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Google Professional Machine Learning Engineer Sample Questions (Q231-Q236):

NEW QUESTION # 231
Your organization manages an online message board A few months ago, you discovered an increase in toxic language and bullying on the message board. You deployed an automated text classifier that flags certain comments as toxic or harmful. Now some users are reporting that benign comments referencing their religion are being misclassified as abusive Upon further inspection, you find that your classifier's false positive rate is higher for comments that reference certain underrepresented religious groups. Your team has a limited budget and is already overextended. What should you do?

A. Add synthetic training data where those phrases are used in non-toxic ways
B. Remove the model and replace it with human moderation.
C. Replace your model with a different text classifier.
D. Raise the threshold for comments to be considered toxic or harmful

Answer: A

Explanation:
This approach would help to improve the performance of the classifier by providing it with more examples of the religious phrases being used in non-toxic ways. This would allow the classifier to better differentiate between toxic and non-toxic comments that reference these religious groups. Additionally, synthetic data is a cost-effective way to improve the performance of an existing model without requiring a significant investment in human resources.

NEW QUESTION # 232
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?

A. Amazon Kinesis Data Firehose to transform the data and put it into an Amazon S3 bucket.
B. Amazon Kinesis Data Analytics with an AWS Lambda function to transform the data.
C. An Amazon Kinesis Client Library to transform the data and save it to an Amazon ES cluster.
D. AWS Glue with a custom ETL script to transform the data.

Answer: B

Explanation:
Explanation/Reference: https://aws.amazon.com/big-data/real-time-analytics-featured-partners/

NEW QUESTION # 233
You are developing an ML model intended to classify whether X-Ray images indicate bone fracture risk. You have trained on Api Resnet architecture on Vertex AI using a TPU as an accelerator, however you are unsatisfied with the trainning time and use memory usage. You want to quickly iterate your training code but make minimal changes to the code. You also want to minimize impact on the models accuracy. What should you do?

A. Reduce the dimensions of the images used un the model
B. Reduce the global batch size from 1024 to 256
C. Configure your model to use bfloat16 instead float32
D. Reduce the number of layers in the model architecture

Answer: B

NEW QUESTION # 234
Your team is working on an NLP research project to predict political affiliation of authors based on articles they have written. You have a large training dataset that is structured like this:

You followed the standard 80%-10%-10% data distribution across the training, testing, and evaluation subsets. How should you distribute the training examples across the train-test-eval subsets while maintaining the 80-10-10 proportion?

Answer: C

Explanation:
The best way to distribute the training examples across the train-test-eval subsets while maintaining the 80-
10-10 proportion is to use option C. This option ensures that each subset contains a balanced and representative sample of the different classes (Democrat and Republican) and the different authors. This way, the model can learn from a diverse and comprehensive set of articles and avoid overfitting or underfitting.
Option C also avoids the problem of data leakage, which occurs when the same author appears in more than one subset, potentially biasing the model and inflating its performance. Therefore, option C is the most suitable technique for this use case.

NEW QUESTION # 235
You developed a Transformer model in TensorFlow to translate text Your training data includes millions of documents in a Cloud Storage bucket. You plan to use distributed training to reduce training time. You need to configure the training job while minimizing the effort required to modify code and to manage the clusters configuration. What should you do?

A. Create a Vertex Al custom training job with GPU accelerators for the second worker pool Use tf .distribute.MultiWorkerMirroredStrategy for distribution.
B. Create a training job that uses Cloud TPU VMs Use tf.distribute.TPUStrategy for distribution.
C. Create a Vertex Al custom distributed training job with Reduction Server Use N1 high-memory machine type instances for the first and second pools, and use N1 high-CPU machine type instances for the third worker pool.
D. Create a Vertex Al custom training job with a single worker pool of A2 GPU machine type instances Use tf .distribute.MirroredStraregy for distribution.

Answer: D

NEW QUESTION # 236
......

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RELIABLE PROFESSIONAL-MACHINE-LEARNING-ENGINEER DUMPS FREE - PROFESSIONAL-MACHINE-LEARNING-ENGINEER RELIABLE EXAM SAMPLE

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