Google passleader pdf Professional-machine-learning-engineer Study Guide by Alexia q58 vce pdf

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Exam Name:	Google Professional Machine Learning Engineer
Exam Code:	Professional-Machine-Learning-Engineer Dumps
Vendor:	Google	Certification:	Machine Learning Engineer
Questions:	270 Q&A's	Shared By:	alexia

Question 40

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?

Options:

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.

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.

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.

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.

Discussion

Question 41

You are training an object detection model using a Cloud TPU v2. Training time is taking longer than expected. Based on this simplified trace obtained with a Cloud TPU profile, what action should you take to decrease training time in a cost-efficient way?

Questions 41

Options:

Move from Cloud TPU v2 to Cloud TPU v3 and increase batch size.

Move from Cloud TPU v2 to 8 NVIDIA V100 GPUs and increase batch size.

Rewrite your input function to resize and reshape the input images.

Rewrite your input function using parallel reads, parallel processing, and prefetch.

Discussion

Question 42

You work for a retail company. You have been asked to develop a model to predict whether a customer will purchase a product on a given day. Your team has processed the company's sales data, and created a table with the following rows:

• Customer_id

• Product_id

• Date

• Days_since_last_purchase (measured in days)

• Average_purchase_frequency (measured in 1/days)

• Purchase (binary class, if customer purchased product on the Date)

You need to interpret your models results for each individual prediction. What should you do?

Options:

Create a BigQuery table Use BigQuery ML to build a boosted tree classifier Inspect the partition rules of the trees to understand how each prediction flows through the trees.

Create a Vertex Al tabular dataset Train an AutoML model to predict customer purchases Deploy the model

to a Vertex Al endpoint and enable feature attributions Use the "explain" method to get feature attribution values for each individual prediction.

Create a BigQuery table Use BigQuery ML to build a logistic regression classification model Use the values of the coefficients of the model to interpret the feature importance with higher values corresponding to more importance.

Create a Vertex Al tabular dataset Train an AutoML model to predict customer purchases Deploy the model to a Vertex Al endpoint. At each prediction enable L1 regularization to detect non-informative features.

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Question 43

You are building a MLOps platform to automate your company's ML experiments and model retraining. You need to organize the artifacts for dozens of pipelines How should you store the pipelines' artifacts'?

Options:

Store parameters in Cloud SQL and store the models' source code and binaries in GitHub

Store parameters in Cloud SQL store the models' source code in GitHub, and store the models' binaries in Cloud Storage.

Store parameters in Vertex ML Metadata store the models' source code in GitHub and store the models' binaries in Cloud Storage.

Store parameters in Vertex ML Metadata and store the models source code and binaries in GitHub.