Free Access Google.Professional-Machine-Learning-Engineer.v2025-05-29.q260 Practice Test (Page 30)

Question 141

You are building a model to predict daily temperatures. You split the data randomly and then transformed the training and test datasets. Temperature data for model training is uploaded hourly. During testing, your model performed with 97% accuracy; however, after deploying to production, the model's accuracy dropped to 66%. How can you make your production model more accurate?

A.Normalize the data for the training, and test datasets as two separate steps.

B.Split the training and test data based on time rather than a random split to avoid leakage

C.Add more data to your test set to ensure that you have a fair distribution and sample for testing

D.Apply data transformations before splitting, and cross-validate to make sure that the transformations are applied to both the training and test sets.

Question 142

You are an ML engineer at a global shoe store. You manage the ML models for the company's website. You are asked to build a model that will recommend new products to the user based on their purchase behavior and similarity with other users. What should you do?

A.Build a classification model

B.Build a knowledge-based filtering model

C.Build a collaborative-based filtering model

D.Build a regression model using the features as predictors

Correct Answer: C

A recommender system is a type of machine learning system that suggests relevant items to users based on their preferences and behavior. Recommender systems are widely used in e-commerce, media, and entertainment industries to enhance user experience and increase revenue1 There are different types of recommender systems that use different filtering methods to generate recommendations. The most common types are:
Content-based filtering: This method uses the features of the items and the users to find the similarity between them. For example, a content-based recommender system for movies may use the genre, director, cast, and ratings of the movies, and the preferences, demographics, and history of the users, to recommend movies that are similar to the ones the user liked before2 Collaborative filtering: This method uses the feedback and ratings of the users to find the similarity between them and the items. For example, a collaborative filtering recommender system for books may use the ratings of the users for different books, and recommend books that are liked by other users who have similar ratings to the target user3 Hybrid method: This method combines content-based and collaborative filtering methods to overcome the limitations of each method and improve the accuracy and diversity of the recommendations. For example, a hybrid recommender system for music may use both the features of the songs and the artists, and the ratings and listening habits of the users, to recommend songs that match the user's taste and preferences4 Deep learning-based: This method uses deep neural networks to learn complex and non-linear patterns from the data and generate recommendations. Deep learning-based recommender systems can handle large-scale and high-dimensional data, and incorporate various types of information, such as text, images, audio, and video. For example, a deep learning-based recommender system for fashion may use the images and descriptions of the products, and the profiles and feedback of the users, to recommend products that suit the user's style and preferences.
For the use case of building a model that will recommend new products to the user based on their purchase behavior and similarity with other users, the best option is to build a collaborative-based filtering model. This is because collaborative filtering can leverage the implicit feedback and ratings of the users to find the items that are most likely to interest them. Collaborative filtering can also help discover new products that the user may not be aware of, and increase the diversity and serendipity of the recommendations3 The other options are not as suitable for this use case. Building a classification model or a regression model using the features as predictors is not a good idea, as these models are not designed for recommendation tasks, and may not capture the preferences and behavior of the users. Building a knowledge-based filtering model is not relevant, as this method uses the explicit knowledge and requirements of the users to find the items that meet their criteria, and does not rely on the purchase behavior or similarity with other users.

Question 143

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 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.

C.Create a training job that uses Cloud TPU VMs Use tf.distribute.TPUStrategy for distribution.

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.

Question 144

You recently developed a deep learning model using Keras, and now you are experimenting with different training strategies. First, you trained the model using a single GPU, but the training process was too slow. Next, you distributed the training across 4 GPUs using tf.distribute.MirroredStrategy (with no other changes), but you did not observe a decrease in training time. What should you do?

A.Distribute the dataset with tf.distribute.Strategy.experimental_distribute_dataset

B.Create a custom training loop.

C.Use a TPU with tf.distribute.TPUStrategy.

D.Increase the batch size.

Question 145

You work for a manufacturing company. You need to train a custom image classification model to detect product defects at the end of an assembly line Although your model is performing well some images in your holdout set are consistently mislabeled with high confidence You want to use Vertex Al to understand your model's results What should you do?