MLA-C01 Dumps.pdf
Q: 1
A company uses a hybrid cloud environment. A model that is deployed on premises uses data in
Amazon 53 to provide customers with a live conversational engine.
The model is using sensitive dat
a. An ML engineer needs to implement a solution to identify and remove the sensitive data.
Which solution will meet these requirements with the LEAST operational overhead?
Options
Q: 2
A company has trained and deployed an ML model by using Amazon SageMaker. The company needs
to implement a solution to record and monitor all the API call events for the SageMaker endpoint.
The solution also must provide a notification when the number of API call events breaches a
threshold.
Use SageMaker Debugger to track the inferences and to report metrics. Create a custom rule to
provide a notification when the threshold is breached.
Which solution will meet these requirements?
Options
Q: 3
An ML engineer has trained a neural network by using stochastic gradient descent (SGD). The neural
network performs poorly on the test set. The values for training loss and validation loss remain high
and show an oscillating pattern. The values decrease for a few epochs and then increase for a few
epochs before repeating the same cycle.
What should the ML engineer do to improve the training process?
Options
Q: 4
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?
Options
Q: 5
A financial company receives a high volume of real-time market data streams from an external
provider. The streams consist of thousands of JSON records every second.
The company needs to implement a scalable solution on AWS to identify anomalous data points.
Which solution will meet these requirements with the LEAST operational overhead?
Options
Q: 6
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.
Before the ML engineer trains the model, the ML engineer must resolve the issue of the imbalanced
data.
Which solution will meet this requirement with the LEAST operational effort?
Options
Q: 7
A company that has hundreds of data scientists is using Amazon SageMaker to create ML models.
The models are in model groups in the SageMaker Model Registry.
The data scientists are grouped into three categories: computer vision, natural language processing
(NLP), and speech recognition. An ML engineer needs to implement a solution to organize the
existing models into these groups to improve model discoverability at scale. The solution must not
affect the integrity of the model artifacts and their existing groupings.
Which solution will meet these requirements?
Options
Q: 8
A company has implemented a data ingestion pipeline for sales transactions from its ecommerce
website. The company uses Amazon Data Firehose to ingest data into Amazon OpenSearch Service.
The buffer interval of the Firehose stream is set for 60 seconds. An OpenSearch linear model
generates real-time sales forecasts based on the data and presents the data in an OpenSearch
dashboard.
The company needs to optimize the data ingestion pipeline to support sub-second latency for the
real-time dashboard.
Which change to the architecture will meet these requirements?
Options
Q: 9
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
?
?Your Answer
Q: 10
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. 
Your Answer
Question 1 of 10
