Amazon AWS Certified Machine Learning - Specialty
Prev

There are 204 results

Next
#41 (Accuracy: 100% / 1 votes)
A company’s machine learning (ML) team needs to build a system that can detect whether people in a collection of images are wearing the company’s logo. The company has a set of labeled training data.

Which algorithm should the ML team use to meet this requirement?
  • A. Principal component analysis (PCA)
  • B. Recurrent neural network (RNN)
  • C. К-nearest neighbors (k-NN)
  • D. Convolutional neural network (CNN)
#42 (Accuracy: 100% / 2 votes)
A company wants to use machine learning (ML) to improve its customer churn prediction model. The company stores data in an Amazon Redshift data warehouse.

A data science team wants to use Amazon Redshift machine learning (Amazon Redshift ML) to build a model and run predictions for new data directly within the data warehouse.

Which combination of steps should the company take to use Amazon Redshift ML to meet these requirements? (Choose three.)
  • A. Define the feature variables and target variable for the churn prediction model.
  • B. Use the SOL EXPLAIN_MODEL function to run predictions.
  • C. Write a CREATE MODEL SQL statement to create a model.
  • D. Use Amazon Redshift Spectrum to train the model.
  • E. Manually export the training data to Amazon S3.
  • F. Use the SQL prediction function to run predictions.
#43 (Accuracy: 100% / 1 votes)
A machine learning (ML) specialist collected daily product usage data for a group of customers. The ML specialist appended customer metadata such as age and gender from an external data source.

The ML specialist wants to understand product usage patterns for each day of the week for customers in specific age groups. The ML specialist creates two categorical features named dayofweek and binned_age, respectively.

Which approach should the ML specialist use discover the relationship between the two new categorical features?
  • A. Create a scatterplot for day_of_week and binned_age.
  • B. Create crosstabs for day_of_week and binned_age.
  • C. Create word clouds for day_of_week and binned_age.
  • D. Create a boxplot for day_of_week and binned_age.
#44 (Accuracy: 100% / 2 votes)
A car company has dealership locations in multiple cities. The company uses a machine learning (ML) recommendation system to market cars to its customers.

An ML engineer trained the ML recommendation model on a dataset that includes multiple attributes about each car. The dataset includes attributes such as car brand, car type, fuel efficiency, and price.

The ML engineer uses Amazon SageMaker Data Wrangler to analyze and visualize data. The ML engineer needs to identify the distribution of car prices for a specific type of car.

Which type of visualization should the ML engineer use to meet these requirements?
  • A. Use the SageMaker Data Wrangler scatter plot visualization to inspect the relationship between the car price and type of car.
  • B. Use the SageMaker Data Wrangler quick model visualization to quickly evaluate the data and produce importance scores for the car price and type of car.
  • C. Use the SageMaker Data Wrangler anomaly detection visualization to Identify outliers for the specific features.
  • D. Use the SageMaker Data Wrangler histogram visualization to inspect the range of values for the specific feature.
#45 (Accuracy: 100% / 2 votes)
A global bank requires a solution to predict whether customers will leave the bank and choose another bank. The bank is using a dataset to train a model to predict customer loss. The training dataset has 1,000 rows. The training dataset includes 100 instances of customers who left the bank.

A machine learning (ML) specialist is using Amazon SageMaker Data Wrangler to train a churn prediction model by using a SageMaker training job. After training, the ML specialist notices that the model returns only false results. The ML specialist must correct the model so that it returns more accurate predictions.

Which solution will meet these requirements?
  • A. Apply anomaly detection to remove outliers from the training dataset before training.
  • B. Apply Synthetic Minority Oversampling Technique (SMOTE) to the training dataset before training.
  • C. Apply normalization to the features of the training dataset before training.
  • D. Apply undersampling to the training dataset before training.
#46 (Accuracy: 90% / 8 votes)
A Machine Learning Specialist is training a model to identify the make and model of vehicles in images. The Specialist wants to use transfer learning and an existing model trained on images of general objects. The Specialist collated a large custom dataset of pictures containing different vehicle makes and models.
What should the Specialist do to initialize the model to re-train it with the custom data?
  • A. Initialize the model with random weights in all layers including the last fully connected layer.
  • B. Initialize the model with pre-trained weights in all layers and replace the last fully connected layer.
  • C. Initialize the model with random weights in all layers and replace the last fully connected layer.
  • D. Initialize the model with pre-trained weights in all layers including the last fully connected layer.
#47 (Accuracy: 100% / 2 votes)
A Machine Learning Specialist needs to be able to ingest streaming data and store it in Apache Parquet files for exploration and analysis.
Which of the following services would both ingest and store this data in the correct format?
  • A. AWS DMS
  • B. Amazon Kinesis Data Streams
  • C. Amazon Kinesis Data Firehose
  • D. Amazon Kinesis Data Analytics
#48 (Accuracy: 100% / 4 votes)
An online retailer collects the following data on customer orders: demographics, behaviors, location, shipment progress, and delivery time. A data scientist joins all the collected datasets. The result is a single dataset that includes 980 variables.

The data scientist must develop a machine learning (ML) model to identify groups of customers who are likely to respond to a marketing campaign.

Which combination of algorithms should the data scientist use to meet this requirement? (Choose two.)
  • A. Latent Dirichlet Allocation (LDA)
  • B. K-means
  • C. Semantic segmentation
  • D. Principal component analysis (PCA)
  • E. Factorization machines (FM)
#49 (Accuracy: 100% / 2 votes)
A company builds computer-vision models that use deep learning for the autonomous vehicle industry. A machine learning (ML) specialist uses an Amazon EC2 instance that has a CPU:GPU ratio of 12:1 to train the models.

The ML specialist examines the instance metric logs and notices that the GPU is idle half of the time. The ML specialist must reduce training costs without increasing the duration of the training jobs.

Which solution will meet these requirements?
  • A. Switch to an instance type that has only CPUs.
  • B. Use a heterogeneous cluster that has two different instances groups.
  • C. Use memory-optimized EC2 Spot Instances for the training jobs.
  • D. Switch to an instance type that has a CPU:GPU ratio of 6:1.
#50 (Accuracy: 100% / 6 votes)
A large consumer goods manufacturer has the following products on sale:
* 34 different toothpaste variants
* 48 different toothbrush variants
* 43 different mouthwash variants
The entire sales history of all these products is available in Amazon S3. Currently, the company is using custom-built autoregressive integrated moving average
(ARIMA) models to forecast demand for these products. The company wants to predict the demand for a new product that will soon be launched.
Which solution should a Machine Learning Specialist apply?
  • A. Train a custom ARIMA model to forecast demand for the new product.
  • B. Train an Amazon SageMaker DeepAR algorithm to forecast demand for the new product.
  • C. Train an Amazon SageMaker k-means clustering algorithm to forecast demand for the new product.
  • D. Train a custom XGBoost model to forecast demand for the new product.