Amazon Amazon Certifications MLS-C01 Questions & Answers

• Question 301:

  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

  Correct Answer: C

• Question 302:

  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?

  A. Amazon Comprehend syntax analysts and entity detection

  B. Amazon SageMaker BlazingText allow mode

  C. Natural Language Toolkit (NLTK) stemming and stop word removal

  D. Scikit-learn term frequency-inverse document frequency (TF-IDF) vectorizers

  Correct Answer: D

  Reference: https://monkeylearn.com/sentiment-analysis/

• Question 303:

  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: BE

  https://www.kdnuggets.com/2019/12/5-techniques-prevent-overfitting-neural-networks.html 5 techniques to prevent overfitting:

  1.

  Simplifying the model

  2.

  Early stopping

  3.

  Use data argumentation

  4.

  Use regularization

  5.

  Use dropouts

• Question 304:

  A Machine Learning Specialist is packaging a custom ResNet model into a Docker container so the company can leverage Amazon SageMaker for training The Specialist is using Amazon EC2 P3 instances to train the model and needs to properly configure the Docker container to leverage the NVIDIA GPUs

  What does the Specialist need to do1?

  A. Bundle the NVIDIA drivers with the Docker image

  B. Build the Docker container to be NVIDIA-Docker compatible

  C. Organize the Docker container's file structure to execute on GPU instances.

  D. Set the GPU flag in the Amazon SageMaker Create TrainingJob request body

  Correct Answer: B

  https://docs.aws.amazon.com/sagemaker/latest/dg/sagemaker-dg.pdf page 55: If you plan to use GPU devices, make sure that your containers are nvidia-docker compatible. Only the CUDA toolkit should be included on containers. Don't bundle NVIDIA drivers with the image. For more information about nvidia-docker, see NVIDIA/nvidia-docker.

• Question 305:

  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 lime 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?

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

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

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

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

  Correct Answer: B

  https://github.com/ecloudvalley/Building-a-Data-Lake-with-AWS-Glue-and-Amazon-S3

• Question 306:

  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?

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

  B. 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.

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

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

  Correct Answer: B

  You could install docker-compose (and nvidia-docker if training with a gpu) for local training

  https://sagemaker.readthedocs.io/en/stable/overview.html#local-mode https://github.com/awslabs/amazon-sagemaker-examples/blob/master/sagemaker-python-sdk/tensorflow_distributed_mnist/tensorflow_local_mode_mnist.ipynb

• Question 307:

  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?

  A. Create one-hot word encoding vectors.

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

  C. Create word embedding factors that store edit distance with every other word.

  D. Download word embedding's pre-trained on a large corpus.

  Correct Answer: D

  Word embeddings are a type of dense representation of words, which encode semantic meaning in a vector form. These embeddings are typically pre-trained on a large corpus of text data, such as a large set of books, news articles, or web pages, and capture the context in which words are used. Word embeddings can be used as features for a nearest neighbor model, which can be used to find words used in similar contexts.

  Downloading pre-trained word embeddings is a good way to get started quickly and leverage the strengths of these representations, which have been optimized on a large amount of data. This is likely to result in more accurate and reliable features than other options like one-hot encoding, edit distance, or using Amazon Mechanical Turk to produce synonyms.

• Question 308:

  A Machine Learning Specialist is working with a large company to leverage machine learning within its products. The company wants to group its customers into categories based on which customers will and will not churn within the next 6 months. The company has labeled the data available to the Specialist.

  Which machine learning model type should the Specialist use to accomplish this task?

  A. Linear regression

  B. Classification

  C. Clustering

  D. Reinforcement learning

  Correct Answer: B

  The goal of classification is to determine to which class or category a data point (customer in our case) belongs to. For classification problems, data scientists would use historical data with predefined target variables AKA labels (churner/nonchurner) ?answers that need to be predicted ?to train an algorithm. With classification, businesses can answer the following questions:

  1.

  Will this customer churn or not?

  2.

  Will a customer renew their subscription?

  3.

  Will a user downgrade a pricing plan?

  4.

  Are there any signs of unusual customer behavior?

  Reference: https://www.kdnuggets.com/2019/05/churn-prediction-machine-learning.html

• Question 309:

  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 310:

  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: BCF

  https://towardsdatascience.com/nlp-extracting-the-main-topics-from-your-dataset-using-lda-in-minutes-21486f5aa925