MLS-C01 practice questions 321–330

1. Question 321 · 368 in bank

   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?

   • AUse the SageMaker batch transform feature to transform the training data into a DataFrame.
   • BUse AWS Glue to compress the data into the Apache Parquet format.
   • CTransform the dataset into the RecordIO protobuf format.
   • DUse the SageMaker hyperparameter optimization feature to automatically optimize the data.

   Show answer

   Answer: C

   Explanation

   Anwer is C. Most Amazon SageMaker algorithms work best when you use the optimized protobuf recordIO format for the training data. https://docs.aws.amazon.com/sagemaker/latest/dg/cdf-training.html

   Source: Adapted from community discussion — stamarpadar

   References

   • AWS documentation

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2. Question 322 · 368 in bank

   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?

   • AIncrease the training data by adding variation in rotation for training images.
   • BIncrease the number of epochs for model training
   • CIncrease the number of layers for the neural network.
   • DIncrease the dropout rate for the second-to-last layer.

   Show answer

   Answer: A

   Explanation

   NO CORRECT TRAINING DATA, MORE WORKS JUST WASTE TIME. ONE OF THE REASONS FOR POOR ACCURACY COULD BE INSUFFICIENT DATA. THIS CAN BE OVERCOME BY IMAGE AUGMENTATION. IMAGE AUGMENTATION IS A TECHNIQUE OF INCREASING THE DATASET SIZE BY PROCESSING (MIRRORING, FLIPPING, ROTATING, INCREASING/DECREASING BRIGHTNESS, CONTRAST, COLOR) THE IMAGES. HTTPS://MEDIUM.COM/DATADRIVENINVESTOR/AUTO-MODEL-TUNING-FOR-KERAS-ON-AMAZON-SAGEMAKER-PLANT-SEEDLING-DATASET-7B591334501E ANSWER A. ADD MORE TRAINING DATA FOR ROTATION IMAGES COULD BE A WAY TO DEAL WITH ISSUE

   Source: Adapted from community discussion — DonaldCMLIN

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3. Question 323 · 368 in bank

   A Machine Learning Specialist needs to be able to ingest streaming data and store it in Apache Parquet files for exploration and analysis. Which of the following services would both ingest and store this data in the correct format?

   • AAWS DMS
   • BAmazon Kinesis Data Streams
   • CAmazon Kinesis Data Firehose
   • DAmazon Kinesis Data Analytics

   Show answer

   Answer: C

   Explanation

   the answer is C. as the main point of the question is data transformation to Parquet format which is done by Kinesis Data Firehose not Data Stream. Coming to the data store the data store in Kinesis Data Stream is only for couple of days so it does not serve the purpose here

   Source: Adapted from community discussion — JayK

   References

   • AWS documentation
   • AWS documentation

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4. Question 324 · 368 in bank

   A data scientist has explored and sanitized a dataset in preparation for the modeling phase of a supervised learning task. The statistical dispersion can vary widely between features, sometimes by several orders of magnitude. Before moving on to the modeling phase, the data scientist wants to ensure that the prediction performance on the production data is as accurate as possible. Which sequence of steps should the data scientist take to meet these requirements?

   • AApply random sampling to the dataset. Then split the dataset into training, validation, and test sets.
   • BSplit the dataset into training, validation, and test sets. Then rescale the training set and apply the same scaling to the validation and test sets.
   • CRescale the dataset. Then split the dataset into training, validation, and test sets.
   • DSplit the dataset into training, validation, and test sets. Then rescale the training set, the validation set, and the test set independently.

   Show answer

   Answer: B

   Explanation

   https://stackoverflow.com/questions/49444262/normalize-data-before-or-after-split-of-training-and-testing-data C also leads to data leakage. You are using the test data to scale everything. So part of the data in the test set is used to scale for when you build the model on the training and check against the validation set.

   Source: Adapted from community discussion — masoa3b

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5. Question 325 · 368 in bank

   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?

   • AAmazon SageMaker notebook instances are based on the EC2 instances within the customer account, but they run outside of VPCs.
   • BAmazon SageMaker notebook instances are based on the Amazon ECS service within customer accounts.
   • CAmazon SageMaker notebook instances are based on EC2 instances running within AWS service accounts.
   • DAmazon SageMaker notebook instances are based on AWS ECS instances running within AWS service accounts.

   Show answer

   Answer: C

   Explanation

   The correct answer HAS TO be A The instances are running in customer accounts but it's in an AWS managed VPC while exposing ENI to customer VPC if it was chosen. See explanation at https://aws.amazon.com/blogs/machine-learning/understanding-amazon-sagemaker-notebook-instance-networking-configurations-and-advanced-routing-options/

   Source: Adapted from community discussion — dhs227

   References

   • AWS documentation
   • AWS documentation
   • AWS documentation

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6. Question 326 · 368 in bank

   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?

   • AInstall Python 3 and boto3 on their laptop and continue the code development using that environment.
   • BDownload 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.
   • CDownload TensorFlow from tensorflow.org to emulate the TensorFlow kernel in the SageMaker environment.
   • DDownload the SageMaker notebook to their local environment, then install Jupyter Notebooks on their laptop and continue the development in a local notebook.

   Show answer

   Answer: B

   Explanation

   My assumption is that D there is no way to test the code. You need the Sagemaker SDK in order to utilize dockerized container of Tensorflow from Sagemaker is my best guess.

   Source: Adapted from community discussion — AddiWei

   References

   • AWS documentation

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7. Question 327 · 368 in bank

   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?

   • AIngest the data using Amazon Kinesis Data Firehose, and use Amazon Kinesis Data Analytics Random Cut Forest (RCF) for anomaly detection. Then use Kinesis Data Firehose to stream the results to Amazon S3.
   • BIngest the data into Apache Spark Streaming using Amazon EMR, and use Spark MLlib with k-means to perform anomaly detection. Then store the results in an Apache Hadoop Distributed File System (HDFS) using Amazon EMR with a replication factor of three as the data lake.
   • CIngest the data and store it in Amazon S3. Use AWS Batch along with the AWS Deep Learning AMIs to train a k-means model using TensorFlow on the data in Amazon S3.
   • DIngest the data and store it in Amazon S3. Have an AWS Glue job that is triggered on demand transform the new data. Then use the built-in Random Cut Forest (RCF) model within Amazon SageMaker to detect anomalies in the data.

   Show answer

   Answer: A

   Explanation

   Answer is A. As the word anamoly talks about Random Cut Forest in the exam and that can be done in a cost effective manner using Kinesis Data Analytics

   Source: Adapted from community discussion — JayK

   References

   • AWS documentation
   • AWS documentation
   • AWS documentation

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8. Question 328 · 368 in bank

   A Data Scientist wants to gain real-time insights into a data stream of GZIP files. Which solution would allow the use of SQL to query the stream with the LEAST latency?

   • AAmazon Kinesis Data Analytics with an AWS Lambda function to transform the data.
   • BAWS Glue with a custom ETL script to transform the data.
   • CAn Amazon Kinesis Client Library to transform the data and save it to an Amazon ES cluster.
   • DAmazon Kinesis Data Firehose to transform the data and put it into an Amazon S3 bucket.

   Show answer

   Answer: A

   Explanation

   A is correct. Kinesis Data Analytics can use lamda to convert GZIP and can run SQL on the converted data. https://aws.amazon.com/about-aws/whats-new/2017/10/amazon-kinesis-analytics-can-now-pre-process-data-prior-to-running-sql-queries/

   Source: Adapted from community discussion — cybe001

   References

   • AWS documentation

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9. Question 329 · 368 in bank

   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?

   • AAnXGBoost model where the objective parameter is set to multi:softmax
   • BA deep convolutional neural network (CNN) with a softmax activation function for the last layer
   • CA regression forest where the number of trees is set equal to the number of product categories
   • DA DeepAR forecasting model based on a recurrent neural network (RNN)

   Show answer

   Answer: A

   Explanation

   Answer is A. This a classification problem thus XGBoost and the fact that there are six categories SOFTMAX is the right activation function

   Source: Adapted from community discussion — JayK

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10. Question 330 · 368 in bank

    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?

    • AAmazon Comprehend syntax analysis and entity detection
    • BAmazon SageMaker BlazingText cbow mode
    • CNatural Language Toolkit (NLTK) stemming and stop word removal
    • DScikit-leam term frequency-inverse document frequency (TF-IDF) vectorizer

    Show answer

    Answer: D

    Explanation

    AWS COMPREHEND IS A NATURAL LANGUAGE PROCESSING (NLP) SERVICE THAT USES MACHINE LEARNING TO DISCOVER INSIGHTS FROM TEXT. AMAZON COMPREHEND PROVIDES KEYPHRASE EXTRACTION, SENTIMENT ANALYSIS, ENTITY RECOGNITION, TOPIC MODELING, AND LANGUAGE DETECTION APIS SO YOU CAN EASILY INTEGRATE NATURAL LANGUAGE PROCESSING INTO YOUR APPLICATIONS. HTTPS://AWS.AMAZON.COM/COMPREHEND/FEATURES/?NC1=H_LS JUST THROUGH AMAZON COMPREHEND IS MUCH EASY THAN OTHER THE MUCH MORE CONVENIENT ANSWER IS A.

    Source: Adapted from community discussion — DonaldCMLIN

    References

    • AWS documentation
    • AWS documentation

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MLS-C01 practice, question 321. 368 questions available.