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NEW QUESTION 53
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. Configure the training program as an ENTRYPOINTnamed trainB. Copy the training program to directory /opt/ml/trainC. Use CMD configin the Dockerfile to add the training program as a CMD of the imageD. Modify the bash_profile file in the container and add a bashcommand to start the training program

Answer: C

 

NEW QUESTION 54
A Machine Learning Specialist built an image classification deep learning model. However the Specialist ran into an overfitting problem in which the training and testing accuracies were 99% and 75%r respectively.
How should the Specialist address this issue and what is the reason behind it?

A. The learning rate should be increased because the optimization process was trapped at a local minimum.B. The dropout rate at the flatten layer should be increased because the model is not generalized enough.C. The dimensionality of dense layer next to the flatten layer should be increased because the model is not complex enough.D. The epoch number should be increased because the optimization process was terminated before it reached the global minimum.

Answer: A

 

NEW QUESTION 55
A Machine Learning Specialist must build out a process to query a dataset on Amazon S3 using Amazon Athena. The dataset contains more than 800,000 records stored as plaintext CSV files.
Each record contains 200 columns and is approximately 1.5 MB in size. Most queries will span 5 to 10 columns only.
How should the Machine Learning Specialist transform the dataset to minimize query runtime?

A. Convert the records to Apache Parquet format.B. Convert the records to XML format.C. Convert the records to JSON format.D. Convert the records to GZIP CSV format.

Answer: A

Explanation:
Using compressions will reduce the amount of data scanned by Amazon Athena, and also reduce your S3 bucket storage. It's a Win-Win for your AWS bill. Supported formats: GZIP, LZO, SNAPPY (Parquet) and ZLIB.
https://www.cloudforecast.io/blog/using-parquet-on-athena-to-save-money-on-aws/

 

NEW QUESTION 56
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. Download word embeddings pre-trained on a large corpus.B. Create word embedding vectors that store edit distance with every other word.C. Create one-hot word encoding vectors.D. Produce a set of synonyms for every word using Amazon Mechanical Turk.

Answer: A

Explanation:
As it is a interactive online dictionary, we need pre-trained word embedding thus the answer is D.
In addition, there is no mention that the online dictonary is unique and does not have a pre- trained word embedding.

 

NEW QUESTION 57
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 filesB. RecordIOC. Parquet filesD. Compressed JSON

Answer: C

 

NEW QUESTION 58
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