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Question 141

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?

A.Move from Cloud TPU v2 to Cloud TPU v3 and increase batch size.
B.Move from Cloud TPU v2 to 8 NVIDIA V100 GPUs and increase batch size.
C.Rewrite your input function to resize and reshape the input images.
D.Rewrite your input function using parallel reads, parallel processing, and prefetch.

Question 142

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 = AI Platform, 2 = AI Platform, 3 = AutoML Vision
B.1 = AI Platform, 2 = AI Platform, 3 = AutoML Natural Language
C.1 = AI Platform, 2 = AI Platform, 3 = Cloud Natural Language API
D.1 = Cloud Natural Language API, 2 = AI Platform, 3 = Cloud Vision API

Question 143

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 144

While performing exploratory data analysis on a dataset, you find that an important categorical feature has 5% null values. You want to minimize the bias that could result from the missing values. How should you handle the missing values?

A.Remove the rows with missing values, and upsample your dataset by 5%.
B.Replace the missing values with the feature's mean.
C.Replace the missing values with a placeholder category indicating a missing value.
D.Move the rows with missing values to your validation dataset.

Correct Answer: C

The best option for handling missing values in a categorical feature is to replace them with a placeholder category indicating a missing value. This is a type of imputation, which is a method of estimating the missing values based on the observed data. Imputing the missing values with a placeholder category preserves the information that the data is missing, and avoids introducing bias or distortion in the feature distribution. It also allows the machine learning model to learn from the missingness pattern, and potentially use it as a predictor for the target variable. The other options are not suitable for handling missing values in a categorical feature, because:
* Removing the rows with missing values and upsampling the dataset by 5% would reduce the size of the dataset and potentially lose important information. It would also introduce sampling bias and overfitting, as the upsampling process would create duplicate or synthetic observations that do not reflect the true population.
* Replacing the missing values with the feature's mean would not make sense for a categorical feature, as the mean is a numerical measure that does not capture the mode or frequency of the categories. It would
* also create a new category that does not exist in the original data, and might confuse the machine learning model.
* Moving the rows with missing values to the validation dataset would compromise the validity and reliability of the model evaluation, as the validation dataset would not be representative of the test or production data. It would also reduce the amount of data available for training the model, and might introduce leakage or inconsistency between the training and validation datasets. References:
* Imputation of missing values
* Effective Strategies to Handle Missing Values in Data Analysis
* How to Handle Missing Values of Categorical Variables?
* Google Cloud launches machine learning engineer certification
* Google Professional Machine Learning Engineer Certification
* Professional ML Engineer Exam Guide
* Preparing for Google Cloud Certification: Machine Learning Engineer Professional Certificate

Question 145

You want to rebuild your ML pipeline for structured data on Google Cloud. You are using PySpark to conduct data transformations at scale, but your pipelines are taking over 12 hours to run. To speed up development and pipeline run time, you want to use a serverless tool and SQL syntax.
You have already moved your raw data into Cloud Storage. How should you build the pipeline on Google Cloud while meeting the speed and processing requirements?

A.Use Data Fusion's GUI to build the transformation pipelines, and then write the data into BigQuery.
B.Convert your PySpark into SparkSQL queries to transform the data, and then run your pipeline on Dataproc to write the data into BigQuery.
C.Ingest your data into Cloud SQL, convert your PySpark commands into SQL queries to transform the data, and then use federated queries from BigQuery for machine learning.
D.Ingest your data into BigQuery using BigQuery Load, convert your PySpark commands into BigQuery SQL queries to transform the data, and then write the transformations to a new table.