Question 1

A Data Science team is designing a dataset repository where it will store a large amount of training data commonly used in its machine learning models. As Data Scientists may create an arbitrary number of new datasets every day, the solution has to scale automatically and be cost-effective. Also, it must be possible to explore the data using SQL. Which storage scheme is MOST adapted to this scenario?

Accepted Answer

The answer of A Data Science team is designing a...

Question 2

A Data Scientist is working on an application that performs sentiment analysis. The validation accuracy is poor, and the Data Scientist thinks that the cause may be a rich vocabulary and a low average frequency of words in the dataset. Which tool should be used to improve the validation accuracy?

Accepted Answer

TF-IDF vectorizer can help by giving more weight to less frequent words, which might be more informative for sentiment analysis, thus potentially improving validation accuracy.

Question 3

A Machine Learning Specialist must build out a process to query a dataset on Amazon S3 using Amazon Athena. The dataset contains more than 800,000 records stored as plaintext CSV files. Each record contains 200 columns and is approximately 1.5 MB in size. Most queries will span 5 to 10 columns only. How should the Machine Learning Specialist transform the dataset to minimize query runtime?

Accepted Answer

Converting the records to Apache Parquet format is optimal because Parquet is a columnar storage file format. It allows for efficient querying of specific columns, which minimizes the amount of data scanned and reduces query runtime.

Question 4

A Machine Learning Specialist wants to determine the appropriate SageMakerVariantInvocationsPerInstance setting for an endpoint automatic scaling configuration. The Specialist has performed a load test on a single instance and determined that peak requests per second (RPS) without service degradation is about 20 RPS. As this is the first deployment, the Specialist intends to set the invocation safety factor to 0.5. Based on the stated parameters and given that the invocations per instance setting is measured on a per-minute basis, what should the Specialist set as the setting?

Accepted Answer

The peak RPS is 20, so per minute it is 20 * 60 = 1,200. With a safety factor of 0.5, the setting should be 1,200 * 0.5 = 600.

Question 5

Given the following confusion matrix for a movie classification model, what is the true class frequency for Romance and the predicted class frequency for Adventure?

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Question 6

A Machine Learning Specialist wants to bring a custom algorithm to Amazon SageMaker. The Specialist implements the algorithm in a Docker container supported by Amazon SageMaker. How should the Specialist package the Docker container so that Amazon SageMaker can launch the training correctly?

Accepted Answer

CMD config in the Dockerfile is used to specify the default command to run when the container starts. In this case, the Specialist should use CMD config to add the training program as a CMD of the image so that Amazon SageMaker can launch the training correctly.

Question 7

A real estate company wants to create a machine learning model for predicting housing prices based on a historical dataset. The dataset contains 32 features. Which model will meet the business requirement?

Accepted Answer

Linear regression is a supervised machine learning algorithm that can be used to predict continuous values, such as housing prices. It is a simple and effective algorithm that is easy to implement and interpret.

Question 8

An agency collects census information within a country to determine healthcare and social program needs by province and city. The census form collects responses for approximately 500 questions from each citizen. Which combination of algorithms would provide the appropriate insights? (Choose two.)

Accepted Answer

PCA is used for dimensionality reduction, which helps in analyzing large datasets like census data by reducing the number of variables. K-means is useful for clustering data, which can help identify patterns and group similar responses to inform healthcare and social program needs.

Question 9

A web-based company wants to improve its conversion rate on its landing page. Using a large historical dataset of customer visits, the company has repeatedly trained a multi-class deep learning network algorithm on Amazon SageMaker. However, there is an overfitting problem: training data shows 90% accuracy in predictions, while test data shows 70% accuracy only. The company needs to boost the generalization of its model before deploying it into production to maximize conversions of visits to purchases. Which action is recommended to provide the HIGHEST accuracy model for the company's test and validation data?

Accepted Answer

The answer of A web-based company wants to improve its...

Question 10

A Machine Learning Specialist works for a credit card processing company and needs to predict which transactions may be fraudulent in near-real time. Specifically, the Specialist must train a model that returns the probability that a given transaction may fraudulent. How should the Specialist frame this business problem?

Accepted Answer

The answer of A Machine Learning Specialist works for a...

Question 11

A Machine Learning team runs its own training algorithm on Amazon SageMaker. The training algorithm requires external assets. The team needs to submit both its own algorithm code and algorithm-specific parameters to Amazon SageMaker. What combination of services should the team use to build a custom algorithm in Amazon SageMaker? (Choose two.)

Accepted Answer

Amazon ECR is used to store Docker images for the custom algorithm, and Amazon S3 is used to store external assets and data needed for training.

Question 12

A Machine Learning Specialist is given a structured dataset on the shopping habits of a company's customer base. The dataset contains thousands of columns of data and hundreds of numerical columns for each customer. The Specialist wants to identify whether there are natural groupings for these columns across all customers and visualize the results as quickly as possible. What approach should the Specialist take to accomplish these tasks?

Accepted Answer

The answer of A Machine Learning Specialist is given a...

Question 13

A credit card company wants to build a credit scoring model to help predict whether a new credit card applicant will default on a credit card payment. The company has collected data from a large number of sources with thousands of raw attributes. Early experiments to train a classification model revealed that many attributes are highly correlated, the large number of features slows down the training speed significantly, and that there are some overfitting issues. The Data Scientist on this project would like to speed up the model training time without losing a lot of information from the original dataset. Which feature engineering technique should the Data Scientist use to meet the objectives?

Accepted Answer

The answer of A credit card company wants to build...

Question 14

A Machine Learning Specialist previously trained a logistic regression model using scikit-learn on a local machine, and the Specialist now wants to deploy it to production for inference only. What steps should be taken to ensure Amazon SageMaker can host a model that was trained locally?

Accepted Answer

The answer of A Machine Learning Specialist previously trained a...

Question 15

A trucking company is collecting live image data from its fleet of trucks across the globe. The data is growing rapidly and approximately 100 GB of new data is generated every day. The company wants to explore machine learning uses cases while ensuring the data is only accessible to specific IAM users. Which storage option provides the most processing flexibility and will allow access control with IAM?

Accepted Answer

The answer of A trucking company is collecting live image...

Question 16

A Data Scientist needs to analyze employment data. The dataset contains approximately 10 million observations on people across 10 different features. During the preliminary analysis, the Data Scientist notices that income and age distributions are not normal. While income levels shows a right skew as expected, with fewer individuals having a higher income, the age distribution also show a right skew, with fewer older individuals participating in the workforce. Which feature transformations can the Data Scientist apply to fix the incorrectly skewed data? (Choose two.)

Accepted Answer

The answer of A Data Scientist needs to analyze employment...

Question 17

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?

Accepted Answer

The Specialist should initialize the model with pre-trained weights in all layers and replace the last fully connected layer to adapt the model to the specific task of identifying vehicle makes and models. This approach leverages the learned features from the pre-trained model while allowing customization for the new task.

Question 18

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?

Accepted Answer

The answer of A company uses a long short-term memory...

Question 19

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 MLP's 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?

Accepted Answer

The answer of A Data Scientist is training a multilayer...

Question 20

A Machine Learning Specialist is preparing data for training on Amazon SageMaker. The Specialist is using one of the SageMaker built-in algorithms for the training. The dataset is stored in .CSV format and is transformed into a numpy.array , which appears to be negatively affecting the speed of the training. What should the Specialist do to optimize the data for training on SageMaker?

Accepted Answer

Transforming the dataset into the RecordIO protobuf format can optimize the data for training on SageMaker, as it is a more efficient format for reading and processing large datasets compared to CSV or numpy.array.

Exam 7: AWS Certified Machine Learning - Specialty (MLS-C01)