A Data Scientist is training a multilayer perception (MLP) on a dataset with multiple classes. The target class of interest is
unique compared to the other classes within the dataset, but it does not achieve and acceptable recall metric. The Data
Scientist has already tried varying the number and size of the MLPs hidden layers, which has not significantly improved the
results. A solution to improve recall must be implemented as quickly as possible.
Which techniques should be used to meet these requirements?
A Machine Learning Specialist is working with a large company to leverage machine learning within its products. The
company wants to group its customers into categories based on which customers will and will not churn within the next 6
months. The company has labeled the data available to the Specialist.
Which machine learning model type should the Specialist use to accomplish this task?
The goal of classification is to determine to which class or category a data point (customer in our case) belongs to. For
classification problems, data scientists would use historical data with predefined target variables AKA labels (churner/non-
churner) answers that need to be predicted to train an algorithm. With classification, businesses can answer the following
Will this customer churn or not?
Will a customer renew their subscription?
Will a user downgrade a pricing plan?
Are there any signs of unusual customer behavior?
A company that manufactures mobile devices wants to determine and calibrate the appropriate sales price for its devices.
The company is collecting the relevant data and is determining data features that it can use to train machine learning (ML)
models. There are more than 1,000 features, and the company wants to determine the primary features that contribute to the
Which techniques should the company use for feature selection? (Choose three.)
C D F
A telecommunications company is developing a mobile app for its customers. The company is using an Amazon SageMaker
hosted endpoint for machine learning model inferences.
Developers want to introduce a new version of the model for a limited number of users who subscribed to a preview feature
of the app. After the new version of the model is tested as a preview, developers will evaluate its accuracy. If a new version
of the model has better accuracy, developers need to be able to gradually release the new version for all users over a fixed
period of time.
How can the company implement the testing model with the LEAST amount of operational overhead?
A Machine Learning Specialist is assigned to a Fraud Detection team and must tune an XGBoost model, which is working
appropriately for test data. However, with unknown data, it is not working as expected. The existing parameters are provided
Which parameter tuning guidelines should the Specialist follow to avoid overfitting?
A company uses a long short-term memory (LSTM) model to evaluate the risk factors of a particular energy sector. The
model reviews multi-page text documents to analyze each sentence of the text and categorize it as either a potential risk or
no risk. The model is not performing well, even though the Data Scientist has experimented with many different network
structures and tuned the corresponding hyperparameters.
Which approach will provide the MAXIMUM performance boost?
A Machine Learning Specialist has completed a proof of concept for a company using a small data sample, and now the
Specialist is ready to implement an end-to-end solution in AWS using Amazon SageMaker. The historical training data is
stored in Amazon RDS.
Which approach should the Specialist use for training a model using that data?
A Machine Learning Specialist is building a logistic regression model that will predict whether or not a person will order a
pizza. The Specialist is trying to build the optimal model with an ideal classification threshold.
What model evaluation technique should the Specialist use to understand how different classification thresholds will impact
the model's performance?
The chief editor for a product catalog wants the research and development team to build a machine learning system that can
be used to detect whether or not individuals in a collection of images are wearing the company's retail brand. The team has
a set of training data.
Which machine learning algorithm should the researchers use that BEST meets their requirements?
A data scientist uses an Amazon SageMaker notebook instance to conduct data exploration and analysis. This requires
certain Python packages that are not natively available on Amazon SageMaker to be installed on the notebook instance.
How can a machine learning specialist ensure that required packages are automatically available on the notebook instance
for the data scientist to use?
A manufacturing company uses machine learning (ML) models to detect quality issues. The models use images that are
taken of the company's product at the end of each production step. The company has thousands of machines at the
production site that generate one image per second on average.
The company ran a successful pilot with a single manufacturing machine. For the pilot, ML specialists used an industrial PC
that ran AWS IoT Greengrass with a long-running AWS Lambda function that uploaded the images to Amazon S3. The
uploaded images invoked a Lambda function that was written in Python to perform inference by using an Amazon
SageMaker endpoint that ran a custom model. The inference results were forwarded back to a web service that was hosted
at the production site to prevent faulty products from being shipped.
The company scaled the solution out to all manufacturing machines by installing similarly configured industrial PCs on each
production machine. However, latency for predictions increased beyond acceptable limits. Analysis shows that the internet
connection is at its capacity limit.
How can the company resolve this issue MOST cost-effectively?
A retail company is selling products through a global online marketplace. The company wants to use machine learning (ML)
to analyze customer feedback and identify specific areas for improvement. A developer has built a tool that collects customer
reviews from the online marketplace and stores them in an Amazon S3 bucket. This process yields a dataset of 40 reviews.
A data scientist building the ML models must identify additional sources of data to increase the size of the dataset.
Which data sources should the data scientist use to augment the dataset of reviews? (Choose three.)
B D F
A company wants to classify user behavior as either fraudulent or normal. Based on internal research, a machine learning
specialist will build a binary classifier based on two features: age of account, denoted by x, and transaction month, denoted
by y. The class distributions are illustrated in the provided figure. The positive class is portrayed in red, while the negative
class is portrayed in black.
Which model would have the HIGHEST accuracy?
A Data Scientist needs to create a serverless ingestion and analytics solution for high-velocity, real-time streaming data.
The ingestion process must buffer and convert incoming records from JSON to a query-optimized, columnar format without
data loss. The output datastore must be highly available, and Analysts must be able to run SQL queries against the data and
connect to existing business intelligence dashboards.
Which solution should the Data Scientist build to satisfy the requirements?
A company wants to predict the sale prices of houses based on available historical sales data. The target variable in the
companys dataset is the sale price. The features include parameters such as the lot size, living area measurements, non-
living area measurements, number of bedrooms, number of bathrooms, year built, and postal code. The company wants to
use multi-variable linear regression to predict house sale prices.
Which step should a machine learning specialist take to remove features that are irrelevant for the analysis and reduce the