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Google Professional Machine Learning Engineer Sample Questions (Q106-Q111):

NEW QUESTION # 106
Your team is building a convolutional neural network (CNN)-based architecture from scratch. The preliminary experiments running on your on-premises CPU-only infrastructure were encouraging, but have slow convergence. You have been asked to speed up model training to reduce time-to-market. You want to experiment with virtual machines (VMs) on Google Cloud to leverage more powerful hardware. Your code does not include any manual device placement and has not been wrapped in Estimator model-level abstraction. Which environment should you train your model on?

A. A Deep Learning VM with an n1-standard-2 machine and 1 GPU with all libraries pre-installed.
B. A Deep Learning VM with more powerful CPU e2-highcpu-16 machines with all libraries pre-installed.
C. AVM on Compute Engine and 1 TPU with all dependencies installed manually.
D. AVM on Compute Engine and 8 GPUs with all dependencies installed manually.

Answer: C

NEW QUESTION # 107
You are collaborating on a model prototype with your team. You need to create a Vertex Al Workbench environment for the members of your team and also limit access to other employees in your project. What should you do?

A. 1. Create a new service account and grant it the Notebook Viewer role.
2 Grant the Service Account User role to each team member on the service account.
3 Grant the Vertex Al User role to each team member.
4. Provision a Vertex Al Workbench user-managed notebook instance that uses the new service account.
B. 1. Grant the Vertex Al User role to the default Compute Engine service account.
2. Grant the Service Account User role to each team member on the default Compute Engine service account.
3. Provision a Vertex Al Workbench user-managed notebook instance that uses the default Compute Engine service account.
C. 1 Grant the Vertex Al User role to the primary team member.
2. Grant the Notebook Viewer role to the other team members.
3. Provision a Vertex Al Workbench user-managed notebook instance that uses the primary user's account.
D. 1 Create a new service account and grant it the Vertex Al User role.
2 Grant the Service Account User role to each team member on the service account.
3. Grant the Notebook Viewer role to each team member.
4 Provision a Vertex Al Workbench user-managed notebook instance that uses the new service account.

Answer: D

Explanation:
To create a Vertex AI Workbench environment for your team and limit access to other employees in your project, you should follow these steps:
Create a new service account and grant it the Vertex AI User role. This role grants full access to all resources in Vertex AI, including creating and managing notebook instances1.
Grant the Service Account User role to each team member on the service account. This role allows the team members to impersonate the service account and use its permissions2.
Grant the Notebook Viewer role to each team member. This role allows the team members to view and connect to the notebook instance, but not to modify or delete it3.
Provision a Vertex AI Workbench user-managed notebook instance that uses the new service account. This way, the notebook instance will run as the service account and only the team members who have the Service Account User and Notebook Viewer roles will be able to access it.
Reference:
1: Vertex AI access control with IAM | Google Cloud
2: Understanding service accounts | Cloud IAM Documentation
3: Manage access to a Vertex AI Workbench instance | Google Cloud
[4]: Create and manage Vertex AI Workbench instances | Google Cloud

NEW QUESTION # 108
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 the feature's mean.
B. Remove the rows with missing values, and upsample your dataset by 5%.
C. Replace the missing values with a placeholder category indicating a missing value.
D. Move the rows with missing values to your validation dataset.

Answer: C

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 # 109
Your team needs to build a model that predicts whether images contain a driver's license, passport, or credit card. The data engineering team already built the pipeline and generated a dataset composed of 10,000 images with driver's licenses, 1,000 images with passports, and 1,000 images with credit cards. You now have to train a model with the following label map: ['driversjicense', 'passport', 'credit_card']. Which loss function should you use?

A. Sparse categorical cross-entropy
B. Categorical cross-entropy
C. Binary cross-entropy
D. Categorical hinge

Answer: A

Explanation:
se sparse_categorical_crossentropy. Examples for above 3-class classification problem: [1] , [2], [3]

NEW QUESTION # 110
You need to analyze user activity data from your company's mobile applications. Your team will use BigQuery for data analysis, transformation, and experimentation with ML algorithms. You need to ensure real-time ingestion of the user activity data into BigQuery. What should you do?

A. Configure Pub/Sub and a Dataflow streaming job to ingest the data into BigQuery,
B. Run an Apache Spark streaming job on Dataproc to ingest the data into BigQuery.
C. Run a Dataflow streaming job to ingest the data into BigQuery.
D. Configure Pub/Sub to stream the data into BigQuery.

Answer: D

NEW QUESTION # 111
......

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Exam Professional-Machine-Learning-Engineer Papers, Valid Professional-Machine-Learning-Engineer Exam Test

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Google Professional Machine Learning Engineer Sample Questions (Q106-Q111):

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