Demo Questions:
Question: 1
A Machine Learning Specialist observes several performance problems with the training portion of a machine learning solution on Amazon SageMaker The solution uses a large training dataset 2 TB in size and is using the SageMaker k-means algorithm The observed issues include the unacceptable length of time it takes before the training job launches and poor I/O throughput while training the model What should the Specialist do to address the performance issues with the current solution?
A. Use the SageMaker batch transform feature
B. Compress the training data into Apache Parquet format.
C. Ensure that the input mode for the training job is set to Pipe.
D. Copy the training dataset to an Amazon EFS volume mounted on the SageMaker instance.
Answer: B
Question: 2
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.
Answer: B
Question: 3
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
Answer: B
Question: 4
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? (Select 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.
Answer: A B D
Question: 5
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?
A. CSV files
B. Parquet files
C. Compressed JSON
D. RecordIO
Answer: B
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Question: 1
A Machine Learning Specialist observes several performance problems with the training portion of a machine learning solution on Amazon SageMaker The solution uses a large training dataset 2 TB in size and is using the SageMaker k-means algorithm The observed issues include the unacceptable length of time it takes before the training job launches and poor I/O throughput while training the model What should the Specialist do to address the performance issues with the current solution?
A. Use the SageMaker batch transform feature
B. Compress the training data into Apache Parquet format.
C. Ensure that the input mode for the training job is set to Pipe.
D. Copy the training dataset to an Amazon EFS volume mounted on the SageMaker instance.
Answer: B
Question: 2
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.
Answer: B
Question: 3
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
Answer: B
Question: 4
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? (Select 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.
Answer: A B D
Question: 5
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?
A. CSV files
B. Parquet files
C. Compressed JSON
D. RecordIO
Answer: B
Related Links:
https://www.dumps4download.com/mls-c01-dumps.html
https://www.dumps4download.com/amazon.html
