MLA-C01 Question Bank · Latest 50 Questions Free Preview (Page 1/5)
Questions 1–10 of the latest 50 MLA-C01 questions (5 pages total), with answers and explanations, no login required. Practice all 233 questions online.
Question 1 · No. 184 · Single choice
A company is developing a new ML model that uses the XGBoost algorithm. The company will train the model on data that is stored in an Amazon S3 bucket. The data is in a nested JSON format. An ML engineer needs to convert the JSON files into a tabular format. Which solution will meet this requirement with the LEAST operational overhead?
- A. Create an AWS Glue PySpark job that uses the Relationalize transform to convert the files.
- B. Write custom Scala code to convert the files. Use Amazon EMR Serverless to run the Scala code.
- C. Create an AWS Lambda function that uses a Python runtime and invokes the reduce() function to convert the files. Invoke the Lambda function.
- D. Create an Amazon Athena database that is based on the JSON files. Use the Athena flatten function to convert the data.
Answer: A
Explanation: AWS Glue provides a built-in transform called 'Relationalize' that is specifically designed to flatten nested JSON structures into a tabular format suitable for machine learning workflows. Using an AWS Glue PySpark job leverages a managed service, minimizing operational overhead compared to writing and managing custom code (as in options B and C) or relying on Athena's limited transformation capabilities (option D). Athena can query nested JSON but does not natively support a 'flatten' function that fully relationalizes deeply nested data like Glue’s Relationalize transform does. Therefore, option A offers the least operational overhead while effectively meeting the requirement.
Question 2 · No. 185 · Single choice
A company is using an Amazon S3 bucket to collect data that will be used for ML workflows. The company needs to use AWS Glue DataBrew to clean and normalize the data. Which solution will meet these requirements?
- A. Create a DataBrew dataset by using the S3 path. Clean and normalize the data by using a DataBrew profile job.
- B. Create a DataBrew dataset by using the S3 path. Clean and normalize the data by using a DataBrew recipe job.
- C. Create a DataBrew dataset by using a Java Database Connectivity (JDBC) driver to connect to the S3 bucket. Clean and normalize the data by using a DataBrew profile job.
- D. Create a DataBrew dataset by using a Java Database Connectivity (JDBC) driver to connect to the S3 bucket. Clean and normalize the data by using a DataBrew recipe job.
Answer: B
Explanation: AWS Glue DataBrew supports creating datasets directly from Amazon S3 using an S3 path. To clean and normalize data, DataBrew uses 'recipe jobs,' which apply a series of data transformation steps defined in a recipe. Profile jobs are used for analyzing and understanding data (e.g., generating statistics), not for performing transformations. Additionally, JDBC drivers are not used to connect to S3 buckets, as S3 is an object storage service, not a relational database. Therefore, option B is the correct solution.
Question 3 · No. 186 · Single choice
A company stores training data as a .csv file in an Amazon S3 bucket. The company must encrypt the data and must control which applications have access to the encryption key. Which solution will meet these requirements?
- A. Create a new SSH access key. Use the AWS Encryption CLI with a reference to the new access key to encrypt the file.
- B. Create a new API key by using the Amazon API Gateway CreateApiKey API operation. Use the AWS CLI with a reference to the new API key to encrypt the file.
- C. Create a new IAM role. Attach a policy that allows the AWS Key Management Service (AWS KMS) GenerateDataKey action. Use the role to encrypt the file.
- D. Create a new AWS Key Management Service (AWS KMS) key. Use the AWS Encryption CLI with a reference to the new KMS key to encrypt the file.
Answer: D
Explanation: The requirement specifies that the data must be encrypted and the company must control which applications have access to the encryption key. AWS KMS (Key Management Service) is designed exactly for this purpose—it allows centralized control over encryption keys and fine-grained access management via IAM policies. Option D correctly proposes creating a new AWS KMS key and using the AWS Encryption CLI with that key to encrypt the file. This ensures both encryption of data and controlled access to the key. Other options are incorrect: SSH keys (A) are not used for data encryption in S3; API keys from API Gateway (B) are for API authentication, not encryption; and while IAM roles (C) can grant permissions to use KMS keys, the option does not mention creating a KMS key itself, which is essential for encryption.
Question 4 · No. 187 · Single choice
A company wants to build a real-time analytics application that uses streaming data from social media. An ML engineer must implement a solution that ingests and transforms 5 GB of data each minute. The solution also must load the data into a data store that supports fast queries for the real-time analytics. Which solution will meet these requirements?
- A. Use Amazon EventBridge to ingest the social media data. Use AWS Glue to transform the data. Store the transformed data in Amazon ElastiCache (Memcached).
- B. Use Amazon Simple Queue Service (Amazon SQS) to ingest the social media data. Use AWS Lambda to transform the data. Store the transformed data in Amazon S3.
- C. Use Amazon Simple Notification Service (Amazon SNS) to ingest the social media data. Use Amazon EMR to transform the data. Store the transformed data in Amazon RDS.
- D. Use Amazon Kinesis Data Streams to ingest the social media data. Use Amazon Managed Service for Apache Flink to transform the data. Store the transformed data in Amazon DynamoDB.
Answer: D
Explanation: Option D is the best choice because it uses Amazon Kinesis Data Streams, which is designed for real-time ingestion of high-volume streaming data (5 GB per minute). Amazon Managed Service for Apache Flink (formerly Kinesis Data Analytics) is purpose-built for real-time stream processing and transformation. Finally, Amazon DynamoDB is a fully managed NoSQL database that supports fast, low-latency queries—ideal for real-time analytics applications. The other options use services not optimized for high-throughput real-time streaming: EventBridge, SQS, and SNS are not designed for continuous high-volume data ingestion; ElastiCache is an in-memory cache, not a persistent queryable data store; S3 is object storage not optimized for fast interactive queries; and RDS is a relational database that may not scale as effectively for high-velocity, schema-flexible streaming workloads.
Question 5 · No. 188 · Single choice
A company has an existing Amazon SageMaker model (v1) on a production endpoint. The company develops a new model version (v2) and needs to test v2 in production before substituting v2 for v1. The company needs to implement a solution to minimize the risk of v2 generating incorrect output in production. The solution must prevent any disruption of production traffic during the change to v2. Which solution will meet these requirements?
- A. Create a second production variant for v2. Assign 1% of the traffic to v2 and 99% of the traffic to v1. Collect all the output of v2 in an Amazon S3 bucket. If v2 performs as expected, switch all the traffic to v2.
- B. Create a second production variant for v2. Assign 10% of the traffic to v2 and 90% of the traffic to v1. Collect all the output of v2 in an Amazon S3 bucket. If v2 performs as expected, switch all the traffic to v2.
- C. Deploy v2 to a new endpoint. Turn on data capturing for the production endpoint. Write a script to pass 100% of input data to v2. If v2 performs as expected, deactivate the v1 endpoint and direct the traffic to v2.
- D. Deploy v2 into a shadow variant that samples 100% of the inference requests. Collect all the output in an Amazon S3 bucket. If v2 performs as expected, promote v2 to production.
Answer: D
Explanation: Option D uses SageMaker's shadow variant feature, which allows v2 to receive 100% of the production traffic copies without affecting the actual responses sent to clients (which still come from v1). This enables thorough testing of v2 under real production conditions while ensuring zero disruption to users. All outputs from v2 can be captured and analyzed for correctness before promoting it to production. Options A and B use traffic splitting, which risks sending potentially incorrect predictions from v2 to real users (even if only 1% or 10%). Option C deploys v2 to a separate endpoint and manually reroutes traffic, which doesn't guarantee identical input conditions and introduces operational complexity and potential disruption during the switch. Therefore, D is the safest and most effective approach.
Question 6 · No. 189 · Single choice
A company is building an ML model by using Amazon SageMaker, AWS owned libraries, and open source libraries. The company must ensure that SageMaker does not collect metadata about usage and errors during training. Which solution will meet these requirements?
- A. Associate the SageMaker domain with a custom IAM role. Attach the role to a policy that denies Amazon CloudWatch service usage logs.
- B. Add an IAM role to the SageMaker domain to deny Amazon CloudWatch the permission to report metadata.
- C. Turn off the setting in the SageMaker domain to share metadata for console jobs. Opt out of metadata collection for each training job that is submitted through the AWS CLI or AWS SDKs.
- D. Set a parameter to opt out of metadata collection for each training job that is submitted through the AWS CLI, Boto3, or the SageMaker Python SDK.
Answer: D
Question 7 · No. 190 · Single choice
An ML engineer is training an ML model to identify people’s health risk based on 20 features and 1 target. The target class has two values: • Likely to have health risk (positive class)
• Unlikely to have health risk (negative class) The age range of people in the dataset is 30 years old to 60 years old. Age is one of the features. The ML engineer analyzes the features. For the positive class, the difference in proportions of labels (DPL) value is (+0.9) for the age range of 40 to 45 compared with all other age ranges. What should the ML engineer do to correct this data imbalance?
- A. Oversample the positive class for the age range of 40 to 45.
- B. Undersample the positive class for the age range of 40 to 45.
- C. Undersample the positive class for all age ranges except 40 to 45.
- D. Oversample the negative class for all age ranges except 40 to 45.
Answer: B
Explanation: The DPL (Difference in Proportions of Labels) value of +0.9 for the age range 40–45 in the positive class indicates a significant overrepresentation of positive cases in that subgroup compared to others. This suggests bias or imbalance where the model may become overly confident or skewed toward predicting positive outcomes for people aged 40–45. To correct this imbalance, the engineer should reduce the dominance of this subgroup by undersampling the positive class specifically for the 40–45 age range, which corresponds to option B.
Question 8 · No. 191 · Multiple choice
A company wants to launch a new internal generative AI interface to answer user questions. The interface will be based on a popular open source large language model (LLM). Which combination of steps will deploy the interface with the LEAST operational overhead? (Choose two.)
- A. Use Amazon SageMaker JumpStart to deploy the LLM.
- B. Download the LLM as a .zip file. Deploy the LLM on a GPU-based Amazon EC2 instance.
- C. Create a frontend HTML interface that uses an Amazon API Gateway WebSocket API with AWS Lambda functions to handle the user interaction.
- D. Use Amazon QuickSight to create a UI to handle the user interaction.
- E. Use Amazon Lex to create a UI to handle the user interaction.
Answer: A, E
Question 9 · No. 192 · Single choice
A company runs an ML model on Amazon SageMaker. The company uses an automatic process that makes API calls to create training jobs for the model. The company has new compliance rules that prohibit the collection of aggregated metadata from training jobs. Which solution will prevent SageMaker from collecting metadata from the training jobs?
- A. Opt out of metadata tracking for any training job that is submitted.
- B. Ensure that training jobs are running in a private subnet in a custom VPC.
- C. Encrypt the training data with an AWS Key Management Service (AWS KMS) customer managed key.
- D. Reconfigure the training jobs to use only AWS Nitro instances.
Answer: A
Explanation: The question asks for a solution to prevent Amazon SageMaker from collecting aggregated metadata from training jobs. Option A directly addresses this by opting out of metadata tracking, which is a feature SageMaker provides to disable the collection of usage metrics and metadata for compliance or privacy reasons. The other options—using a private subnet (B), encrypting data with KMS (C), or using Nitro instances (D)—are related to network isolation, data security, and hardware features respectively, but none of them stop SageMaker from collecting metadata about the training job itself.
Question 10 · No. 193 · Single choice
A company needs to use Amazon SageMaker to train a model on more than 300 GB of data. The training data is composed of files that are 200 MB in size. The data is stored in Amazon S3 Standard storage and feeds a dashboard tool. Which SageMaker training ingestion mechanism is the MOST cost-effective solution for this scenario?
- A. Amazon Elastic File System (Amazon EFS) file system
- B. Amazon FSx for Lustre file system
- C. Amazon S3 in fast file mode while using S3 Express One Zone
- D. Amazon S3 in fast file mode without using S3 Express One Zone
Answer: D