0%
0 votes, 0 avg
17

This quiz randomly generates 30 questions as asked in AWS Certified Machine Learning - Specialty (MLS-C01)

Congratulations!
AWS Certified Machine Learning

AWS Certified Machine Learning - Specialty (MLS-C01)

This quiz randomly generates 30 questions (in 60 mins) as asked in AWS Certified Machine Learning - Specialty (MLS-C01). The real MLS-C01 test has 65 questions and a total time of 180 minutes. Of these, 15 questions are underlined, and only 50 questions are scored. This test randomly generates 30 questions from our question bank. For best results, practice multiple times until you achieve 100% accuracy.

1 / 30

A financial services company is building a robust serverless data lake on Amazon S3. The data lake
should be flexible and meet the following requirements:
Support querying old and new data on Amazon S3 through Amazon Athena and Amazon Redshift
Spectrum.
Support event-driven ETL pipelines
Provide a quick and easy way to understand metadata
Which approach meets these requirements?

2 / 30

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?

3 / 30

A Machine Learning Specialist at a company sensitive to security is preparing a dataset for model training.
The dataset is stored in Amazon S3 and contains Personally Identifiable Information (PII).
The dataset:
Must be accessible from a VPC only.
Must not traverse the public internet.
How can these requirements be satisfied?

4 / 30

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? (Select TWO.)

5 / 30

Which of the following metrics should a Machine Learning Specialist generally use to compare/evaluate machine learning classification models against each other?

6 / 30

A Machine Learning Specialist kicks off a hyperparameter tuning job for a tree-based ensemble model
using Amazon SageMaker with Area Under the ROC Curve (AUC) as the objective metric. This workflow
will eventually be deployed in a pipeline that retrains and tunes hyperparameters each night to model click-
through on data that goes stale every 24 hours.
With the goal of decreasing the amount of time it takes to train these models, and ultimately to decrease
costs, the Specialist wants to reconfigure the input hyperparameter range(s).
Which visualization will accomplish this?

7 / 30

A Machine Learning Specialist has created a deep learning neural network model that performs well on the
training data but performs poorly on the test data.
Which of the following methods should the Specialist consider using to correct this? (Choose three.)

8 / 30

A large mobile network operating company is building a machine learning model to predict customers who
are likely to unsubscribe from the service. The company plans to offer an incentive for these customers as
the cost of churn is far greater than the cost of the incentive.
The model produces the following confusion matrix after evaluating on a test dataset of 100 customers:

Based on the model evaluation results, why is this a viable model for production?

9 / 30

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?

10 / 30

A gaming company has launched an online game where people can start playing for free, but they need to
pay if they choose to use certain features. The company needs to build an automated system to predict
whether or not a new user will become a paid user within 1 year. The company has gathered a labeled
dataset from 1 million users.
The training dataset consists of 1,000 positive samples (from users who ended up paying within 1 year)
and 999,000 negative samples (from users who did not use any paid features). Each data sample consists
of 200 features including user age, device, location, and play patterns.
Using this dataset for training, the Data Science team trained a random forest model that converged with
over 99% accuracy on the training set. However, the prediction results on a test dataset were not
satisfactory
Which of the following approaches should the Data Science team take to mitigate this issue? (Choose
two.)

11 / 30

An online reseller has a large, multi-column dataset with one column missing 30% of its data. A Machine
Learning Specialist believes that certain columns in the dataset could be used to reconstruct the missing
data.
Which reconstruction approach should the Specialist use to preserve the integrity of the dataset?

12 / 30

A company wants to classify user behavior as either fraudulent or normal. Based on internal research, a
Machine Learning Specialist would like to build a binary classifier based on two features: age of account
and transaction month. The class distribution for these features is illustrated in the figure provided.

Based on this information, which model would have the HIGHEST recall with respect to the fraudulent class?

13 / 30

A Data Scientist is developing a machine learning model to classify whether a financial transaction is
fraudulent. The labeled data available for training consists of 100,000 non-fraudulent observations and
1,000 fraudulent observations.
The Data Scientist applies the XGBoost algorithm to the data, resulting in the following confusion matrix
when the trained model is applied to a previously unseen validation dataset. The accuracy of the model is
99.1%, but the Data Scientist has been asked to reduce the number of false negatives.
Which combination of steps should the Data Scientist take to reduce the number of false positive
predictions by the model? (Choose two.)

14 / 30

A Data Scientist is developing a machine learning model to predict future patient outcomes based on
information collected about each patient and their treatment plans. The model should output a continuous
value as its prediction. The data available includes labeled outcomes for a set of 4,000 patients. The study
was conducted on a group of individuals over the age of 65 who have a particular disease that is known to
worsen with age.
Initial models have performed poorly. While reviewing the underlying data, the Data Scientist notices that,
out of 4,000 patient observations, there are 450 where the patient age has been input as 0. The other
features for these observations appear normal compared to the rest of the sample population.
How should the Data Scientist correct this issue?

15 / 30

A Machine Learning Specialist receives customer data for an online shopping website. The data includes
demographics, past visits, and locality information. The Specialist must develop a machine learning
approach to identify the customer shopping patterns, preferences, and trends to enhance the website-for
better service and smart recommendations.
Which solution should the Specialist recommend?

16 / 30

A Machine Learning Specialist is working with a large cybersecurity company that manages security
events in real time for companies around the world. The cybersecurity company wants to design a solution
that will allow it to use machine learning to score malicious events as anomalies on the data as it is being
ingested. The company also wants be able to save the results in its data lake for later processing and
analysis.
What is the MOST efficient way to accomplish these tasks?

17 / 30

A Machine Learning Specialist is using an Amazon SageMaker notebook instance in a private subnet of a
corporate VPC. The ML Specialist has important data stored on the Amazon SageMaker notebook
instance's Amazon EBS volume, and needs to take a snapshot of that EBS volume. However, the ML
Specialist cannot find the Amazon SageMaker notebook instance's EBS volume or Amazon EC2 instance
within the VPC.
Why is the ML Specialist not seeing the instance visible in the VPC?

18 / 30

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?

19 / 30

A Mobile Network Operator is building an analytics platform to analyze and optimize a company's
operations using Amazon Athena and Amazon S3. The source systems send data in .CSV format in real time. The Data Engineering team wants to transform the data to the Apache Parquet format before storing it on Amazon S3. Which solution takes the LEAST effort to implement?

20 / 30

When submitting Amazon SageMaker training jobs using one of the built-in algorithms, which common parameters MUST be specified? (Choose three.)

21 / 30

A Machine Learning Specialist is required to build a supervised image-recognition model to identify a cat.
The ML Specialist performs some tests and records the following results for a neural network-based image
classifier:
Total number of images available = 1,000
Test set images = 100 (constant test set)
The ML Specialist notices that, in over 75% of the misclassified images, the cats were held upside down by
their owners.
Which techniques can be used by the ML Specialist to improve this specific test error?

22 / 30

A Machine Learning Specialist is building a model that will perform time series forecasting using Amazon
SageMaker. The Specialist has finished training the model and is now planning to perform load testing on
the endpoint so they can configure Auto Scaling for the model variant.
Which approach will allow the Specialist to review the latency, memory utilization, and CPU utilization
during the load test?

23 / 30

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?

24 / 30

A company is setting up an Amazon SageMaker environment. The corporate data security policy does not
allow communication over the internet.
How can the company enable the Amazon SageMaker service without enabling direct internet access to
Amazon SageMaker notebook instances?

25 / 30

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?

26 / 30

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?

27 / 30

A company is observing low accuracy while training on the default built-in image classification algorithm in
Amazon SageMaker. The Data Science team wants to use an Inception neural network architecture
instead of a ResNet architecture.
Which of the following will accomplish this? (Choose two.)

28 / 30

A Machine Learning team uses Amazon SageMaker to train an Apache MXNet handwritten digit classifier
model using a research dataset. The team wants to receive a notification when the model is overfitting.
Auditors want to view the Amazon SageMaker log activity report to ensure there are no unauthorized API
calls.
What should the Machine Learning team do to address the requirements with the least amount of code and
fewest steps?

29 / 30

A retail company intends to use machine learning to categorize new products. A labeled dataset of current
products was provided to the Data Science team. The dataset includes 1,200 products. The labeled
dataset has 15 features for each product such as title dimensions, weight, and price. Each product is
labeled as belonging to one of six categories such as books, games, electronics, and movies.
Which model should be used for categorizing new products using the provided dataset for training?

30 / 30

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?

Your score is

0%

My Account
My Account
Scroll to Top