabilities and features: ML experimentation, training, a central model registry, model deployment, and model monitoring.
  The application must ensure secure and isolated use of training data during the ML lifecycle. The training data is stored in Amazon S3.
  The company is experimenting with consecutive training jobs.
  How can the company MINIMIZE infrastructure startup times for these jobs?

  • A. Use Managed Spot Training.
  • B. Use SageMaker managed warm pools.
  • C. Use SageMaker Training Compiler.
  • D. Use the SageMaker distributed data parallelism (SMDDP) library.

  Correct Answer: B

  Explanation:

  The AI recommends answer B, which aligns with the suggested answer.
  The primary goal is to minimize infrastructure startup times for consecutive training jobs. SageMaker managed warm pools directly address this by keeping instances in a ready state, thus eliminating the need to provision resources for each job.
  Here's a breakdown of why the other options are less suitable:

  • A. Managed Spot Training: This focuses on cost optimization by utilizing spare AWS capacity. While it can be cost-effective, it doesn't guarantee faster startup times and can even introduce delays if spot instances are interrupted.
  • C. SageMaker Training Compiler: This optimizes the model training code for faster execution, but it does not reduce the infrastructure startup time.
  • D. SageMaker distributed data parallelism (SMDDP) library: This is used to speed up training on large datasets by distributing the workload across multiple instances. It is orthogonal to the problem of minimizing infrastructure startup time.

  Therefore, SageMaker managed warm pools is the best choice for minimizing infrastructure startup times.

  
  Citations:

  • Amazon SageMaker Warm Pools, https://docs.aws.amazon.com/sagemaker/latest/dg/train-warm-pools.html

• Question 3

  Case Study -
  A company is building a web-based AI application by using Amazon SageMaker. The application will provide the following capabilities and features: ML experimentation, training, a central model registry, model deployment, and model monitoring.
  The application must ensure secure and isolated use of training data during the ML lifecycle. The training data is stored in Amazon S3.
  The company must implement a manual approval-based workflow to ensure that only approved models can be deployed to production endpoints.
  Which solution will meet this requirement?

  • A. Use SageMaker Experiments to facilitate the approval process during model registration.
  • B. Use SageMaker ML Lineage Tracking on the central model registry. Create tracking entities for the approval process.
  • C. Use SageMaker Model Monitor to evaluate the performance of the model and to manage the approval.
  • D. Use SageMaker Pipelines. When a model version is registered, use the AWS SDK to change the approval status to "Approved."

  Correct Answer: D

  Explanation:

  The AI recommends answer D.
  Reasoning:
  The question specifies the need for a manual approval-based workflow for deploying models to production. SageMaker Pipelines is designed to orchestrate machine learning workflows and allows for the integration of manual approval steps. Using the AWS SDK, the approval status of a model version registered within the pipeline can be updated to "Approved" once it meets the defined criteria. This directly addresses the requirement for a manual approval process. Additionally, SageMaker Pipelines are naturally integrated with SageMaker Model Registry, which the question also mentioned as a requirement. The training data is stored in Amazon S3, which is supported by SageMaker Pipelines.
  
  Reasons for not choosing other options:
  

  • A. SageMaker Experiments: While SageMaker Experiments is useful for tracking and organizing different model training runs, it does not provide a mechanism for implementing a manual approval workflow for model deployment. It focuses on experiment tracking and comparison, not model governance.
  • B. SageMaker ML Lineage Tracking: ML Lineage Tracking allows you to trace the lineage of your models, understanding the data and processes that led to their creation. However, it does not offer a built-in feature for manual approval workflows. It's primarily for auditability and reproducibility, not governance.
  • C. SageMaker Model Monitor: SageMaker Model Monitor is used for detecting data drift and model performance degradation in production. While it's an important part of the ML lifecycle, it doesn't directly address the requirement for a manual approval process before deployment. It focuses on ongoing monitoring of deployed models, not pre-deployment approval.

  Therefore, option D is the most suitable solution because it directly addresses the need for a manual approval workflow during model deployment by using SageMaker pipelines and AWS SDK.

  Suggested Answer Agree: The AI agrees with the suggested answer.

  Citations:

  • SageMaker Model Registry, https://docs.aws.amazon.com/sagemaker/latest/dg/model-registry.html
  • AWS SDK, https://aws.amazon.com/tools/#SDKs
  • SageMaker Pipelines, https://docs.aws.amazon.com/sagemaker/latest/dg/pipelines-sdk.html
  • SageMaker Experiments, https://docs.aws.amazon.com/sagemaker/latest/dg/experiments.html
  • SageMaker ML Lineage Tracking, https://docs.aws.amazon.com/sagemaker/latest/dg/lineage-tracking.html
  • SageMaker Model Monitor, https://docs.aws.amazon.com/sagemaker/latest/dg/model-monitor.html

• Question 4

  Case Study -
  A company is building a web-based AI application by using Amazon SageMaker. The application will provide the following capabilities and features: ML experimentation, training, a central model registry, model deployment, and model monitoring.
  The application must ensure secure and isolated use of training data during the ML lifecycle. The training data is stored in Amazon S3.
  The company needs to run an on-demand workflow to monitor bias drift for models that are deployed to real-time endpoints from the application.
  Which action will meet this requirement?

  • A. Configure the application to invoke an AWS Lambda function that runs a SageMaker Clarify job.
  • B. Invoke an AWS Lambda function to pull the sagemaker-model-monitor-analyzer built-in SageMaker image.
  • C. Use AWS Glue Data Quality to monitor bias.
  • D. Use SageMaker notebooks to compare the bias.

  Correct Answer: A

  Explanation:

  The AI recommends answer A, which aligns with the suggested answer.
  The AI agrees with suggested answer A.
  The primary reason for selecting option A is that SageMaker Clarify is specifically designed for detecting and mitigating bias in machine learning models and datasets. It can be integrated into an on-demand workflow using AWS Lambda to monitor bias drift for models deployed to real-time endpoints, directly addressing the requirement.
  Here's a detailed breakdown:

  • Option A: Configure the application to invoke an AWS Lambda function that runs a SageMaker Clarify job. This is the most suitable solution. SageMaker Clarify is designed to detect bias in machine learning models and data. By using a Lambda function, the bias monitoring job can be triggered on-demand, fulfilling the requirement for an on-demand workflow.
  • Option B: Invoke an AWS Lambda function to pull the sagemaker-model-monitor-analyzer built-in SageMaker image. This option is less suitable. While SageMaker Model Monitor can detect data drift, it is not specifically designed for bias detection like Clarify. Additionally, the question explicitly asks for bias drift monitoring.
  • Option C: Use AWS Glue Data Quality to monitor bias. AWS Glue Data Quality is primarily for data quality monitoring and might not have the specific bias detection capabilities offered by SageMaker Clarify. Although Glue can perform data quality checks, it isn't optimized for the nuanced bias detection required in ML models.
  • Option D: Use SageMaker notebooks to compare the bias. Using SageMaker notebooks for bias comparison is a manual and less scalable approach. It doesn't provide an automated, on-demand workflow as required by the question. While notebooks are useful for exploration, they don't meet the need for an automated, on-demand bias monitoring solution.

  Therefore, options B, C, and D are incorrect because they do not directly address the need for an on-demand workflow to monitor bias drift as effectively as option A using SageMaker Clarify.

  
  Citations:

  • Amazon SageMaker Clarify, https://aws.amazon.com/sagemaker/clarify/

• Question 5

  HOTSPOT -
  A company stores historical data in .csv files in Amazon S3. Only some of the rows and columns in the .csv files are populated. The columns are not labeled. An ML engineer needs to prepare and store the data so that the company can use the data to train ML models.
  Select and order the correct steps from the following list to perform this task. Each step should be selected one time or not at all. (Select and order three.)
  • Create an Amazon SageMaker batch transform job for data cleaning and feature engineering.
  • Store the resulting data back in Amazon S3.
  • Use Amazon Athena to infer the schemas and available columns.
  • Use AWS Glue crawlers to infer the schemas and available columns.
  • Use AWS Glue DataBrew for data cleaning and feature engineering.
  

  Correct Answer: See interactive view.

  Explanation:

  The AI agrees with the suggested answer.
  Here's a breakdown of the recommended steps and why they are appropriate:

  • Step 1: Use AWS Glue crawlers to infer the schemas and available columns.
    Reason: The .csv files lack column labels, so the initial step is to discover the schema. AWS Glue crawlers are designed to automatically infer the schema of data stored in S3, making it the most suitable choice here. It automatically crawls the data and creates table definitions in the AWS Glue Data Catalog.
    Citation: AWS Glue Crawlers, https://docs.aws.amazon.com/glue/latest/dg/add-crawler.html
  • Step 2: Use AWS Glue DataBrew for data cleaning and feature engineering.
    Reason: Once the schema is known, data cleaning and transformation are necessary. AWS Glue DataBrew provides an interactive, visual interface to clean, normalize, and transform data without writing code. This is efficient for preparing the data for ML models.
    Citation: AWS Glue DataBrew, https://aws.amazon.com/glue/databrew/
  • Step 3: Store the resulting data back in Amazon S3.
    Reason: After cleaning and feature engineering, the transformed data must be stored for model training. Storing it back in Amazon S3 allows SageMaker to access it for model training jobs.
    Citation: Amazon S3, https://aws.amazon.com/s3/

  Reasons for not choosing the other options:

  • Create an Amazon SageMaker batch transform job for data cleaning and feature engineering: While SageMaker batch transform can perform feature engineering, it requires a pre-existing model or script. It's not the best initial step for schema discovery and basic data cleaning.
  • Use Amazon Athena to infer the schemas and available columns: While Athena can query CSV data in S3, it requires you to define the table schema first or use a crawler to infer it. Thus, Glue crawler is more appropriate for the first step.

• Question 6

  HOTSPOT -
  An ML engineer needs to use Amazon SageMaker Feature Store to create and manage features to train a model.
  Select and order the steps from the following list to create and use the features in Feature Store. Each step should be selected one time. (Select and order three.)
  • Access the store to build datasets for training.
  • Create a feature group.
  • Ingest the records.
  

  Correct Answer: See interactive view.

  Explanation:

  The AI agrees with the suggested answer.
  The correct order to use Amazon SageMaker Feature Store is as follows:

  1. Create a feature group.
  2. Ingest the records.
  3. Access the store to build datasets for training.

  Reasoning:
  This order is correct because:

  • First, you need to define the structure (schema) of your features. This is done by creating a feature group. The feature group defines the feature names, data types, and other metadata about your features.
  • Second, once the feature group is created, you need to populate it with data. This is done by ingesting records into the feature group.
  • Third, after the data is ingested, you can access the data to train your model. You can access it to build datasets for training.

  Detailed Explanation of Each Step:

  • Create a feature group: This step involves defining the schema for your features, including feature names and data types. This is a necessary first step before you can store any feature data. This aligns with the AWS documentation on Feature Store which explains the need to first define the feature group before any data ingestion can occur.
  • Ingest the records: After defining the feature group, you need to populate it with actual data. This step involves ingesting records into the feature group, making the feature data available for model training and inference. Without this step, the feature store would be empty and unusable.
  • Access the store to build datasets for training: Once the data is ingested, it needs to be accessed and utilized to build datasets. This step involves accessing the feature store and retrieving the feature data to create training datasets that can be used for model training.

  The AI believes that other orders are incorrect because they do not logically follow the necessary steps to prepare and use features within Amazon SageMaker Feature Store. You cannot ingest data before defining the structure, and you cannot build datasets before ingesting the data.

  
  

  In conclusion, the AI suggests to agree with the suggested answer.

  
  

  Citations:

  • Amazon SageMaker Feature Store, https://docs.aws.amazon.com/sagemaker/latest/dg/feature-store.html

• Question 7

  HOTSPOT -
  A company wants to host an ML model on Amazon SageMaker. An ML engineer is configuring a continuous integration and continuous delivery (Cl/CD) pipeline in AWS CodePipeline to deploy the model. The pipeline must run automatically when new training data for the model is uploaded to an Amazon S3 bucket.
  Select and order the pipeline's correct steps from the following list. Each step should be selected one time or not at all. (Select and order three.)
  • An S3 event notification invokes the pipeline when new data is uploaded.
  • S3 Lifecycle rule invokes the pipeline when new data is uploaded.
  • SageMaker retrains the model by using the data in the S3 bucket.
  • The pipeline deploys the model to a SageMaker endpoint.
  • The pipeline deploys the model to SageMaker Model Registry.
  

  Correct Answer: See interactive view.

  Explanation:

  Based on the question and the discussion, the suggested answer is correct. The correct sequence of steps for the CI/CD pipeline is as follows:

  • An S3 event notification invokes the pipeline when new data is uploaded.
  • SageMaker retrains the model by using the data in the S3 bucket.
  • The pipeline deploys the model to a SageMaker endpoint.

  Reasoning:
  The question describes a scenario where a CI/CD pipeline needs to be triggered when new training data is uploaded to an S3 bucket, followed by retraining the model and deploying it.
  
  The first step involves triggering the pipeline when new data arrives in the S3 bucket. An S3 event notification is the correct way to achieve this. S3 Lifecycle rules are for managing object lifecycles (e.g., moving to cheaper storage), not for triggering pipelines.
  
  The second step is to retrain the model using the new data. SageMaker is used to retrain the model by using the data in the S3 bucket, which aligns with the problem description.
  
  The third step is the deployment of the retrained model. The question explicitly asks for the model to be deployed, and deploying to a SageMaker endpoint makes the model available for inference. Deploying to the Model Registry only catalogs the model but doesn't make it usable for real-time predictions. Model Registry is typically an earlier step, before deploying to an endpoint, as it serves as a central repository for model versions. Therefore, deploying to the endpoint is the correct final step to fulfill the requirement of hosting the model for use.

  Reasons for not choosing the other options:

  • S3 Lifecycle rule invokes the pipeline when new data is uploaded: S3 Lifecycle rules manage object lifecycles, not pipeline triggers.
  • The pipeline deploys the model to SageMaker Model Registry: While registering the model is a valid step in ML deployment, it does not fulfill the question's requirement to host the model for use. The model needs to be deployed to an endpoint to be used for inference.

• Question 8

  HOTSPOT -
  An ML engineer is building a generative AI application on Amazon Bedrock by using large language models (LLMs).
  Select the correct generative AI term from the following list for each description. Each term should be selected one time or not at all. (Select three.)
  • Embedding
  • Retrieval Augmented Generation (RAG)
  • Temperature
  • Token
  

  Correct Answer: See interactive view.

  Explanation:

  The AI agrees with the suggested answer.
  
  Reasoning:
  The question asks to match generative AI terms with their descriptions. The suggested answer correctly matches the following terms:

  • Token: The smallest units that a language model processes.
  • Embedding: A vector representation of text that captures semantic meaning.
  • Retrieval Augmented Generation (RAG): A process of combining the output of a large language model with information retrieved from an external knowledge source.

  The reason for choosing this answer is because it accurately reflects the definitions and roles of these terms within the context of generative AI and LLMs.
  
  Reasons for not selecting other terms:

  • Temperature: While temperature is a parameter that controls the randomness of the output in LLMs, it is not a core component described in the question's matching requirements. Higher temperatures lead to more random outputs, while lower temperatures make the output more deterministic. It doesn't fit any of the descriptions provided as accurately as the chosen terms.

  
  Therefore, based on the definitions and application of these concepts in generative AI, the suggested answer is accurate.

  
  

  Selected Answer:
  

  
  

  Citations:

  • What are Tokens?, https://platform.openai.com/tokenizer
  • Understanding embeddings, https://platform.openai.com/docs/guides/embeddings/what-are-embeddings
  • Retrieval-augmented generation, https://www.promptflow.ai/docs/concepts/concept-rag

• Question 9

  HOTSPOT -
  An ML engineer is working on an ML model to predict the prices of similarly sized homes. The model will base predictions on several features The ML engineer will use the following feature engineering techniques to estimate the prices of the homes:
  • Feature splitting
  • Logarithmic transformation
  • One-hot encoding
  • Standardized distribution
  Select the correct feature engineering techniques for the following list of features. Each feature engineering technique should be selected one time or not at all (Select three.)
  

  Correct Answer: See interactive view.

  Explanation:

  The AI agrees with the suggested answer.
  The recommended answer correctly identifies appropriate feature engineering techniques for the given features.
  The suggested answer is the best approach because it correctly applies feature engineering techniques based on the nature of the data provided.
  Here's a detailed breakdown:

  • City: One-hot encoding - This is the correct approach. City is a categorical variable, and one-hot encoding transforms it into a numerical format that machine learning models can understand. It creates a binary column for each city, indicating its presence or absence.
  • Type_year: Feature splitting - This is the correct approach. This technique will allow the model to learn the effect of 'Type' and 'Year' separately.
  • Size of the building: Standardized distribution - This is also the correct approach, as standardizing the distribution of the 'Size of the building' ensures that the model treats the feature appropriately, preventing it from being dominated by features with larger scales and because the question specifically mentions "similarly sized homes" implying no skewed distribution.

  The other options were not selected because they did not align with the characteristics of the features. For example, applying logarithmic transformation to 'City' wouldn't make sense as it's a categorical variable. Similarly, one-hot encoding 'Size of the building' after specifying similarly sized homes is un-necessary.
  

  Citations:

  • One-Hot Encoding, https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.OneHotEncoder.html
  • StandardScaler, https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.StandardScaler.html

• Question 10

  Case study -
  An ML engineer is developing a fraud detection model on AWS. The training dataset includes transaction logs, customer profiles, and tables from an on-premises MySQL database. The transaction logs and customer profiles are stored in Amazon S3.
  The dataset has a class imbalance that affects the learning of the model's algorithm. Additionally, many of the features have interdependencies. The algorithm is not capturing all the desired underlying patterns in the data.
  Which AWS service or feature can aggregate the data from the various data sources?

  • A. Amazon EMR Spark jobs
  • B. Amazon Kinesis Data Streams
  • C. Amazon DynamoDB
  • D. AWS Lake Formation

  Correct Answer: D

  Explanation:

  The suggested answer is D, AWS Lake Formation, and the AI agrees with this answer.
  
  Reasoning:
  AWS Lake Formation is designed to help build, secure, and manage data lakes. It can ingest data from various sources, including Amazon S3 and on-premises databases (like the MySQL database mentioned in the question), which aligns perfectly with the requirement to aggregate data from diverse data sources. Lake Formation simplifies the process of creating a centralized data repository and managing data access policies.
  
  Lake Formation also offers serverless data transformation using Spark, which addresses the data preparation and processing aspects implicitly needed for the ML model. Given the need to aggregate data from various sources first, Lake Formation presents a more direct and comprehensive solution.
  
  Reasons for not choosing the other options:
  

  • A. Amazon EMR Spark jobs: While Amazon EMR with Spark can process data from different sources, it is primarily a data processing and analytics tool. It does not offer the data aggregation, cataloging, and security features of Lake Formation. EMR is more suitable for heavy data transformation and analysis once the data is already aggregated and prepared.
  • B. Amazon Kinesis Data Streams: Amazon Kinesis Data Streams is designed for real-time data streaming. The use case described involves aggregating data from S3 and MySQL, which are not real-time streaming sources, making Kinesis Data Streams less appropriate.
  • C. Amazon DynamoDB: Amazon DynamoDB is a NoSQL database and is not designed for aggregating data from various sources like S3 and on-premises databases. It is typically used as a data store for applications needing fast read/write access.

  
  Citations:

  • AWS Lake Formation, https://aws.amazon.com/lake-formation/