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Latest Amazon AWS MLS-C01 practice exam questions at here:
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
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
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?
Correct Answer: C
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
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
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 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
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
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
Which of the following metrics should a Machine Learning Specialist generally use to compare/evaluate machine
learning classification models against each other?
B. Misclassification rate
C. Mean absolute percentage error (MAPE)
D. Area Under the ROC Curve (AUC)
Correct Answer: A
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
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
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
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
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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