Free Online Google Professional-Machine-Learning-Engineer Practice Test

Prepare Your Google Professional-Machine-Learning-Engineer Exam Questions with Free online Professional-Machine-Learning-Engineer Practice Test. Get Brilliant Professional Machine Learning Engineer Exam Results with Valid Professional Machine Learning Engineer Exam Dumps.

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Total 298 Questions | Updated On: Apr 24, 2024

Question 1

You trained a text classification model. You have the following SignatureDefs:

You started a TensorFlow-serving component server and tried to send an HTTP request to get a prediction using: headers = {"content-type": "application/json"} json_response = requests.post('http: //localhost:8501/v1/models/text_model:predict', data=data, headers=headers)

What is the correct way to write the predict request?

A : data = json.dumps({ג€signature_nameג€: ג€seving_defaultג€, ג€instancesג€ [['ab', 'bc', 'cd']]})

B : data = json.dumps({ג€signature_nameג€: ג€serving_defaultג€, ג€instancesג€ [['a', 'b', 'c', 'd', 'e', 'f']]})

C : data = json.dumps({ג€signature_nameג€: ג€serving_defaultג€, ג€instancesג€ [['a', 'b', 'c'], ['d', 'e', 'f']]})

D : data = json.dumps({ג€signature_nameג€: ג€serving_defaultג€, ג€instancesג€ [['a', 'b'], ['c', 'd'], ['e', 'f']]})

Answer: C

Question 2

You work for a bank. You have created a custom model to predict whether a loan application should be flagged for human review. The input features are stored in a BigQuery table. The model is performing well, and you plan to deploy it to production. Due to compliance requirements the model must provide explanations for each prediction. You want to add this functionality to your model code with minimal effort and provide explanations that are as accurate as possible. What should you do?

A : Create an AutoML tabular model by using the BigQuery data with integrated Vertex Explainable AI.

B : Create a BigQuery ML deep neural network model and use the ML.EXPLAIN_PREDICT method with the num_integral_steps parameter.

C : Upload the custom model to Vertex AI Model Registry and configure feature-based attribution by using sampled Shapley with input baselines.

D : Update the custom serving container to include sampled Shapley-based explanations in the prediction outputs.

Answer: B

Question 3

You are training an ML model on a large dataset. You are using a TPU to accelerate the training process You notice that the training process is taking longer than expected. You discover that the TPU is not reaching its full capacity. What should you do?

A : Increase the learning rate

B : Increase the number of epochs

C : Decrease the learning rate

D : Increase the batch size

Answer: D

Question 4

You work for a large social network service provider whose users post articles and discuss news. Millions of comments are posted online each day, and more than 200 human moderators constantly review comments and flag those that are inappropriate. Your team is building an ML model to help human moderators check content on the platform. The model scores each comment and flags suspicious comments to be reviewed by a human. Which metric(s) should you use to monitor the model’s performance?

A : Number of messages flagged by the model per minute

B : Number of messages flagged by the model per minute confirmed as being inappropriate by humans.

C : Precision and recall estimates based on a random sample of 0.1% of raw messages each minute sent to a human for review

D : Precision and recall estimates based on a sample of messages flagged by the model as potentially inappropriate each minute

Answer: B

Question 5

You work for a company that is developing an application to help users with meal planning You want to use machine learning to scan a corpus of recipes and extract each ingredient (e g carrot, rice pasta) and each kitchen cookware (e.g. bowl, pot spoon) mentioned Each recipe is saved in an unstructured text file What should you do?

A :

Create a text dataset on Vertex Al for entity extraction Create two entities called ingredient" and cookware" and label at least 200 examples of each entity Train an AutoML entity extraction model to extract occurrences of these entity types Evaluate performance on a holdout dataset.

B :

Create a multi-label text classification dataset on Vertex Al Create a test dataset and label each recipe that corresponds to its ingredients and cookware Train a multi-class classification model Evaluate the model’s performance on a holdout dataset.

C :

Use the Entity Analysis method of the Natural Language API to extract the ingredients and cookware from each recipe Evaluate the model's performance on a prelabeled dataset.

D :

Create a text dataset on Vertex Al for entity extraction Create as many entities as there are different ingredients and cookware Train an AutoML entity extraction model to extract those entities Evaluate the models performance on a holdout dataset.

Answer: A

Page: 1 / 60

Total 298 Questions | Updated On: Apr 24, 2024