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You want to train an AutoML model to predict house prices by using a small public dataset stored in BigQuery. You need to prepare the data and want to use the simplest most efficient approach. What should you do?
A. Write a query that preprocesses the data by using BigQuery and creates a new table Create a
Vertex Al managed dataset with the new table as the data source.
B. Use Dataflow to preprocess the data Write the output in TFRecord format to a Cloud Storage
bucket.
C. Write a query that preprocesses the data by using BigQuery Export the query results as CSV files
and use those files to create a Vertex Al managed dataset.
D. Use a Vertex Al Workbench notebook instance to preprocess the data by using the pandas library
Export the data as CSV files, and use those files to create a Vertex Al managed dataset.
You are training an ML model using data stored in BigQuery that contains several values that are considered Personally Identifiable Information (Pll). You need to reduce the sensitivity of the dataset before training your model. Every column is critical to your model. How should you proceed?
A. Using Dataflow, ingest the columns with sensitive data from BigQuery, and then randomize the
values in each sensitive column.
B. Use the Cloud Data Loss Prevention (DLP) API to scan for sensitive data, and use Dataflow with the
DLP API to encrypt sensitive values with Format Preserving Encryption
C. Use the Cloud Data Loss Prevention (DLP) API to scan for sensitive data, and use Dataflow to
replace all sensitive data by using the encryption algorithm AES-256 with a salt.
D. Before training, use BigQuery to select only the columns that do not contain sensitive data Create
an authorized view of the data so that sensitive values cannot be accessed by unauthorized
individuals.
You have trained a DNN regressor with TensorFlow to predict housing prices using a set of predictive features. Your default precision is tf.float64, and you use a standard TensorFlow estimator; estimator = tf.estimator.DNNRegressor( feature_columns=[YOUR_LIST_OF_FEATURES], hidden_units-[1024, 512, 256], dropout=None) Your model performs well, but Just before deploying it to production, you discover that your current serving latency is 10ms @ 90 percentile and you currently serve on CPUs. Your production requirements expect a model latency of 8ms @ 90 percentile. You are willing to accept a small decrease in performance in order to reach the latency requirement Therefore your plan is to improve latency while evaluating how much the model's prediction decreases. What should you first try to quickly lower the serving latency?
A. Increase the dropout rate to 0.8 in_PREDICT mode by adjusting the TensorFlow Serving
parameters
B. Increase the dropout rate to 0.8 and retrain your model.
C. Switch from CPU to GPU serving
D. Apply quantization to your SavedModel by reducing the floating point precision to tf.float16.
You developed a Vertex Al ML pipeline that consists of preprocessing and training steps and each setof steps runs on a separate custom Docker image Your organization uses GitHub and GitHub Actionsas CI/CD to run unit and integration tests You need to automate the model retraining workflow sothat it can be initiated both manually and when a new version of the code is merged in the mainbranch You want to minimize the steps required to build the workflow while also allowing formaximum flexibility How should you configure the CI/CD workflow?
A. Trigger a Cloud Build workflow to run tests build custom Docker images, push the images toArtifact Registry and launch the pipeline in Vertex Al Pipelines.
B. Trigger GitHub Actions to run the tests launch a job on Cloud Run to build custom Docker imagespush the images to Artifact Registry and launch the pipeline in Vertex Al Pipelines.
C. Trigger GitHub Actions to run the tests build custom Docker images push the images to ArtifactRegistry, and launch the pipeline in Vertex Al Pipelines.
D. Trigger GitHub Actions to run the tests launch a Cloud Build workflow to build custom Dickerimages, push the images to Artifact Registry, and launch the pipeline in Vertex Al Pipelines.
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 usermanaged
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.
Your organization manages an online message board A few months ago, you discovered an increase in toxic language and bullying on the message board. You deployed an automated text classifier that flags certain comments as toxic or harmful. Now some users are reporting that benign comments referencing their religion are being misclassified as abusive Upon further inspection, you find that your classifier's false positive rate is higher for comments that reference certain underrepresented religious groups. Your team has a limited budget and is already overextended. What should you do?
A. Add synthetic training data where those phrases are used in non-toxic ways
B. Remove the model and replace it with human moderation.
C. Replace your model with a different text classifier.
D. Raise the threshold for comments to be considered toxic or harmful
You are working with a dataset that contains customer transactions. You need to build an ML modelto predict customer purchase behavior You plan to develop the model in BigQuery ML, and export itto Cloud Storage for online prediction You notice that the input data contains a few categoricalfeatures, including product category and payment method You want to deploy the model as quicklyas possible. What should you do?
A. Use the transform clause with the ML. ONE_HOT_ENCODER function on the categorical features atmodel creation and select the categorical and non-categorical features.
B. Use the ML. ONE_HOT_ENCODER function on the categorical features, and select the encodedcategorical features and non-categorical features as inputs to create your model.
C. Use the create model statement and select the categorical and non-categorical features.
D. Use the ML. ONE_HOT_ENCODER function on the categorical features, and select the encodedcategorical features and non-categorical features as inputs to create your model.
You are an ML engineer at a manufacturing company You are creating a classification model for a predictive maintenance use case You need to predict whether a crucial machine will fail in the next three days so that the repair crew has enough time to fix the machine before it breaks. Regular maintenance of the machine is relatively inexpensive, but a failure would be very costly You have trained several binary classifiers to predict whether the machine will fail. where a prediction of 1 means that the ML model predicts a failure. You are now evaluating each model on an evaluation dataset. You want to choose a model that prioritizes detection while ensuring that more than 50% of the maintenance jobs triggered by your model address an imminent machine failure. Which model should you choose?
A. The model with the highest area under the receiver operating characteristic curve (AUC ROC) and
precision greater than 0 5
B. The model with the lowest root mean squared error (RMSE) and recall greater than 0.5.
C. The model with the highest recall where precision is greater than 0.5.
D. The model with the highest precision where recall is greater than 0.5.
You need to develop an image classification model by using a large dataset that contains labeledimages in a Cloud Storage Bucket. What should you do?
A. Use Vertex Al Pipelines with the Kubeflow Pipelines SDK to create a pipeline that reads the imagesfrom Cloud Storage and trains the model.
B. Use Vertex Al Pipelines with TensorFlow Extended (TFX) to create a pipeline that reads the imagesfrom Cloud Storage and trams the model.
C. Import the labeled images as a managed dataset in Vertex Al: and use AutoML to tram the model.
D. Convert the image dataset to a tabular format using Dataflow Load the data into BigQuery and useBigQuery ML to tram the model.
You are developing an image recognition model using PyTorch based on ResNet50 architecture. Your code is working fine on your local laptop on a small subsample. Your full dataset has 200k labeled images You want to quickly scale your training workload while minimizing cost. You plan to use 4 V100 GPUs. What should you do? (Choose Correct Answer and Give Reference and Explanation)
A. Configure a Compute Engine VM with all the dependencies that launches the training Train your
model with Vertex Al using a custom tier that contains the required GPUs
B. Package your code with Setuptools. and use a pre-built container Train your model with Vertex Al
using a custom tier that contains the required GPUs
C. Create a Vertex Al Workbench user-managed notebooks instance with 4 V100 GPUs, and use it to
train your model
D. Create a Google Kubernetes Engine cluster with a node pool that has 4 V100 GPUs Prepare and
submit a TFJob operator to this node pool.
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