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Google Professional Machine Learning Engineer Sample Questions (Q123-Q128):

NEW QUESTION # 123
A company uses a long short-term memory (LSTM) model to evaluate the risk factors of a particular energy sector. The model reviews multi-page text documents to analyze each sentence of the text and categorize it as either a potential risk or no risk. The model is not performing well, even though the Data Scientist has experimented with many different network structures and tuned the corresponding hyperparameters.
Which approach will provide the MAXIMUM performance boost?

A. Initialize the words by word2vec embeddings pretrained on a large collection of news articles related to the energy sector.B. Use gated recurrent units (GRUs) instead of LSTM and run the training process until the validation loss stops decreasing.C. Initialize the words by term frequency-inverse document frequency (TF-IDF) vectors pretrained on a large collection of news articles related to the energy sector.D. Reduce the learning rate and run the training process until the training loss stops decreasing.

Answer: D

NEW QUESTION # 124
A Machine Learning Specialist works for a credit card processing company and needs to predict which transactions may be fraudulent in near-real time. Specifically, the Specialist must train a model that returns the probability that a given transaction may fraudulent.
How should the Specialist frame this business problem?

A. Regression classificationB. Streaming classificationC. Multi-category classificationD. Binary classification

Answer: C

NEW QUESTION # 125
You are designing an architecture with a serverless ML system to enrich customer support tickets with informative metadata before they are routed to a support agent. You need a set of models to predict ticket priority, predict ticket resolution time, and perform sentiment analysis to help agents make strategic decisions when they process support requests. Tickets are not expected to have any domain-specific terms or jargon.
The proposed architecture has the following flow:

Which endpoints should the Enrichment Cloud Functions call?

A. 1 = Al Platform, 2 = Al Platform, 3 = AutoML VisionB. 1 = Al Platform, 2 = Al Platform, 3 = AutoML Natural LanguageC. 1 = cloud Natural Language API, 2 = Al Platform, 3 = Cloud Vision APID. 1 = Al Platform, 2 = Al Platform, 3 = Cloud Natural Language API

Answer: D

Explanation:
https://cloud.google.com/architecture/architecture-of-a-serverless-ml-model#architecture The architecture has the following flow:
A user writes a ticket to Firebase, which triggers a Cloud Function.
-The Cloud Function calls 3 different endpoints to enrich the ticket:
-An AI Platform endpoint, where the function can predict the priority.
-An AI Platform endpoint, where the function can predict the resolution time.
-The Natural Language API to do sentiment analysis and word salience.
-For each reply, the Cloud Function updates the Firebase real-time database.
-The Cloud Function then creates a ticket into the helpdesk platform using the RESTful API.

NEW QUESTION # 126
You are training a TensorFlow model on a structured data set with 100 billion records stored in several CSV files. You need to improve the input/output execution performance. What should you do?

A. Load the data into Cloud Bigtable, and read the data from BigtableB. Load the data into BigQuery and read the data from BigQuery.C. Convert the CSV files into shards of TFRecords, and store the data in the Hadoop Distributed File System (HDFS)D. Convert the CSV files into shards of TFRecords, and store the data in Cloud Storage

Answer: D

NEW QUESTION # 127
You trained a text classification model. You have the following SignatureDefs:

What is the correct way to write the predict request?

A. data = json.dumps({"signature_name": "serving_default, "instances": [['a', 'b\ 'c'1, [d\ 'e\ T]]})B. data = json dumps({"signature_name": "serving_default"! "instances": [['a', 'b', "c", 'd', 'e', 'f']]})C. data = json.dumps({"signature_name": "serving_default'\ "instances": [fab', 'be1, 'cd']]})D. data = json dumps({"signature_name": f,serving_default", "instances": [['a', 'b'], [c\ 'd'], ['e\ T]]})

Answer: D

Explanation:
https://stackoverflow.com/questions/37956197/what-is-the-negative-index-in-shape-arrays-used-for-tensorflow

NEW QUESTION # 128
......

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