MLA-C01 — questions

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?

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 company is building an Amazon SageMaker AI pipeline for an ML model. The pipeline uses distributed processing and training. An ML engineer needs to encrypt network communication between instances that run distributed jobs. The ML engineer configures the distributed jobs to run in a private VPC. What should the ML engineer do to meet the encryption requirement?

An ML model is deployed in production. The model has performed well and has met its metric thresholds for months. An ML engineer who is monitoring the model observes a sudden degradation. The performance metrics of the model are now below the thresholds. What could be the cause of the performance degradation?

An ML engineer is using AWS Glue to transform proprietary data from a third-party vendor to a format that the ML engineer intends to use with the Amazon SageMaker DeepAR forecasting algorithm. The data includes several similar time series data files that the ML engineer must convert to the appropriate format. The ML engineer must compress the files to optimize storage costs. Which solution will meet these requirements?

A company has significantly increased the amount of data that is stored as .csv files in an Amazon S3 bucket. Data transformation scripts and queries are now taking much longer than they used to take. An ML engineer must implement a solution to optimize the data for query performance. Which solution will meet this requirement with the LEAST operational overhead?

An ML engineer is analyzing a classification dataset before training a model in Amazon SageMarker AI. The ML engineer suspects that the dataset has a significant imbalance between class labels that could lead to biased model predictions. To confirm class imbalance, the ML engineer needs to select an appropriate pre-training bias metric. Which metric will meet this requirement?

An ML engineer uses one ML framework to train multiple ML models. The ML engineer needs to optimize the inference costs and host the models on Amazon SageMaker AI. Which solution will meet these requirements MOST cost-effectively?

A company is using an ML model to classify motion in videos. The data is stored in MP4 format in Amazon S3. When the company created the model, the company needed 4 months to label all the video frames. The company needs to retrain the model with an existing training workflow in Amazon SageMaker AI. An ML engineer must implement a solution that decreases the labeling time. Which solution will meet these requirements?

An ecommerce company trains an ML model to forecast demand for near real-time inventory management based on historical customer activity. The company successfully deploys the trained model to a production Amazon SageMaker AI endpoint. However, the company notices that the model’s forecast performance degrades over time. The company needs a long-term and automated solution to mitigate the performance degradation. Which solution will meet these requirements?

A logistics company has installed in-vehicle cameras for basic monitoring of its drivers. The company wants to improve driver safety by identifying distractions that could lead to accidents. Which solution will meet this requirement with the LEAST operational effort?

A company has trained an ML model that is packaged in a container. The company will integrate the model with an existing Python web application. The company needs to host the model on AWS by using Kubernetes. The company does not want to manage the control plane and must provision the resources in a repeatable manner. The infrastructure must be provisioned by using Python. Which solution will meet these requirements?

An ML engineer is developing a linear regression ML model. The model shows high accuracy on the training dataset but performs poorly on unseen new data. Which action should the ML engineer take to address this issue?

A company is training a new ML model to replace a model that is deployed on an Amazon SageMaker AI real-time endpoint. An ML engineer needs to determine the latency and the accuracy of the new model. The ML engineer must evaluate the new model in a production scenario without affecting the users of the existing model. Which solution will meet these requirements?

An ML engineer wants to use, prepare, and load data from Amazon S3 for analytics. The ML engineer must run an extract, transform, and load (ETL) job to discover the schema of the data and to store the metadata. Which solution will meet these requirements with the LEAST manual effort?

An ML engineer is building an ML pipeline. The pipeline must process a dataset in two ways by using Amazon Athena. The pipeline must use batch processing to perform large-scale data transformations and for model training. The pipeline must also use near real-time processing to perform low-latency queries for inference and analytics. Which file format will provide the LEAST latency for both types of processing?

A company uses an Amazon SageMaker AI ML model to make real-time inferences. The company has configured auto scaling for the Amazon EC2 instances that SageMaker AI uses for the inferences. During times of peak usage, new instances launch before existing instances are fully ready. As a result, the model experiences inefficiencies and delays. Which solution will optimize the scaling process without affecting response times?

A company deployed an Amazon SageMaker AI ML model to an endpoint by calling the CreateModel API operation. The network that was established with the API call includes two private subnets and one security group. The model must download data from an Amazon S3 bucket and must upload data to the S3 bucket. The traffic to the S3 bucket must not travel across the internet. Which solution will meet these requirements?

A company is developing a new ML model to rank customers in order of their potential to pay back loans. The company needs to use an Amazon SageMaker AI built-in algorithm. Which algorithm should the company use to meet these requirements?

An ML engineer is setting up an Amazon SageMaker AI pipeline for an ML model. The pipeline must automatically initiate a re-training job if any data drift is detected. How should the ML engineer set up the pipeline to meet this requirement?