Free Access Google.Professional-Machine-Learning-Engineer.v2024-09-13.q242 Practice Test (Page 14)

Question 61

You work on the data science team at a manufacturing company. You are reviewing the company's historical sales data, which has hundreds of millions of records. For your exploratory data analysis, you need to calculate descriptive statistics such as mean, median, and mode; conduct complex statistical tests for hypothesis testing; and plot variations of the features over time You want to use as much of the sales data as possible in your analyses while minimizing computational resources. What should you do?

A.Spin up a Vertex Al Workbench user-managed notebooks instance and import the dataset Use this data to create statistical and visual analyses

B.Visualize the time plots in Google Data Studio. Import the dataset into Vertex Al Workbench user-managed notebooks Use this data to calculate the descriptive statistics and run the statistical analyses

C.Use BigQuery to calculate the descriptive statistics. Use Vertex Al Workbench user-managed notebooks to visualize the time plots and run the statistical analyses.

Question 62

A Machine Learning Specialist is building a model that will perform time series forecasting using Amazon SageMaker. The Specialist has finished training the model and is now planning to perform load testing on the endpoint so they can configure Auto Scaling for the model variant.
Which approach will allow the Specialist to review the latency, memory utilization, and CPU utilization during the load test?

A.Review SageMaker logs that have been written to Amazon S3 by leveraging Amazon Athena and Amazon QuickSight to visualize logs as they are being produced.

B.Generate an Amazon CloudWatch dashboard to create a single view for the latency, memory utilization, and CPU utilization metrics that are outputted by Amazon SageMaker.

C.Build custom Amazon CloudWatch Logs and then leverage Amazon ES and Kibana to query and visualize the log data as it is generated by Amazon SageMaker.

D.Send Amazon CloudWatch Logs that were generated by Amazon SageMaker to Amazon ES and use Kibana to query and visualize the log data.

Question 63

You are developing a recommendation engine for an online clothing store. The historical customer transaction data is stored in BigQuery and Cloud Storage. You need to perform exploratory data analysis (EDA), preprocessing and model training. You plan to rerun these EDA, preprocessing, and training steps as you experiment with different types of algorithms. You want to minimize the cost and development effort of running these steps as you experiment. How should you configure the environment?

A.Create a Vertex Al Workbench managed notebook to browse and query the tables directly from the JupyterLab interface.

B.Create a Vertex Al Workbench managed notebook on a Dataproc cluster, and use the spark-bigquery-connector to access the tables.

C.Create a Vertex Al Workbench user-managed notebook using the default VM instance, and use the %%bigquery magic commands in Jupyter to query the tables.

D.Create a Vertex Al Workbench user-managed notebook on a Dataproc Hub. and use the %%bigquery magic commands in Jupyter to query the tables.

Question 64

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 65

A data scientist has developed a machine learning translation model for English to Japanese by using Amazon SageMaker's built-in seq2seq algorithm with 500,000 aligned sentence pairs. While testing with sample sentences, the data scientist finds that the translation quality is reasonable for an example as short as five words. However, the quality becomes unacceptable if the sentence is 100 words long.
Which action will resolve the problem?

A.Choose a different weight initialization type.

B.Change preprocessing to use n-grams.

C.Adjust hyperparameters related to the attention mechanism.

D.Add more nodes to the recurrent neural network (RNN) than the largest sentence's word count.

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