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Google Professional-Machine-Learning-Engineer - Google Professional Machine Learning Engineer

You are going to train a DNN regression model with Keras APIs using this code:

How many trainable weights does your model have? (The arithmetic below is correct.)

A.

501*256+257*128+2 = 161154

B.

500*256+256*128+128*2 = 161024

C.

501*256+257*128+128*2=161408

D.

500*256*0 25+256*128*0 25+128*2 = 40448

You have built a custom model that performs several memory-intensive preprocessing tasks before it makes a prediction. You deployed the model to a Vertex Al endpoint. and validated that results were received in a reasonable amount of time After routing user traffic to the endpoint, you discover that the endpoint does not autoscale as expected when receiving multiple requests What should you do?

A.

Use a machine type with more memory

B.

Decrease the number of workers per machine

C.

Increase the CPU utilization target in the autoscaling configurations

D.

Decrease the CPU utilization target in the autoscaling configurations

You work on a growing team of more than 50 data scientists who all use Al Platform. You are designing a strategy to organize your jobs, models, and versions in a clean and scalable way. Which strategy should you choose?

A.

Set up restrictive I AM permissions on the Al Platform notebooks so that only a single user or group can access a given instance.

B.

Separate each data scientist's work into a different project to ensure that the jobs, models, and versions created by each data scientist are accessible only to that user.

C.

Use labels to organize resources into descriptive categories. Apply a label to each created resource so that users can filter the results by label when viewing or monitoring the resources

D.

Set up a BigQuery sink for Cloud Logging logs that is appropriately filtered to capture information about Al Platform resource usage In BigQuery create a SQL view that maps users to the resources they are using.

You have a custom job that runs on Vertex Al on a weekly basis The job is Implemented using a proprietary ML workflow that produces the datasets. models, and custom artifacts, and sends them to a Cloud Storage bucket Many different versions of the datasets and models were created Due to compliance requirements, your company needs to track which model was used for making a particular prediction, and needs access to the artifacts for each model. How should you configure your workflows to meet these requirement?

A.

Configure a TensorFlow Extended (TFX) ML Metadata database, and use the ML Metadata API.

B.

Create a Vertex Al experiment, and enable autologging inside the custom job

C.

Use the Vertex Al Metadata API inside the custom Job to create context, execution, and artifacts for each model, and use events to link them together.

D.

Register each model in Vertex Al Model Registry, and use model labels to store the related dataset and model information.

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.

D Use BigQuery to calculate the descriptive statistics, and use Google Data Studio to visualize the time plots. Use Vertex Al Workbench user-managed notebooks to run the statistical analyses.

You built a deep learning-based image classification model by using on-premises data. You want to use Vertex Al to deploy the model to production Due to security concerns you cannot move your data to the cloud. You are aware that the input data distribution might change over time You need to detect model performance changes in production. What should you do?

A.

Use Vertex Explainable Al for model explainability Configure feature-based explanations.

B.

Use Vertex Explainable Al for model explainability Configure example-based explanations.

C.

Create a Vertex Al Model Monitoring job. Enable training-serving skew detection for your model.

D.

Create a Vertex Al Model Monitoring job. Enable feature attribution skew and dnft detection for your model.

You are developing an ML model intended to classify whether X-Ray images indicate bone fracture risk. You have trained on Api Resnet architecture on Vertex AI using a TPU as an accelerator, however you are unsatisfied with the trainning time and use memory usage. You want to quickly iterate your training code but make minimal changes to the code. You also want to minimize impact on the models accuracy. What should you do?

A.

Configure your model to use bfloat16 instead float32

B.

Reduce the global batch size from 1024 to 256

C.

Reduce the number of layers in the model architecture

D.

Reduce the dimensions of the images used un the model

You have been asked to productionize a proof-of-concept ML model built using Keras. The model was trained in a Jupyter notebook on a data scientist’s local machine. The notebook contains a cell that performs data validation and a cell that performs model analysis. You need to orchestrate the steps contained in the notebook and automate the execution of these steps for weekly retraining. You expect much more training data in the future. You want your solution to take advantage of managed services while minimizing cost. What should you do?

A.

Move the Jupyter notebook to a Notebooks instance on the largest N2 machine type, and schedule the execution of the steps in the Notebooks instance using Cloud Scheduler.

B.

Write the code as a TensorFlow Extended (TFX) pipeline orchestrated with Vertex AI Pipelines. Use standard TFX components for data validation and model analysis, and use Vertex AI Pipelines for model retraining.

C.

Rewrite the steps in the Jupyter notebook as an Apache Spark job, and schedule the execution of the job on ephemeral Dataproc clusters using Cloud Scheduler.

D.

Extract the steps contained in the Jupyter notebook as Python scripts, wrap each script in an Apache Airflow BashOperator, and run the resulting directed acyclic graph (DAG) in Cloud Composer.

You recently deployed a scikit-learn model to a Vertex Al endpoint You are now testing the model on live production traffic While monitoring the endpoint. you discover twice as many requests per hour than expected throughout the day You want the endpoint to efficiently scale when the demand increases in the future to prevent users from experiencing high latency What should you do?

A.

Deploy two models to the same endpoint and distribute requests among them evenly.

B.

Configure an appropriate minReplicaCount value based on expected baseline traffic.

C.

Set the target utilization percentage in the autcscalir.gMetricspecs configuration to a higher value

D.

Change the model's machine type to one that utilizes GPUs.

You work with a team of researchers to develop state-of-the-art algorithms for financial analysis. Your team develops and debugs complex models in TensorFlow. You want to maintain the ease of debugging while also reducing the model training time. How should you set up your training environment?

A.

Configure a v3-8 TPU VM SSH into the VM to tram and debug the model.

B.

Configure a v3-8 TPU node Use Cloud Shell to SSH into the Host VM to train and debug the model.

C.

Configure a M-standard-4 VM with 4 NVIDIA P100 GPUs SSH into the VM and use

Parameter Server Strategy to train the model.

D.

Configure a M-standard-4 VM with 4 NVIDIA P100 GPUs SSH into the VM and use

MultiWorkerMirroredStrategy to train the model.