AWS Certified Machine Learning - Specialty (MLS-C01)

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

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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 Machine Learning Specialist is assigned a TensorFlow project using Amazon SageMaker for training,

and needs to continue working for an extended period with no Wi-Fi access.

Which approach should the Specialist use to continue working?

Install Python 3 and boto3 on their laptop and continue the code development using that environment.

Download the TensorFlow Docker container used in Amazon SageMaker from GitHub to their local environment, and use the Amazon SageMaker Python SDK to test the code.

Download TensorFlow from tensorflow.org to emulate the TensorFlow kernel in the SageMaker environment.

Download the SageMaker notebook to their local environment, then install Jupyter Notebooks on their laptop and continue the development in a local notebook.

2 / 30

A Machine Learning Specialist is building a prediction model for a large number of features using linear

models, such as linear regression and logistic regression. During exploratory data analysis, the Specialist

observes that many features are highly correlated with each other. This may make the model unstable.

What should be done to reduce the impact of having such a large number of features?

Perform one-hot encoding on highly correlated features.

Use matrix multiplication on highly correlated features.

Create a new feature space using principal component analysis (PCA)

Apply the Pearson correlation coefficient.

3 / 30

The displayed graph is from a forecasting model for testing a time series.

Considering the graph only, which conclusion should a Machine Learning Specialist make about the

behavior of the model?

The model predicts both the trend and the seasonality well

The model predicts the trend well, but not the seasonality.

The model predicts the seasonality well, but not the trend.

The model does not predict the trend or the seasonality well.

4 / 30

A company's Machine Learning Specialist needs to improve the training speed of a time-series forecasting

model using TensorFlow. The training is currently implemented on a single-GPU machine and takes

approximately 23 hours to complete. The training needs to be run daily.

The model accuracy is acceptable, but the company anticipates a continuous increase in the size of the

training data and a need to update the model on an hourly, rather than a daily, basis. The company also

wants to minimize coding effort and infrastructure changes.

What should the Machine Learning Specialist do to the training solution to allow it to scale for future

demand?

Do not change the TensorFlow code. Change the machine to one with a more powerful GPU to speed up the training.

Change the TensorFlow code to implement a Horovod distributed framework supported by Amazon SageMaker. Parallelize the training to as many machines as needed to achieve the business goals.

Switch to using a built-in AWS SageMaker DeepAR model. Parallelize the training to as many machines as needed to achieve the business goals.

Move the training to Amazon EMR and distribute the workload to as many machines as needed to achieve the business goals.

5 / 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?

Amazon Comprehend syntax analysis and entity detection

Amazon SageMaker BlazingText cbow mode

Natural Language Toolkit (NLTK) stemming and stop word removal

Scikit-leam term frequency-inverse document frequency (TF-IDF) vectorizer

6 / 30

A Data Engineer needs to build a model using a dataset containing customer credit card information

How can the Data Engineer ensure the data remains encrypted and the credit card information is secure?

Use a custom encryption algorithm to encrypt the data and store the data on an Amazon SageMaker instance in a VPC. Use the SageMaker DeepAR algorithm to randomize the credit card numbers.

Use an IAM policy to encrypt the data on the Amazon S3 bucket and Amazon Kinesis to automatically discard credit card numbers and insert fake credit card numbers.

Use an Amazon SageMaker launch configuration to encrypt the data once it is copied to the SageMaker instance in a VPC. Use the SageMaker principal component analysis (PCA) algorithm to reduce the length of the credit card numbers.

Use AWS KMS to encrypt the data on Amazon S3 and Amazon SageMaker, and redact the credit card numbers from the customer data with AWS Glue.

7 / 30

A Machine Learning Specialist deployed a model that provides product recommendations on a company's

website. Initially, the model was performing very well and resulted in customers buying more products on

average. However, within the past few months, the Specialist has noticed that the effect of product

recommendations has diminished and customers are starting to return to their original habits of spending

less. The Specialist is unsure of what happened, as the model has not changed from its initial deployment

over a year ago.

Which method should the Specialist try to improve model performance?

The model needs to be completely re-engineered because it is unable to handle product inventory changes.

The model's hyperparameters should be periodically updated to prevent drift.

The model should be periodically retrained from scratch using the original data while adding a regularization term to handle product inventory changes

The model should be periodically retrained using the original training data plus new data as product inventory changes.

8 / 30

A monitoring service generates 1 TB of scale metrics record data every minute. A Research team performs

queries on this data using Amazon Athena. The queries run slowly due to the large volume of data, and the

team requires better performance. How should the records be stored in Amazon S3 to improve query performance?

CSV files

Parquet files

Compressed JSON

RecordlO

9 / 30

A Machine Learning Specialist working for an online fashion company wants to build a data ingestion

solution for the company's Amazon S3-based data lake.

The Specialist wants to create a set of ingestion mechanisms that will enable future capabilities comprised

of:

Real-time analytics

Interactive analytics of historical data

Clickstream analytics

Product recommendations

Which services should the Specialist use?

AWS Glue as the data catalog; Amazon Kinesis Data Streams and Amazon Kinesis Data Analytics for real-time data insights; Amazon Kinesis Data Firehose for delivery to Amazon ES for clickstream analytics; Amazon EMR to generate personalized product recommendations

Amazon Athena as the data catalog: Amazon Kinesis Data Streams and Amazon Kinesis Data Analytics for near-real-time data insights; Amazon Kinesis Data Firehose for clickstream analytics; AWS Glue to generate personalized product recommendations

AWS Glue as the data catalog; Amazon Kinesis Data Streams and Amazon Kinesis Data Analytics for historical data insights; Amazon Kinesis Data Firehose for delivery to Amazon ES for clickstream analytics; Amazon EMR to generate personalized product recommendations

Amazon Athena as the data catalog; Amazon Kinesis Data Streams and Amazon Kinesis Data Analytics for historical data insights; Amazon DynamoDB streams for clickstream analytics; AWS Glue to generate personalized product recommendations

10 / 30

A Machine Learning Specialist is building a convolutional neural network (CNN) that will classify 10 types

of animals. The Specialist has built a series of layers in a neural network that will take an input image of an

animal, pass it through a series of convolutional and pooling layers, and then finally pass it through a

dense and fully connected layer with 10 nodes. The Specialist would like to get an output from the neural

network that is a probability distribution of how likely it is that the input image belongs to each of the 10

classes.

Which function will produce the desired output?

Dropout

Smooth L1 loss

Softmax

Rectified linear units (ReLU)

11 / 30

An interactive online dictionary wants to add a widget that displays words used in similar contexts. A

Machine Learning Specialist is asked to provide word features for the downstream nearest neighbor model

powering the widget.

What should the Specialist do to meet these requirements?

Create one-hot word encoding vectors.

Produce a set of synonyms for every word using Amazon Mechanical Turk.

Create word embedding vectors that store edit distance with every other word.

Download word embeddings pre-trained on a large corpus.

12 / 30

During mini-batch training of a neural network for a classification problem, a Data Scientist notices that

training accuracy oscillates.

What is the MOST likely cause of this issue?

The class distribution in the dataset is imbalanced.

Dataset shuffling is disabled.

The batch size is too big.

The learning rate is very high.

13 / 30

A company is running a machine learning prediction service that generates 100 TB of predictions every

day. A Machine Learning Specialist must generate a visualization of the daily precision-recall curve from

the predictions, and forward a read-only version to the Business team.

Which solution requires the LEAST coding effort?

Run daily Amazon EMR workflow to generate precision-recall data, and save the results in Amazon S3. Give the Business team read-only access to S3.

Generate daily precision-recall data in Amazon QuickSight, and publish the results in a dashboard shared with the Business team.

Run a daily Amazon EMR workflow to generate precision-recall data, and save the results in Amazon S3. Visualize the arrays in Amazon QuickSight, and publish them in a dashboard shared with the Business team.

Generate daily precision-recall data in Amazon ES, and publish the results in a dashboard shared with the Business team.

14 / 30

A Machine Learning Specialist built an image classification deep learning model. However, the Specialist

ran into an overfitting problem in which the training and testing accuracies were 99% and 75%,

respectively.

How should the Specialist address this issue and what is the reason behind it?

The learning rate should be increased because the optimization process was trapped at a local minimum.

The dropout rate at the flatten layer should be increased because the model is not generalized enough.

The dimensionality of dense layer next to the flatten layer should be increased because the model is not complex enough.

The epoch number should be increased because the optimization process was terminated before it reached the global minimum.

15 / 30

A Data Science team within a large company uses Amazon SageMaker notebooks to access data stored

in Amazon S3 buckets. The IT Security team is concerned that internet-enabled notebook instances create

a security vulnerability where malicious code running on the instances could compromise data privacy. The

company mandates that all instances stay within a secured VPC with no internet access, and data

communication traffic must stay within the AWS network.

How should the Data Science team configure the notebook instance placement to meet these

requirements?

Associate the Amazon SageMaker notebook with a private subnet in a VPC. Place the Amazon SageMaker endpoint and S3 buckets within the same VPC.

Associate the Amazon SageMaker notebook with a private subnet in a VPC. Use IAM policies to grant access to Amazon S3 and Amazon SageMaker.

Associate the Amazon SageMaker notebook with a private subnet in a VPC. Ensure the VPC has S3 VPC endpoints and Amazon SageMaker VPC endpoints attached to it.

Associate the Amazon SageMaker notebook with a private subnet in a VPC. Ensure the VPC has a NAT gateway and an associated security group allowing only outbound connections to Amazon S3 and Amazon SageMaker.

16 / 30

A manufacturing company has structured and unstructured data stored in an Amazon S3 bucket. A Machine Learning Specialist wants to use SQL to run queries on this data. Which solution requires the LEAST effort to be able to query this data?

Use AWS Data Pipeline to transform the data and Amazon RDS to run queries.

Use AWS Glue to catalogue the data and Amazon Athena to run queries.

Use AWS Batch to run ETL on the data and Amazon Aurora to run the queries.

Use AWS Lambda to transform the data and Amazon Kinesis Data Analytics to run queries.

17 / 30

A Machine Learning Specialist trained a regression model, but the first iteration needs optimizing. The

Specialist needs to understand whether the model is more frequently overestimating or underestimating

the target.

What option can the Specialist use to determine whether it is overestimating or underestimating the target

value?

Root Mean Square Error (RMSE)

Residual plots

Area under the curve

Confusion matrix

18 / 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?

A histogram showing whether the most important input feature is Gaussian.

A scatter plot with points colored by target variable that uses t-Distributed Stochastic Neighbor Embedding (t-SNE) to visualize the large number of input variables in an easier-to-read dimension.

A scatter plot showing the performance of the objective metric over each training iteration.

A scatter plot showing the correlation between maximum tree depth and the objective metric.

19 / 30

Machine Learning Specialist is building a model to predict future employment rates based on a wide range

of economic factors. While exploring the data, the Specialist notices that the magnitude of the input

features vary greatly. The Specialist does not want variables with a larger magnitude to dominate the

model.

What should the Specialist do to prepare the data for model training?

Apply quantile binning to group the data into categorical bins to keep any relationships in the data by replacing the magnitude with distribution.

Apply the Cartesian product transformation to create new combinations of fields that are independent of the magnitude.

Apply normalization to ensure each field will have a mean of 0 and a variance of 1 to remove any significant magnitude.

Apply the orthogonal sparse bigram (OSB) transformation to apply a fixed-size sliding window to generate new features of a similar magnitude.

20 / 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?

Create a NAT gateway within the corporate VPC.

Route Amazon SageMaker traffic through an on-premises network.

Create Amazon SageMaker VPC interface endpoints within the corporate VPC.

Create VPC peering with Amazon VPC hosting Amazon SageMaker.

21 / 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?

Use the SageMaker batch transform feature to transform the training data into a DataFrame.

Use AWS Glue to compress the data into the Apache Parquet format.

Transform the dataset into the RecordIO protobuf format.

Use the SageMaker hyperparameter optimization feature to automatically optimize the data.

22 / 30

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

The training channel identifying the location of training data on an Amazon S3 bucket.

The validation channel identifying the location of validation data on an Amazon S3 bucket.

The IAM role that Amazon SageMaker can assume to perform tasks on behalf of the users.

Hyperparameters in a JSON array as documented for the algorithm used.

The Amazon EC2 instance class specifying whether training will be run using CPU or GPU.

The output path specifying where on an Amazon S3 bucket the trained model will persist.

23 / 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.)

Add more deep trees to the random forest to enable the model to learn more features.

Include a copy of the samples in the test dataset in the training dataset.

Generate more positive samples by duplicating the positive samples and adding a small amount of noise to the duplicated data.

Change the cost function so that false negatives have a higher impact on the cost value than false positives.

Change the cost function so that false positives have a higher impact on the cost value than false negatives.

24 / 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.)

Change the XGBoost eval_metric parameter to optimize based on rmse instead of error.

Increase the XGBoost scale_pos_weight parameter to adjust the balance of positive and negative weights.

Increase the XGBoost max_depth parameter because the model is currently underfitting the data.

Change the XGBoost eval_metric parameter to optimize based on AUC instead of error.

Decrease the XGBoost max_depth parameter because the model is currently overfitting the data.

25 / 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?

Amazon SageMaker notebook instances are based on the EC2 instances within the customer account, but they run outside of VPCs.

Amazon SageMaker notebook instances are based on the Amazon ECS service within customer accounts.

Amazon SageMaker notebook instances are based on EC2 instances running within AWS service accounts.

Amazon SageMaker notebook instances are based on AWS ECS instances running within AWS service accounts.

26 / 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?

Ingest .CSV data using Apache Kafka Streams on Amazon EC2 instances and use Kafka Connect S3 to serialize data as Parquet

Ingest .CSV data from Amazon Kinesis Data Streams and use Amazon Glue to convert data into Parquet.

Ingest .CSV data using Apache Spark Structured Streaming in an Amazon EMR cluster and use Apache Spark to convert data into Parquet.

Ingest .CSV data from Amazon Kinesis Data Streams and use Amazon Kinesis Data Firehose to convert data into Parquet.

27 / 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?

Receiver operating characteristic (ROC) curve

Misclassification rate

Root Mean Square Error (RMSE)

L1 norm

28 / 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?

Use an AWS Glue crawler to crawl S3 data, an AWS Lambda function to trigger an AWS Glue ETL job, and an AWS Glue Data catalog to search and discover metadata.

Use an AWS Glue crawler to crawl S3 data, an AWS Lambda function to trigger an AWS Batch job, and an external Apache Hive metastore to search and discover metadata.

Use an AWS Glue crawler to crawl S3 data, an Amazon CloudWatch alarm to trigger an AWS Batch job, and an AWS Glue Data Catalog to search and discover metadata.

Use an AWS Glue crawler to crawl S3 data, an Amazon CloudWatch alarm to trigger an AWS Glue ETL job, and an external Apache Hive metastore to search and discover metadata.

29 / 30

A Machine Learning Specialist is developing a daily ETL workflow containing multiple ETL jobs. The

workflow consists of the following processes:

• Start the workflow as soon as data is uploaded to Amazon S3.

• When all the datasets are available in Amazon S3, start an ETL job to join the uploaded datasets with

multiple terabyte-sized datasets already stored in Amazon S3.

• Store the results of joining datasets in Amazon S3.

• If one of the jobs fails, send a notification to the Administrator.

Which configuration will meet these requirements?

Use AWS Lambda to trigger an AWS Step Functions workflow to wait for dataset uploads to complete in Amazon S3. Use AWS Glue to join the datasets. Use an Amazon CloudWatch alarm to send an SNS notification to the Administrator in the case of a failure.

Develop the ETL workflow using AWS Lambda to start an Amazon SageMaker notebook instance. Use a lifecycle configuration script to join the datasets and persist the results in Amazon S3. Use an Amazon CloudWatch alarm to send an SNS notification to the Administrator in the case of a failure.

Develop the ETL workflow using AWS Batch to trigger the start of ETL jobs when data is uploaded to Amazon S3. Use AWS Glue to join the datasets in Amazon S3. Use an Amazon CloudWatch alarm to send an SNS notification to the Administrator in the case of a failure.

Use AWS Lambda to chain other Lambda functions to read and join the datasets in Amazon S3 as soon as the data is uploaded to Amazon S3. Use an Amazon CloudWatch alarm to send an SNS notification to the Administrator in the case of a failure.

30 / 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?

Convert the records to Apache Parquet format.

Convert the records to JSON format.

Convert the records to GZIP CSV format.

Convert the records to XML format.

Your score is