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Google Professional-Machine-Learning-Engineer Practice Test - The Key To Fast Exam

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Google Professional Machine Learning Engineer certification exam is divided into two sections: a multiple choice section and a practical section. The multiple choice section covers topics such as data preparation, feature engineering, model selection, and model evaluation. The practical section requires candidates to complete a set of tasks related to building, training, and deploying machine learning models using Google Cloud Platform.

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Preparation Process

The candidates for the Google Professional Machine Learning Engineer certification can find everything they need to efficiently prepare for the qualifying test on the official website. The most recommended resource offered by the vendor is the Machine Learning Engineer learning path. It contains both lessons and practical labs for a comprehensive understanding of the exam content. Moreover, the students can take advantage of the sample questions designed to help the potential test takers familiarize themselves with the possible exam questions. Finally, the applicants can opt for the Machine Learning Engineer Prep Webinar to join the Google experts and recently certified professionals for the tips and insights on the Machine Learning models, data processing systems, solution quality, and more.

Google Professional Machine Learning Engineer Sample Questions (Q121-Q126):

NEW QUESTION # 121
You work for a biotech startup that is experimenting with deep learning ML models based on properties of biological organisms. Your team frequently works on early-stage experiments with new architectures of ML models, and writes custom TensorFlow ops in C++. You train your models on large datasets and large batch sizes. Your typical batch size has 1024 examples, and each example is about 1 MB in size. The average size of a network with all weights and embeddings is 20 GB. What hardware should you choose for your models?

A. A cluster with 2 a2-megagpu-16g machines, each with 16 NVIDIA Tesla A100 GPUs (640 GB GPU memory in total), 96 vCPUs, and 1.4 TB RAM
B. A cluster with 4 n1-highcpu-96 machines, each with 96 vCPUs and 86 GB RAM
C. A cluster with 2 n1-highcpu-64 machines, each with 8 NVIDIA Tesla V100 GPUs (128 GB GPU memory in total), and a n1-highcpu-64 machine with 64 vCPUs and 58 GB RAM
D. A cluster with an n1-highcpu-64 machine with a v2-8 TPU and 64 GB RAM

Answer: A

NEW QUESTION # 122
A Machine Learning Specialist is using an Amazon SageMaker notebook instance in a private subnet of a corporate VPC. The ML Specialist has important data stored on the Amazon SageMaker notebook instance's Amazon EBS volume, and needs to take a snapshot of that EBS volume. However, the ML Specialist cannot find the Amazon SageMaker notebook instance's EBS volume or Amazon EC2 instance within the VPC.
Why is the ML Specialist not seeing the instance visible in the VPC?

A. Amazon SageMaker notebook instances are based on the EC2 instances within the customer account, but they run outside of VPCs.
B. Amazon SageMaker notebook instances are based on AWS ECS instances running within AWS service accounts.
C. Amazon SageMaker notebook instances are based on EC2 instances running within AWS service accounts.
D. Amazon SageMaker notebook instances are based on the Amazon ECS service within customer accounts.

Answer: C

Explanation:
Explanation/Reference: https://docs.aws.amazon.com/sagemaker/latest/dg/gs-setup-working-env.html

NEW QUESTION # 123
You work for a large retailer and you need to build a model to predict customer churn. The company has a dataset of historical customer data, including customer demographics, purchase history, and website activity.
You need to create the model in BigQuery ML and thoroughly evaluate its performance. What should you do?

A. Create a logistic regression model in BigQuery ML and register the model in Vertex Al Model Registry.
Evaluate the model performance in Vertex Al.
B. Create a linear regression model in BigQuery ML Use the ml. evaluate function to evaluate the model performance.
C. Create a linear regression model in BigQuery ML and register the model in Vertex Al Model Registry Evaluate the model performance in Vertex Al.
D. Create a logistic regression model in BigQuery ML Use the ml.confusion_matrix function to evaluate the model performance.

Answer: A

Explanation:
Customer churn is a binary classification problem, where the target variable is whether a customer has churned or not. Therefore, a logistic regression model is more suitable than a linear regression model, which is used for regression problems. A logistic regression model can output the probability of a customer churning, which can be used to rank the customers by their churn risk and take appropriate actions1.
BigQuery ML is a service that allows you to create and execute machine learning models in BigQuery using standard SQL queries2. You can use BigQuery ML to create a logistic regression model for customer churn prediction by using the CREATE MODEL statement and specifying the LOGISTIC_REG model type3. You can use the historical customer data as the input table for the model, and specify the features and the label columns3.
Vertex AI Model Registry is a central repository where you can manage the lifecycle of your ML models4. You can import models from various sources, such as BigQuery ML, AutoML, or custom models, and assign them to different versions and aliases4. You can also deploy models to endpoints, which are resources that provide a service URL for online prediction.
By registering the BigQuery ML model in Vertex AI Model Registry, you can leverage the Vertex AI features to evaluate and monitor the model performance4. You can use Vertex AI Experiments to track and compare the metrics of different model versions, such as accuracy, precision, recall, and AUC. You can also use Vertex AI Explainable AI to generate feature attributions that show how much each input feature contributed to the model's prediction.
The other options are not suitable for your scenario, because they either use the wrong model type, such as linear regression, or they do not use Vertex AI to evaluate the model performance, which would limit the insights and actions you can take based on the model results.
References:
* Logistic Regression for Machine Learning
* Introduction to BigQuery ML | Google Cloud
* Creating a logistic regression model | BigQuery ML | Google Cloud
* Introduction to Vertex AI Model Registry | Google Cloud
* [Deploy a model to an endpoint | Vertex AI | Google Cloud]
* [Vertex AI Experiments | Google Cloud]

NEW QUESTION # 124
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. Replace the missing values with a placeholder category indicating a missing value.
B. Replace the missing values with the feature's mean.
C. Remove the rows with missing values, and upsample your dataset by 5%.
D. Move the rows with missing values to your validation dataset.

Answer: A

Explanation:
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

NEW QUESTION # 125
A Data Scientist wants to gain real-time insights into a data stream of GZIP files.
Which solution would allow the use of SQL to query the stream with the LEAST latency?

A. AWS Glue with a custom ETL script to transform the data.
B. An Amazon Kinesis Client Library to transform the data and save it to an Amazon ES cluster.
C. Amazon Kinesis Data Firehose to transform the data and put it into an Amazon S3 bucket.
D. Amazon Kinesis Data Analytics with an AWS Lambda function to transform the data.

Answer: D

Explanation:
Explanation/Reference: https://aws.amazon.com/big-data/real-time-analytics-featured-partners/

NEW QUESTION # 126
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