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The MLS-C01 exam’s full name is AWS Certified Machine Learning – Specialty (MLS-C01) with a score of 820/1000. 750 is required to pass. It’s a tough exam that requires spending almost all of your allocated time on it. However, the pace of modern society is fast, and people’s time is limited.

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[1]

A city wants to monitor its air quality to address the consequences of air pollution A Machine Learning Specialist needs to forecast the air quality in parts per million of contaminates for the next 2 days in the city As this is a prototype, only daily data from the last year is available

Which model is MOST likely to provide the best results in Amazon SageMaker?

A. Use the Amazon SageMaker k-Nearest-Neighbors (kNN) algorithm on the single time series consisting of the full year of data with a predictor_type of the regressor.
B. Use Amazon SageMaker Random Cut Forest (RCF) on the single time series consisting of the full year of data.
C. Use the Amazon SageMaker Linear Learner algorithm on the single time series consisting of the full year of data with a predictor_type of the regressor.
D. Use the Amazon SageMaker Linear Learner algorithm on the single time series consisting of the full year of data with a predictor_type of a classifier.

[2]

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 the 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. Linear support vector machine (SVM)
B. Decision tree
C. Support vector machine (SVM) with a radial basis function kernel
D. Single perceptron with a Tanh activation function

[3]

A machine learning specialist stores IoT soil sensor data in the Amazon DynamoDB table and stores weather event data as JSON files in Amazon S3. The dataset in DynamoDB is 10 GB in size and the dataset in Amazon S3 is 5 GB in size.

The specialist wants to train a model on this data to help predict soil moisture levels as a function of weather events using Amazon SageMaker.

Which solution will accomplish the necessary transformation to train the Amazon SageMaker model with the LEAST amount of administrative overhead?

A. Launch an Amazon EMR cluster. Create an Apache Hive external table for the DynamoDB table and S3 data. Join the Hive tables and write the results out to Amazon S3.
B. Crawl the data using AWS Glue crawlers. Write an AWS Glue ETL job that merges the two tables and writes the output to an Amazon Redshift cluster.
C. Enable Amazon DynamoDB Streams on the sensor table. Write an AWS Lambda function that consumes the stream and appends the results to the existing weather files in Amazon S3.
D. Crawl the data using AWS Glue crawlers. Write an AWS Glue ETL job that merges the two tables and writes the output in CSV format to Amazon S3.

[4]

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. Latent Dirichlet Allocation (LDA)
B. Recurrent neural network (RNN)
C. K-means
D. Convolutional neural network (CNN)

[5]

A Data Scientist needs to migrate an existing on-premises ETL process to the cloud. The current process runs at regular time intervals and uses PySpark to combine and format multiple large data sources into a single consolidated output for downstream processing.

The Data Scientist has been given the following requirements to the cloud solution:
Combine multiple data sources.

Reuse existing PySpark logic.
Run the solution on the existing schedule.
Minimize the number of servers that will need to be managed.

Which architecture should the Data Scientist use to build this solution?

A. Write the raw data to Amazon S3. Schedule an AWS Lambda function to submit a Spark step to a persistent Amazon EMR cluster based on the existing schedule. Use the existing PySpark logic to run the ETL job on the EMR cluster. Output the results to a “processed” location in Amazon S3 that is accessible for downstream use.

B. Write the raw data to Amazon S3. Create an AWS Glue ETL job to perform the ETL processing against the input data. Write the ETL job in PySpark to leverage the existing logic. Create a new AWS Glue trigger to trigger the ETL job based on the existing schedule. Configure the output target of the ETL job to write to a “processed” location in Amazon
S3 is accessible for downstream use.

C. Write the raw data to Amazon S3. Schedule an AWS Lambda function to run on the existing schedule and process the input data from Amazon S3. Write the Lambda logic in Python and implement the existing PySpark logic to perform the ETL process. Have the Lambda function output the results to a “processed” location in Amazon S3 that is
accessible for downstream use.

D. Use Amazon Kinesis Data Analytics to stream the input data and perform real-time SQL queries against the stream to carry out the required transformations within the stream. Deliver the output results to a “processed” location in Amazon S3 that is accessible for downstream use.

[6]

A Machine Learning Specialist prepared the following graph displaying the results of k-means fork = [1..10]:

Considering the graph, what is a reasonable selection for the optimal choice of k?


A. 1
B. 4
C. 7
D. 10

[7]

A power company wants to forecast future energy consumption for its customers in residential properties and commercial business properties. Historical power consumption data for the last 10 years is available.

A team of data scientists who performed the initial data analysis and feature selection will include the historical power consumption data
and data such as weather, number of individuals on the property, and public holidays.

The data scientists are using Amazon Forecast to generate forecasts.
Which algorithm in Forecast should the data scientists use to meet these requirements?

A. Autoregressive Integrated Moving Average (AIRMA)
B. Exponential Smoothing (ETS)
C. Convolutional Neural Network – Quantile Regression (CNN-QR)
D. Prophet

[8]

A Machine Learning Specialist is using Amazon SageMaker to host a model for a highly available customer-facing application.

The Specialist has trained a new version of the model, validated it with historical data, and now wants to deploy it to production To limit any risk of a negative customer experience, the Specialist wants to be able to monitor the model and roll it back if needed

What is the SIMPLEST approach with the LEAST risk to deploy the model and roll it back, if needed?

A. Create a SageMaker endpoint and configuration for the new model version. Redirect production traffic to the new endpoint by updating the client configuration. Revert traffic to the last version of the model does not perform as expected.

B. Create a SageMaker endpoint and configuration for the new model version. Redirect production traffic to the new endpoint by using a load balancer Revert traffic to the last version of the model does not perform as expected.

C. Update the existing SageMaker endpoint to use a new configuration that is weighted to send 5% of the traffic to the new variant. Revert traffic to the last version by resetting the weights if the model does not perform as expected.

D. Update the existing SageMaker endpoint to use a new configuration that is weighted to send 100% of the traffic to the new variant Revert traffic to the last version by resetting the weights if the model does not perform as expected.

[9]

Example Corp has an annual sale event from October to December. The company has sequential sales data from the past 15 years and wants to use Amazon ML to predict the sales for this year\\’s upcoming event.

Which method should Example Corp use to split the data into a training dataset and evaluation dataset?

A. Pre-split the data before uploading to Amazon S3
B. Have Amazon ML split the data randomly.
C. Have Amazon ML split the data sequentially.
D. Perform custom cross-validation on the data

[10]

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?

A. Modify the bash_profile file in the container and add a bash command to start the training program
B. Use CMD config in the Dockerfile to add the training program as a CMD of the image
C. Configure the training program as an ENTRYPOINT named train
D. Copy the training program to the directory /opt/ml/train

[11]

A Machine Learning Specialist is configuring automatic model tuning in Amazon SageMaker When using the hyperparameter optimization feature, which of the following guidelines should be followed to improve optimization?

Choose the maximum number of hyperparameters supported by

A. Amazon SageMaker to search the largest number of combinations possible
B. Specify a very large hyperparameter range to allow Amazon SageMaker to cover every possible value.
C. Use log-scaled hyperparameters to allow the hyperparameter space to be searched as quickly as possible
D. Execute only one hyperparameter tuning job at a time and improve tuning through successive rounds of experiments

[12]

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. Install AWS Systems Manager Agent on the underlying Amazon EC2 instance and use Systems Manager Automation to execute the package installation commands.

B. Create a Jupyter notebook file (.ipynb) with cells containing the package installation commands to execute and place the file under the /etc/init directory of each Amazon SageMaker notebook instance.

C. Use the conda package manager from within the Jupyter notebook console to apply the necessary conda packages to the default kernel of the notebook.

D. Create an Amazon SageMaker lifecycle configuration with package installation commands and assign the lifecycle configuration to the notebook instance.

Reference: https://towardsdatascience.com/automating-aws-sagemaker-notebooks-2dec62bc2c84

Correct answer:

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QUESTION 1 #

A Machine Learning Specialist is developing a custom video recommendation model for an application. The dataset used to train this model is very large with millions of data points and is hosted in an Amazon S3 bucket. The Specialist wants to avoid loading all of this data onto an Amazon SageMaker notebook instance because it would take hours to move and will exceed the attached 5 GB Amazon EBS volume on the notebook instance.

Which approach allows the Specialist to use all the data to train the model?

A. Load a smaller subset of the data into the SageMaker notebook and train locally. Confirm that the training code is executing and the model parameters seem reasonable. Initiate a SageMaker training job using the full dataset from the S3 bucket using Pipe input mode.

B. Launch an Amazon EC2 instance with an AWS Deep Learning AMI and attach the S3 bucket to the instance. Train on a small amount of the data to verify the training code and hyperparameters. Go back to Amazon SageMaker and train using the full dataset

C. Use AWS Glue to train a model using a small subset of the data to confirm that the data will be compatible with Amazon SageMaker. Initiate a SageMaker training job using the full dataset from the S3 bucket using Pipe input mode.

D. Load a smaller subset of the data into the SageMaker notebook and train locally. Confirm that the training code is executing and the model parameters seem reasonable. Launch an Amazon EC2 instance with an AWS Deep Learning AMI and attach the S3 bucket to train the full dataset.

Correct Answer: A

QUESTION 2 #

This graph shows the training and validation loss against the epochs for a neural network.
The network being trained is as follows:
1. Two dense layers, one output neuron
2. 100 neurons in each layer
3. 100 epochs
4. Random initialization of weights

Which technique can be used to improve model performance in terms of accuracy in the validation set?

A. Early stopping
B. Random initialization of weights with appropriate seed
C. Increasing the number of epochs
D. Adding another layer with the 100 neurons
Correct Answer: C

QUESTION 3 #

An online reseller has a large, multi-column dataset with one column missing 30% of its data A Machine Learning Does the specialist believe 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?

A. Listwise deletion
B. Last observation carried forward
C. Multiple imputation
D. Mean substitution
Correct Answer: C
Reference: https://worldwidescience.org/topicpages/i/imputing+missing+values.html

QUESTION 4 #

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. Initialize the words by term frequency-inverse document frequency (TF-IDF) vectors pretrained on a large collection
of news articles related to the energy sector.
B. Use gated recurrent units (GRUs) instead of LSTM and run the training process until the valid ation loss stops
decreasing.
C. Reduce the learning rate and run the training process until the training loss stops decreasing.
D. Initialize the words by word2vec embeddings pretrained on a large collection of news articles related to the energy
sector.
Correct Answer: C

QUESTION 5 #

Machine Learning Specialist is working with a media company to perform classification on popular articles from the company\\’s website. The company is using random forests to classify how popular an article will be before it is published. A sample of the data being used is below.

Given the dataset, the Specialist wants to convert the Day_Of_Week column to binary values.

What technique should be used to convert this column to binary values?

A. Binarization
B. One-hot encoding
C. Tokenization
D. Normalization transformation
Correct Answer: B

QUESTION 6 #

An e-commerce company wants to launch a new cloud-based product recommendation feature for its web application.

Due to data localization regulations, any sensitive data must not leave its on-premises data center, and the product recommendation model must be trained and tested using nonsensitive data only. Data transfer to the cloud must use IPsec.

The web application is hosted on-premises with a PostgreSQL database that contains all the data. The company wants the data to be uploaded securely to Amazon S3 each day for model retraining.

How should a machine learning specialist meet these requirements?

A. Create an AWS Glue job to connect to the PostgreSQL DB instance. Ingest tables without sensitive data through an AWS Site-to-Site VPN connection directly into Amazon S3.
B. Create an AWS Glue job to connect to the PostgreSQL DB instance. Ingest all data through an AWS Site-to-Site VPN connection into Amazon S3 while removing sensitive data using a PySpark job.
C. Use AWS Database Migration Service (AWS DMS) with table mapping to select PostgreSQL tables with no sensitive data through an SSL connection. Replicate data directly into Amazon S3.
D. Use PostgreSQL logical replication to replicate all data to PostgreSQL in Amazon EC2 through AWS Direct Connect with a VPN connection. Use AWS Glue to move data from Amazon EC2 to Amazon S3.
Correct Answer: C
Reference: https://docs.aws.amazon.com/dms/latest/userguide/CHAP_Source.PostgreSQL.html

QUESTION 7 #

A media company with a very large archive of unlabeled images, text, audio, and video footage wishes to index its assets to allow rapid identification of relevant content by the Research team. The company wants to use machine learning to accelerate the efforts of its in-house researchers who have limited machine learning expertise.

Which is the FASTEST route to index the assets?

A. Use Amazon Rekognition, Amazon Comprehend, and Amazon Transcribe to tag data into distinct
categories/classes.
B. Create a set of Amazon Mechanical Turk Human Intelligence Tasks to label all footage.
C. Use Amazon Transcribe to convert speech to text. Use the Amazon SageMaker Neural Topic Model (NTM) and Object Detection algorithms to tag data into distinct categories/classes.
D. Use the AWS Deep Learning AMI and Amazon EC2 GPU instances to create custom models for audio transcription and topic modeling and use object detection to tag data into distinct categories/classes.
Correct Answer: A

QUESTION 8 #

A company is using Amazon Textract to extract textual data from thousands of scanned text-heavy legal documents daily. The company uses this information to process loan applications automatically.

Some of the documents fail business validation and are returned to human reviewers, who investigate the errors. This activity increases the time to process the loan applications.

What should the company do to reduce the processing time of loan applications?

A. Configure Amazon Textract to route low-confidence predictions to Amazon SageMaker Ground Truth. Perform a manual review on those words before performing a business validation.
B. Use an Amazon Textract synchronous operation instead of an asynchronous operation.
C. Configure Amazon Textract to route low-confidence predictions to Amazon Augmented AI (Amazon A2I). Perform a manual review on those words before performing a business validation.
D. Use Amazon Rekognition\’s feature to detect text in an image to extract the data from scanned images. Use this information to process the loan applications.
Correct Answer: C

QUESTION 9 #

A Machine Learning Specialist has built a model using Amazon SageMaker built-in algorithms and is not getting expected accurate results The Specialist wants to use hyperparameter optimization to increase the model\\’s accuracy

Which method is the MOST repeatable and requires the LEAST amount of effort to achieve this?

A. Launch multiple training jobs in parallel with different hyperparameters
B. Create an AWS Step Functions workflow that monitors the accuracy in Amazon CloudWatch Logs and relaunches the training job with a defined list of hyperparameters
C. Create a hyperparameter tuning job and set the accuracy as an objective metric.
D. Create a random walk in the parameter space to iterate through a range of values that should be used for each individual hyperparameter
Correct Answer: B

QUESTION 10 #

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?

A. Increase the training data by adding variation in rotation for training images.
B. Increase the number of epochs for model training.
C. Increase the number of layers for the neural network.
D. Increase the dropout rate for the second-to-last layer.
Correct Answer: B

QUESTION 11 #

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 show a right skew as expected, with fewer individuals having a higher income, the age distribution also shows 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.)

A. Cross-validation
B. Numerical value binning
C. high-degree polynomial transformation
D. Logarithmic transformation
E. One hot encoding
Correct Answer: AB

QUESTION 12 #

For the given confusion matrix, what is the recall and precision of the model?

A. Recall = 0.92 Precision = 0.84
B. Recall = 0.84 Precision = 0.8
C. Recall = 0.92 Precision = 0.8
D. Recall = 0.8 Precision = 0.92
Correct Answer: A

QUESTION 13 #

A financial services company wants to adopt Amazon SageMaker as its default data science environment. The company\\’s data scientists run machine learning (ML) models on confidential financial data. The company is worried about data egress and wants an ML engineer to secure the environment.

Which mechanisms can the ML engineer use to control data egress from SageMaker? (Choose three.)

A. Connect to SageMaker by using a VPC interface endpoint powered by AWS PrivateLink.
B. Use SCPs to restrict access to SageMaker.
C. Disable root access on the SageMaker notebook instances.
D. Enable network isolation for training jobs and models.
E. Restrict notebook presigned URLs to specific IPs used by the company.
F. Protect data with encryption at rest and in transit. Use AWS Key Management Service (AWS KMS) to manage
encryption keys.
Correct Answer: BDF

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QUESTION 1
A Machine Learning Specialist is using Apache Spark for pre-processing training data As part of the Spark pipeline, the
Specialist wants to use Amazon SageMaker for training a model and hosting it Which of the following would the
Specialist do to integrate the Spark application with SageMaker? (Select THREE )
A. Download the AWS SDK for the Spark environment
B. Install the SageMaker Spark library in the Spark environment.
C. Use the appropriate estimator from the SageMaker Spark Library to train a model.
D. Compress the training data into a ZIP file and upload it to a pre-defined Amazon S3 bucket.
E. Use the sageMakerModel. transform method to get inferences from the model hosted in SageMaker
F. Convert the DataFrame object to a CSV file, and use the CSV file as input for obtaining inferences from SageMaker.
Correct Answer: DEF


QUESTION 2
Amazon Connect has recently been tolled out across a company as a contact call center The solution has been
configured to store voice call recordings on Amazon S3
The content of the voice calls are being analyzed for the incidents being discussed by the call operators Amazon
Transcribe is being used to convert the audio to text, and the output is stored on Amazon S3
Which approach will provide the information required for further analysis?
A. Use Amazon Comprehend with the transcribed files to build the key topics
B. Use Amazon Translate with the transcribed files to train and build a model for the key topics
C. Use the AWS Deep Learning AMI with Gluon Semantic Segmentation on the transcribed files to train and build a
model for the key topics
D. Use the Amazon SageMaker k-Nearest-Neighbors (kNN) algorithm on the transcribed files to generate a word
embeddings dictionary for the key topics
Correct Answer: B


QUESTION 3
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 SageMakerVariantInvocationsPerInstancesetting?
A. 10
B. 30
C. 600
D. 2,400
Correct Answer: C


QUESTION 4
An insurance company is developing a new device for vehicles that uses a camera to observe drivers\\’ behavior and
alert them when they appear distracted The company created approximately 10,000 training images in a controlled
environment that a Machine Learning Specialist will use to train and evaluate machine learning models
During the model evaluation the Specialist notices that the training error rate diminishes faster as the number of epochs
increases and the model is not accurately inferring on the unseen test images
Which of the following should be used to resolve this issue? (Select TWO)
A. Add vanishing gradient to the model
B. Perform data augmentation on the training data
C. Make the neural network architecture complex.
D. Use gradient checking in the model
E. Add L2 regularization to the model
Correct Answer: BD

QUESTION 5
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?
A. Run self-correlation on all features and remove highly correlated features
B. Normalize all numerical values to be between 0 and 1
C. Use an autoencoder or principal component analysis (PCA) to replace original features with new features
D. Cluster raw data using k-means and use sample data from each cluster to build a new dataset
Correct Answer: B


QUESTION 6
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:
1.
Support querying old and new data on Amazon S3 through Amazon Athena and Amazon Redshift Spectrum.
2.
Support event-driven ETL pipelines.
3.
Provide a quick and easy way to understand metadata.
Which approach meets trfese requirements?
A. 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.
B. 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.
C. 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.
D. 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.
Correct Answer: B

QUESTION 7
A Machine Learning Specialist is creating a new natural language processing application that processes a dataset
comprised of 1 million sentences. The aim is to then run Word2Vec to generate embeddings of the sentences and
enable different types of predictions.
Here is an example from the dataset:
“The quck BROWN FOX jumps over the lazy dog.”
Which of the following are the operations the Specialist needs to perform to correctly sanitize and prepare the data in a
repeatable manner? (Choose three.)
A. Perform part-of-speech tagging and keep the action verb and the nouns only
B. Normalize all words by making the sentence lowercase
C. Remove stop words using an English stopword dictionary.
D. Correct the typography on “quck” to “quick.”
E. One-hot encode all words in the sentence
F. Tokenize the sentence into words.
Correct Answer: ABD


QUESTION 8
For the given confusion matrix, what is the recall and precision of the model?

MLS-C01 exam questions-q8

A. Recall = 0.92 Precision = 0.84
B. Recall = 0.84 Precision = 0.8
C. Recall = 0.92 Precision = 0.8
D. Recall = 0.8 Precision = 0.92
Correct Answer: A


QUESTION 9
Which of the following metrics should a Machine Learning Specialist generally use to compare/evaluate machine
learning classification models against each other?
A. Recall
B. Misclassification rate
C. Mean absolute percentage error (MAPE)
D. Area Under the ROC Curve (AUC)
Correct Answer: A
Reference: https://docs.aws.amazon.com/machine-learning/latest/dg/multiclass-model-insights.html


QUESTION 10
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?
A. The class distribution in the dataset is imbalanced
B. Dataset shuffling is disabled
C. The batch size is too big
D. The learning rate is very high
Correct Answer: D
Reference: https://towardsdatascience.com/deep-learning-personal-notes-part-1-lesson-2-8946fe970b95

QUESTION 11
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. Write a direct connection to the SQL database within the notebook and pull data in
B. Push the data from Microsoft SQL Server to Amazon S3 using an AWS Data Pipeline and provide the S3 location
within the notebook.
C. Move the data to Amazon DynamoDB and set up a connection to DynamoDB within the notebook to pull data in
D. Move the data to Amazon ElastiCache using AWS DMS and set up a connection within the notebook to pull data in
for fast access.
Correct Answer: B


QUESTION 12
A Machine Learning Specialist is working with multiple data sources containing billions of records that need to be joined.
What features engineering and model development approach should the Specialist take with a dataset this large?
A. Use an Amazon SageMaker notebook for both feature engineering and model development
B. Use an Amazon SageMaker notebook for feature engineering and Amazon ML for model development
C. Use Amazon EMR for feature engineering and Amazon SageMaker SDK for model development
D. Use Amazon ML for both feature engineering and model development.
Correct Answer: B


QUESTION 13
A data scientist is developing a pipeline to ingest streaming web traffic data. The data scientist needs to
implement a process to identify unusual web traffic patterns as part of the pipeline. The patterns will be
used downstream for alerting and incident response. The data scientist has access to unlabeled historic
data to use, if needed.
The solution needs to do the following:
Calculate an anomaly score for each web traffic entry.
Adapt unusual event identification to changing web patterns over time.
Which approach should the data scientist implement to meet these requirements?
A. Use historic web traffic data to train an anomaly detection model using the Amazon SageMaker Random Cut Forest
(RCF) built-in model. Use an Amazon Kinesis Data Stream to process the incoming web traffic data. Attach a
preprocessing AWS Lambda function to perform data enrichment by calling the RCF model to calculate the anomaly
the score for each record.
B. Use historic web traffic data to train an anomaly detection model using the Amazon SageMaker built-in XGBoost
model. Use an Amazon Kinesis Data Stream to process the incoming web traffic data. Attach a preprocessing AWS
Lambda function to perform data enrichment by calling the XGBoost model to calculate the anomaly score for each
record.
C. Collect the streaming data using Amazon Kinesis Data Firehose. Map the delivery stream as an input source for
Amazon Kinesis Data Analytics. Write a SQL query to run in real-time against the streaming data with the k-Nearest
Neighbors (kNN) SQL extension to calculate anomaly scores for each record using a tumbling window.
D. Collect the streaming data using Amazon Kinesis Data Firehose. Map the delivery stream as an input source for
Amazon Kinesis Data Analytics. Write a SQL query to run in real-time against the streaming data with the Amazon
Random Cut Forest (RCF) SQL extension to calculate anomaly scores for each record using a sliding window.
Correct Answer: A

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Latest Amazon AWS MLS-C01 practice exam questions at here:

QUESTION 1
A Machine Learning Specialist is using Apache Spark for pre-processing training data As part of the Spark pipeline, the
Specialist wants to use Amazon SageMaker for training a model and hosting it Which of the following would the
Specialist do to integrate the Spark application with SageMaker? (Select THREE )
A. Download the AWS SDK for the Spark environment
B. Install the SageMaker Spark library in the Spark environment.
C. Use the appropriate estimator from the SageMaker Spark Library to train a model.
D. Compress the training data into a ZIP file and upload it to a pre-defined Amazon S3 bucket.
E. Use the sageMakerModel. transform method to get inferences from the model hosted in SageMaker
F. Convert the DataFrame object to a CSV file, and use the CSV file as input for obtaining inferences from SageMaker.
Correct Answer: DEF


QUESTION 2
Amazon Connect has recently been tolled out across a company as a contact call center The solution has been
configured to store voice call recordings on Amazon S3
The content of the voice calls are being analyzed for the incidents being discussed by the call operators Amazon
Transcribe is being used to convert the audio to text, and the output is stored on Amazon S3
Which approach will provide the information required for further analysis?
A. Use Amazon Comprehend with the transcribed files to build the key topics
B. Use Amazon Translate with the transcribed files to train and build a model for the key topics
C. Use the AWS Deep Learning AMI with Gluon Semantic Segmentation on the transcribed files to train and build a
model for the key topics
D. Use the Amazon SageMaker k-Nearest-Neighbors (kNN) algorithm on the transcribed files to generate a word
embeddings dictionary for the key topics
Correct Answer: B

QUESTION 3
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 SageMakerVariantInvocationsPerInstancesetting?
A. 10
B. 30
C. 600
D. 2,400
Correct Answer: C

QUESTION 4
An insurance company is developing a new device for vehicles that uses a camera to observe drivers\\’ behavior and
alert them when they appear distracted The company created approximately 10,000 training images in a controlled
environment that a Machine Learning Specialist will use to train and evaluate machine learning models
During the model evaluation the Specialist notices that the training error rate diminishes faster as the number of epochs
increases and the model is not accurately inferring on the unseen test images
Which of the following should be used to resolve this issue? (Select TWO)
A. Add vanishing gradient to the model
B. Perform data augmentation on the training data
C. Make the neural network architecture complex.
D. Use gradient checking in the model
E. Add L2 regularization to the model
Correct Answer: BD

QUESTION 5
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?
A. Run self-correlation on all features and remove highly correlated features
B. Normalize all numerical values to be between 0 and 1
C. Use an autoencoder or principal component analysis (PCA) to replace original features with new features
D. Cluster raw data using k-means and use sample data from each cluster to build a new dataset
Correct Answer: B

QUESTION 6
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:
1.
Support querying old and new data on Amazon S3 through Amazon Athena and Amazon Redshift Spectrum.
2.
Support event-driven ETL pipelines.
3.
Provide a quick and easy way to understand metadata.
Which approach meets trfese requirements?
A. 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.
B. 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.
C. 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.
D. 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.
Correct Answer: B

QUESTION 7
A Machine Learning Specialist is creating a new natural language processing application that processes a dataset
comprised of 1 million sentences. The aim is to then run Word2Vec to generate embeddings of the sentences and
enable different types of predictions.
Here is an example from the dataset:
“The quck BROWN FOX jumps over the lazy dog.”
Which of the following are the operations the Specialist needs to perform to correctly sanitize and prepare the data in a
repeatable manner? (Choose three.)
A. Perform part-of-speech tagging and keep the action verb and the nouns only
B. Normalize all words by making the sentence lowercase
C. Remove stop words using an English stopword dictionary.
D. Correct the typography on “quck” to “quick.”
E. One-hot encode all words in the sentence
F. Tokenize the sentence into words.
Correct Answer: ABD

QUESTION 8
For the given confusion matrix, what is the recall and precision of the model?

MLS-C01 exam questions-q8

A. Recall = 0.92 Precision = 0.84
B. Recall = 0.84 Precision = 0.8
C. Recall = 0.92 Precision = 0.8
D. Recall = 0.8 Precision = 0.92
Correct Answer: A

QUESTION 9
Which of the following metrics should a Machine Learning Specialist generally use to compare/evaluate machine
learning classification models against each other?
A. Recall
B. Misclassification rate
C. Mean absolute percentage error (MAPE)
D. Area Under the ROC Curve (AUC)
Correct Answer: A
Reference: https://docs.aws.amazon.com/machine-learning/latest/dg/multiclass-model-insights.html


QUESTION 10
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?
A. The class distribution in the dataset is imbalanced
B. Dataset shuffling is disabled
C. The batch size is too big
D. The learning rate is very high
Correct Answer: D
Reference: https://towardsdatascience.com/deep-learning-personal-notes-part-1-lesson-2-8946fe970b95

QUESTION 11
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. Write a direct connection to the SQL database within the notebook and pull data in
B. Push the data from Microsoft SQL Server to Amazon S3 using an AWS Data Pipeline and provide the S3 location
within the notebook.
C. Move the data to Amazon DynamoDB and set up a connection to DynamoDB within the notebook to pull data in
D. Move the data to Amazon ElastiCache using AWS DMS and set up a connection within the notebook to pull data in
for fast access.
Correct Answer: B


QUESTION 12
A Machine Learning Specialist is working with multiple data sources containing billions of records that need to be joined.
What feature engineering and model development approach should the Specialist take with a dataset this large?
A. Use an Amazon SageMaker notebook for both feature engineering and model development
B. Use an Amazon SageMaker notebook for feature engineering and Amazon ML for model development
C. Use Amazon EMR for feature engineering and Amazon SageMaker SDK for model development
D. Use Amazon ML for both feature engineering and model development.
Correct Answer: B

QUESTION 13
A data scientist is developing a pipeline to ingest streaming web traffic data. The data scientist needs to
implement a process to identify unusual web traffic patterns as part of the pipeline. The patterns will be
used downstream for alerting and incident response. The data scientist has access to unlabeled historic
data to use, if needed.
The solution needs to do the following:
Calculate an anomaly score for each web traffic entry.
Adapt unusual event identification to changing web patterns over time.
Which approach should the data scientist implement to meet these requirements?
A. Use historic web traffic data to train an anomaly detection model using the Amazon SageMaker Random Cut Forest
(RCF) built-in model. Use an Amazon Kinesis Data Stream to process the incoming web traffic data. Attach a
preprocessing AWS Lambda function to perform data enrichment by calling the RCF model to calculate the anomaly
score for each record.
B. Use historic web traffic data to train an anomaly detection model using the Amazon SageMaker built-in XGBoost
model. Use an Amazon Kinesis Data Stream to process the incoming web traffic data. Attach a preprocessing AWS
Lambda function to perform data enrichment by calling the XGBoost model to calculate the anomaly score for each
record.
C. Collect the streaming data using Amazon Kinesis Data Firehose. Map the delivery stream as an input source for
Amazon Kinesis Data Analytics. Write a SQL query to run in real time against the streaming data with the k-Nearest
Neighbors (kNN) SQL extension to calculate anomaly scores for each record using a tumbling window.
D. Collect the streaming data using Amazon Kinesis Data Firehose. Map the delivery stream as an input source for
Amazon Kinesis Data Analytics. Write a SQL query to run in real time against the streaming data with the Amazon
Random Cut Forest (RCF) SQL extension to calculate anomaly scores for each record using a sliding window.
Correct Answer: A

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Amazon AWS MLS-C01 pdf https://drive.google.com/file/d/1bGGgVyYsODGA-b80wCiQS1__BBLxSdLB/view?usp=sharing

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