Quiz IT certification exam

Question 1

You work for a gaming company that develops massively multiplayer online (MMO) games. You built a TensorFlow model that predicts whether players will make in-app purchases of more than $10 in the next two weeks. The model's predictions will be used to adapt each user's game experience. User data is stored in BigQuery. How should you serve your model while optimizing cost, user experience, and ease of management?

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Accepted Answer

Deploy the model to Vertex AI Prediction. Make predictions using batch reading data from Cloud Bigtable, and push the data to Cloud SQL.

Answer

Import the model into BigQuery ML. Make predictions using batch reading data from BigQuery, and push the data to Cloud SQL

Answer

Embed the model in the mobile application. Make predictions after every in-app purchase event is published in Pub/Sub, and push the data to Cloud SQL.

Answer

Embed the model in the streaming Dataflow pipeline. Make predictions after every in-app purchase event is published in Pub/Sub, and push the data to Cloud SQL.

Question 2

You manage a team of data scientists who use a cloud-based backend system to submit training jobs. This system has become very difficult to administer, and you want to use a managed service instead. The data scientists you work with use many different frameworks, including Keras, PyTorch, theano, scikit-learn, and custom libraries. What should you do?

Accepted Answer

Use the Vertex AI Training to submit training jobs using any framework.

Answer

Configure Kubeflow to run on Google Kubernetes Engine and submit training jobs through TFJob.

Answer

Create a library of VM images on Compute Engine, and publish these images on a centralized repository.

Answer

Set up Slurm workload manager to receive jobs that can be scheduled to run on your cloud infrastructure.

Question 3

You are training an object detection model using a Cloud TPU v2. Training time is taking longer than expected. Based on this simplified trace obtained with a Cloud TPU profile, what action should you take to decrease training time in a cost-efficient way?<img alt="Google Professional Machine Learning Engineer image Question 123 30288 09182024191449000000"   width="870" height="375" src="https://examgecko.com/getfile/6e33649d-c54e-459b-84e6-f27a77170b71">

Accepted Answer

Rewrite your input function using parallel reads, parallel processing, and prefetch.

Answer

Move from Cloud TPU v2 to Cloud TPU v3 and increase batch size.

Answer

Move from Cloud TPU v2 to 8 NVIDIA V100 GPUs and increase batch size.

Answer

Rewrite your input function to resize and reshape the input images.

Question 4

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?

Accepted Answer

Replace the missing values with a placeholder category indicating a missing value.

Answer

Remove the rows with missing values, and upsample your dataset by 5%.

Answer

Replace the missing values with the feature's mean.

Answer

Move the rows with missing values to your validation dataset.

Question 5

You are an ML engineer on an agricultural research team working on a crop disease detection tool to detect leaf rust spots in images of crops to determine the presence of a disease. These spots, which can vary in shape and size, are correlated to the severity of the disease. You want to develop a solution that predicts the presence and severity of the disease with high accuracy. What should you do?

Accepted Answer

Develop an image segmentation ML model to locate the boundaries of the rust spots.

Answer

Create an object detection model that can localize the rust spots.

Answer

Develop a template matching algorithm using traditional computer vision libraries.

Answer

Develop an image classification ML model to predict the presence of the disease.

Question 6

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?

Accepted Answer

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.

Answer

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.

Answer

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.

Answer

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.

Question 7

You are working on a system log anomaly detection model for a cybersecurity organization. You have developed the model using TensorFlow, and you plan to use it for real-time prediction. You need to create a Dataflow pipeline to ingest data via Pub/Sub and write the results to BigQuery. You want to minimize the serving latency as much as possible. What should you do?

Accepted Answer

Load the model directly into the Dataflow job as a dependency, and use it for prediction.

Answer

Containerize the model prediction logic in Cloud Run, which is invoked by Dataflow.

Answer

Deploy the model to a Vertex AI endpoint, and invoke this endpoint in the Dataflow job.

Answer

Deploy the model in a TFServing container on Google Kubernetes Engine, and invoke it in the Dataflow job.

Question 8

You are an ML engineer at a mobile gaming company. A data scientist on your team recently trained a TensorFlow model, and you are responsible for deploying this model into a mobile application. You discover that the inference latency of the current model doesn't meet production requirements. You need to reduce the inference time by 50%, and you are willing to accept a small decrease in model accuracy in order to reach the latency requirement. Without training a new model, which model optimization technique for reducing latency should you try first?

Accepted Answer

Dynamic range quantization

Answer

Weight pruning

Answer

Model distillation

Answer

Dimensionality reduction

Question 9

You work on a data science team at a bank and are creating an ML model to predict loan default risk. You have collected and cleaned hundreds of millions of records worth of training data in a BigQuery table, and you now want to develop and compare multiple models on this data using TensorFlow and Vertex AI. You want to minimize any bottlenecks during the data ingestion state while considering scalability. What should you do?

Accepted Answer

Use TensorFlow I/O's BigQuery Reader to directly read the data.

Answer

Use the BigQuery client library to load data into a dataframe, and use tf.data.Dataset.from_tensor_slices() to read it.

Answer

Export data to CSV files in Cloud Storage, and use tf.data.TextLineDataset() to read them.

Answer

Convert the data into TFRecords, and use tf.data.TFRecordDataset() to read them.

Question 10

You have recently created a proof-of-concept (POC) deep learning model. You are satisfied with the overall architecture, but you need to determine the value for a couple of hyperparameters. You want to perform hyperparameter tuning on Vertex AI to determine both the appropriate embedding dimension for a categorical feature used by your model and the optimal learning rate. You configure the following settings:For the embedding dimension, you set the type to INTEGER with a minValue of 16 and maxValue of 64.For the learning rate, you set the type to DOUBLE with a minValue of 10e-05 and maxValue of 10e-02.You are using the default Bayesian optimization tuning algorithm, and you want to maximize model accuracy. Training time is not a concern. How should you set the hyperparameter scaling for each hyperparameter and the maxParallelTrials?

Accepted Answer

Use UNIT_LINEAR_SCALE for the embedding dimension, UNIT_LOG_SCALE for the learning rate, and a large number of parallel trials.

Answer

Use UNIT_LINEAR_SCALE for the embedding dimension, UNIT_LOG_SCALE for the learning rate, and a small number of parallel trials.

Answer

Use UNIT_LOG_SCALE for the embedding dimension, UNIT_LINEAR_SCALE for the learning rate, and a large number of parallel trials.

Answer

Use UNIT_LOG_SCALE for the embedding dimension, UNIT_LINEAR_SCALE for the learning rate, and a small number of parallel trials.

Question 11

You are the Director of Data Science at a large company, and your Data Science team has recently begun using the Kubeflow Pipelines SDK to orchestrate their training pipelines. Your team is struggling to integrate their custom Python code into the Kubeflow Pipelines SDK. How should you instruct them to proceed in order to quickly integrate their code with the Kubeflow Pipelines SDK?

Accepted Answer

Use the func_to_container_op function to create custom components from the Python code.

Answer

Use the predefined components available in the Kubeflow Pipelines SDK to access Dataproc, and run the custom code there.

Answer

Package the custom Python code into Docker containers, and use the load_component_from_file function to import the containers into the pipeline.

Answer

Deploy the custom Python code to Cloud Functions, and use Kubeflow Pipelines to trigger the Cloud Function.

Question 12

You work for the AI team of an automobile company, and you are developing a visual defect detection model using TensorFlow and Keras. To improve your model performance, you want to incorporate some image augmentation functions such as translation, cropping, and contrast tweaking. You randomly apply these functions to each training batch. You want to optimize your data processing pipeline for run time and compute resources utilization. What should you do?

Accepted Answer

Embed the augmentation functions dynamically in the tf.Data pipeline.

Answer

Embed the augmentation functions dynamically as part of Keras generators.

Answer

Use Dataflow to create all possible augmentations, and store them as TFRecords.

Answer

Use Dataflow to create the augmentations dynamically per training run, and stage them as TFRecords.

Question 13

You work for an online publisher that delivers news articles to over 50million readers. You have built an AI model that recommends content for the company's weekly newsletter. A recommendation is considered successful if the article is opened within two days of the newsletter's published date and the user remains on the page for at least one minute.All the information needed to compute the success metric is available in BigQuery and is updated hourly. The model is trained on eight weeks of data, on average its performance degrades below the acceptable baseline after five weeks, and training time is 12hours. You want to ensure that the model's performance is above the acceptable baseline while minimizing cost. How should you monitor the model to determine when retraining is necessary?

Accepted Answer

Schedule a weekly query in BigQuery to compute the success metric.

Answer

Use Vertex AI Model Monitoring to detect skew of the input features with a sample rate of 100% and a monitoring frequency of two days.

Answer

Schedule a cron job in Cloud Tasks to retrain the model every week before the newsletter is created.

Answer

Schedule a daily Dataflow job in Cloud Composer to compute the success metric.

Question 14

You deployed an ML model into production a year ago. Every month, you collect all raw requests that were sent to your model prediction service during the previous month. You send a subset of these requests to a human labeling service to evaluate your model's performance. After a year, you notice that your model's performance sometimes degrades significantly after a month, while other times it takes several months to notice any decrease in performance. The labeling service is costly, but you also need to avoid large performance degradations. You want to determine how often you should retrain your model to maintain a high level of performance while minimizing cost. What should you do?

Accepted Answer

Run training-serving skew detection batch jobs every few days to compare the aggregate statistics of the features in the training dataset with recent serving data. If skew is detected, send the most recent serving data to the labeling service.

Answer

Train an anomaly detection model on the training dataset, and run all incoming requests through this model. If an anomaly is detected, send the most recent serving data to the labeling service.

Answer

Identify temporal patterns in your model's performance over the previous year. Based on these patterns, create a schedule for sending serving data to the labeling service for the next year.

Answer

Compare the cost of the labeling service with the lost revenue due to model performance degradation over the past year. If the lost revenue is greater than the cost of the labeling service, increase the frequency of model retraining; otherwise, decrease the model retraining frequency.

Question 15

You work for a company that manages a ticketing platform for a large chain of cinemas. Customers use a mobile app to search for movies they're interested in and purchase tickets in the app. Ticket purchase requests are sent to Pub/Sub and are processed with a Dataflow streaming pipeline configured to conduct the following steps:1. Check for availability of the movie tickets at the selected cinema.2. Assign the ticket price and accept payment.3. Reserve the tickets at the selected cinema.4. Send successful purchases to your database.Each step in this process has low latency requirements (less than 50milliseconds). You have developed a logistic regression model with BigQuery ML that predicts whether offering a promo code for free popcorn increases the chance of a ticket purchase, and this prediction should be added to the ticket purchase process. You want to identify the simplest way to deploy this model to production while adding minimal latency. What should you do?

Accepted Answer

Export your model in TensorFlow format, and add a tfx_bsl.public.beam.RunInference step to the Dataflow pipeline.

Answer

Run batch inference with BigQuery ML every five minutes on each new set of tickets issued.

Answer

Export your model in TensorFlow format, deploy it on Vertex AI, and query the prediction endpoint from your streaming pipeline.

Answer

Convert your model with TensorFlow Lite (TFLite), and add it to the mobile app so that the promo code and the incoming request arrive together in Pub/Sub.

Question 16

You work for a retailer that sells clothes to customers around the world. You have been tasked with ensuring that ML models are built in a secure manner. Specifically, you need to protect sensitive customer data that might be used in the models. You have identified four fields containing sensitive data that are being used by your data science team: AGE, IS_EXISTING_CUSTOMER, LATITUDE_LONGITUDE, and SHIRT_SIZE. What should you do with the data before it is made available to the data science team for training purposes?

Accepted Answer

Coarsen the data by putting AGE into quantiles and rounding LATITUDE_LONGTTUDE into single precision. The other two fields are already as coarse as possible.

Answer

Tokenize all of the fields using hashed dummy values to replace the real values.

Answer

Use principal component analysis (PCA) to reduce the four sensitive fields to one PCA vector.

Answer

Remove all sensitive data fields, and ask the data science team to build their models using non-sensitive data.

Question 17

You work for a magazine publisher and have been tasked with predicting whether customers will cancel their annual subscription. In your exploratory data analysis, you find that 90% of individuals renew their subscription every year, and only 10% of individuals cancel their subscription. After training a NN Classifier, your model predicts those who cancel their subscription with 99% accuracy and predicts those who renew their subscription with 82% accuracy. How should you interpret these results?

Accepted Answer

This is not a good result because the model is performing worse than predicting that people will always renew their subscription.

Answer

This is not a good result because the model should have a higher accuracy for those who renew their subscription than for those who cancel their subscription.

Answer

This is a good result because predicting those who cancel their subscription is more difficult, since there is less data for this group.

Answer

This is a good result because the accuracy across both groups is greater than 80%.

Question 18

You have built a model that is trained on data stored in Parquet files. You access the data through a Hive table hosted on Google Cloud. You preprocessed these data with PySpark and exported it as a CSV file into Cloud Storage. After preprocessing, you execute additional steps to train and evaluate your model. You want to parametrize this model training in Kubeflow Pipelines. What should you do?

Accepted Answer

Add a ContainerOp to your pipeline that spins a Dataproc cluster, runs a transformation, and then saves the transformed data in Cloud Storage.

Answer

Remove the data transformation step from your pipeline.

Answer

Containerize the PySpark transformation step, and add it to your pipeline.

Answer

Deploy Apache Spark at a separate node pool in a Google Kubernetes Engine cluster. Add a ContainerOp to your pipeline that invokes a corresponding transformation job for this Spark instance.

Question 19

You are developing an ML model using a dataset with categorical input variables. You have randomly split half of the data into training and test sets. After applying one-hot encoding on the categorical variables in the training set, you discover that one categorical variable is missing from the test set. What should you do?

Accepted Answer

Apply one-hot encoding on the categorical variables in the test data.

Answer

Randomly redistribute the data, with 70% for the training set and 30% for the test set

Answer

Use sparse representation in the test set

Answer

Collect more data representing all categories

Question 20

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)

Accepted Answer

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.

Answer

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.

Answer

Create a Vertex Al Workbench user-managed notebooks instance with 4 V100 GPUs, and use it to train your model

Answer

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.

Question 21

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?

Accepted Answer

The model with the highest recall where precision is greater than 0.5.

Answer

The model with the highest area under the receiver operating characteristic curve (AUC ROC) and precision greater than 0 5

Answer

The model with the lowest root mean squared error (RMSE) and recall greater than 0.5.

Answer

The model with the highest precision where recall is greater than 0.5.

Question 22

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?

Accepted Answer

Add synthetic training data where those phrases are used in non-toxic ways

Answer

Remove the model and replace it with human moderation.

Answer

Replace your model with a different text classifier.

Answer

Raise the threshold for comments to be considered toxic or harmful

Question 23

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?

Accepted Answer

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

Answer

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

Answer

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

Answer

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.

Question 24

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?

Accepted Answer

Apply quantization to your SavedModel by reducing the floating point precision to tf.float16.

Answer

Increase the dropout rate to 0.8 in_PREDICT mode by adjusting the TensorFlow Serving parameters

Answer

Increase the dropout rate to 0.8 and retrain your model.

Answer

Switch from CPU to GPU serving

Question 25

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?

Accepted Answer

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

Answer

Using Dataflow, ingest the columns with sensitive data from BigQuery, and then randomize the values in each sensitive column.

Answer

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.

Answer

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.

Question 26

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?

Accepted Answer

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.

Answer

Use Dataflow to preprocess the data Write the output in TFRecord format to a Cloud Storage bucket.

Answer

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.

Answer

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.

Question 27

You developed a Vertex Al ML pipeline that consists of preprocessing and training steps and each set of steps runs on a separate custom Docker image Your organization uses GitHub and GitHub Actions as CI/CD to run unit and integration tests You need to automate the model retraining workflow so that it can be initiated both manually and when a new version of the code is merged in the main branch You want to minimize the steps required to build the workflow while also allowing for maximum flexibility How should you configure the CI/CD workflow?

Accepted Answer

Trigger GitHub Actions to run the tests launch a Cloud Build workflow to build custom Dicker images, push the images to Artifact Registry, and launch the pipeline in Vertex Al Pipelines.

Answer

Trigger a Cloud Build workflow to run tests build custom Docker images, push the images to Artifact Registry and launch the pipeline in Vertex Al Pipelines.

Answer

Trigger GitHub Actions to run the tests launch a job on Cloud Run to build custom Docker images push the images to Artifact Registry and launch the pipeline in Vertex Al Pipelines.

Answer

Trigger GitHub Actions to run the tests build custom Docker images push the images to Artifact Registry, and launch the pipeline in Vertex Al Pipelines.

Question 28

You are working with a dataset that contains customer transactions. You need to build an ML model to predict customer purchase behavior You plan to develop the model in BigQuery ML, and export it to Cloud Storage for online prediction You notice that the input data contains a few categorical features, including product category and payment method You want to deploy the model as quickly as possible. What should you do?

Accepted Answer

Use the transform clause with the ML. ONE_HOT_ENCODER function on the categorical features at model creation and select the categorical and non-categorical features.

Answer

Use the ML. ONE_HOT_ENCODER function on the categorical features, and select the encoded categorical features and non-categorical features as inputs to create your model.

Answer

Use the create model statement and select the categorical and non-categorical features.

Answer

Use the ML. ONE_HOT_ENCODER function on the categorical features, and select the encoded categorical features and non-categorical features as inputs to create your model.

Question 29

You need to develop an image classification model by using a large dataset that contains labeled images in a Cloud Storage Bucket. What should you do?

Accepted Answer

Import the labeled images as a managed dataset in Vertex Al: and use AutoML to tram the model.

Answer

Use Vertex Al Pipelines with the Kubeflow Pipelines SDK to create a pipeline that reads the images from Cloud Storage and trains the model.

Answer

Use Vertex Al Pipelines with TensorFlow Extended (TFX) to create a pipeline that reads the images from Cloud Storage and trams the model.

Answer

Convert the image dataset to a tabular format using Dataflow Load the data into BigQuery and use BigQuery ML to tram the model.

Question 30

You are developing a mode! to detect fraudulent credit card transactions. You need to prioritize detection because missing even one fraudulent transaction could severely impact the credit card holder. You used AutoML to tram a model on users' profile information and credit card transaction data. After training the initial model, you notice that the model is failing to detect many fraudulent transactions. How should you adjust the training parameters in AutoML to improve model performance?Choose 2 answers

Accepted Answer

Decrease the score threshold.

Accepted Answer

Add more positive examples to the training set.

Answer

Increase the score threshold.

Answer

Add more negative examples to the training set.

Answer

Reduce the maximum number of node hours for training.

Question 31

You need to deploy a scikit-learn classification model to production. The model must be able to serve requests 24/7 and you expect millions of requests per second to the production application from 8 am to 7 pm. You need to minimize the cost of deployment What should you do?

Accepted Answer

Deploy an online Vertex Al prediction endpoint Set the max replica count to 100

Answer

Deploy an online Vertex Al prediction endpoint Set the max replica count to 1

Answer

Deploy an online Vertex Al prediction endpoint with one GPU per replica Set the max replica count to 1.

Answer

Deploy an online Vertex Al prediction endpoint with one GPU per replica Set the max replica count to 100.

Question 32

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?

Accepted Answer

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

Answer

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

Answer

Configure a M-standard-4 VM with 4 NVIDIA P100 GPUs SSH into the VM and use Parameter Server Strategy to train the model.

Answer

Configure a M-standard-4 VM with 4 NVIDIA P100 GPUs SSH into the VM and use MultiWorkerMirroredStrategy to train the model.

Question 33

You are training an ML model on a large dataset. You are using a TPU to accelerate the training process You notice that the training process is taking longer than expected. You discover that the TPU is not reaching its full capacity. What should you do?

Accepted Answer

Increase the batch size

Answer

Increase the learning rate

Answer

Increase the number of epochs

Answer

Decrease the learning rate

Question 34

You work for a retail company. You have a managed tabular dataset in Vertex Al that contains sales data from three different stores. The dataset includes several features such as store name and sale timestamp. You want to use the data to train a model that makes sales predictions for a new store that will open soon You need to split the data between the training, validation, and test sets What approach should you use to split the data?

Accepted Answer

Use Vertex Al default data split.

Answer

Use Vertex Al manual split, using the store name feature to assign one store for each set.

Answer

Use Vertex Al chronological split and specify the sales timestamp feature as the time vanable.

Answer

Use Vertex Al random split assigning 70% of the rows to the training set, 10% to the validation set, and 20% to the test set.

Question 35

You have developed a BigQuery ML model that predicts customer churn and deployed the model to Vertex Al Endpoints. You want to automate the retraining of your model by using minimal additional code when model feature values change. You also want to minimize the number of times that your model is retrained to reduce training costs. What should you do?

Accepted Answer

1 Create a Vertex Al Model Monitoring job configured to monitor prediction drift. 2. Configure alert monitoring to publish a message to a Pub/Sub queue when a monitonng alert is detected. 3. Use a Cloud Function to monitor the Pub/Sub queue, and trigger retraining in BigQuery

Answer

1. Enable request-response logging on Vertex Al Endpoints. 2 Schedule a TensorFlow Data Validation job to monitor prediction drift 3. Execute model retraining if there is significant distance between the distributions.

Answer

1. Enable request-response logging on Vertex Al Endpoints 2. Schedule a TensorFlow Data Validation job to monitor training/serving skew 3. Execute model retraining if there is significant distance between the distributions

Answer

1. Create a Vertex Al Model Monitoring job configured to monitor training/serving skew 2. Configure alert monitoring to publish a message to a Pub/Sub queue when a monitoring alert is detected 3. Use a Cloud Function to monitor the Pub/Sub queue, and trigger retraining in BigQuery.

Question 36

You have been tasked with deploying prototype code to production. The feature engineering code is in PySpark and runs on Dataproc Serverless. The model training is executed by using a Vertex Al custom training job. The two steps are not connected, and the model training must currently be run manually after the feature engineering step finishes. You need to create a scalable and maintainable production process that runs end-to-end and tracks the connections between steps. What should you do?

Accepted Answer

Use the Kubeflow pipelines SDK to write code that specifies two components - The first is a Dataproc Serverless component that launches the feature engineering job - The second is a custom component wrapped in the creare_cusrora_rraining_job_from_ccraponent Utility that launches the custom model training job.

Answer

Create a Vertex Al Workbench notebook Use the notebook to submit the Dataproc Serverless feature engineering job Use the same notebook to submit the custom model training job Run the notebook cells sequentially to tie the steps together end-to-end

Answer

Create a Vertex Al Workbench notebook Initiate an Apache Spark context in the notebook, and run the PySpark feature engineering code Use the same notebook to run the custom model training job in TensorFlow Run the notebook cells sequentially to tie the steps together end-to-end

Answer

Create a Vertex Al Pipelines job to link and run both components Use the Kubeflow pipelines SDK to write code that specifies two components - The first component initiates an Apache Spark context that runs the PySpark feature engineering code - The second component runs the TensorFlow custom model training code Create a Vertex Al Pipelines job to link and run both components

Question 37

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?

Accepted Answer

Configure an appropriate minReplicaCount value based on expected baseline traffic.

Answer

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

Answer

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

Answer

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

Question 38

You work at a bank You have a custom tabular ML model that was provided by the bank's vendor. The training data is not available due to its sensitivity. The model is packaged as a Vertex Al Model serving container which accepts a string as input for each prediction instance. In each string the feature values are separated by commas. You want to deploy this model to production for online predictions, and monitor the feature distribution over time with minimal effort What should you do?

Accepted Answer

1 Upload the model to Vertex Al Model Registry and deploy the model to a Vertex Ai endpoint. 2. Create a Vertex Al Model Monitoring job with feature drift detection as the monitoring objective, and provide an instance schema.

Answer

1 Upload the model to Vertex Al Model Registry and deploy the model to a Vertex Al endpoint. 2 Create a Vertex Al Model Monitoring job with feature skew detection as the monitoring objective and provide an instance schema.

Answer

1 Refactor the serving container to accept key-value pairs as input format. 2. Upload the model to Vertex Al Model Registry and deploy the model to a Vertex Al endpoint. 3. Create a Vertex Al Model Monitoring job with feature drift detection as the monitoring objective.

Answer

1 Refactor the serving container to accept key-value pairs as input format. 2 Upload the model to Vertex Al Model Registry and deploy the model to a Vertex Al endpoint. 3. Create a Vertex Al Model Monitoring job with feature skew detection as the monitoring objective.

Question 39

You are implementing a batch inference ML pipeline in Google Cloud. The model was developed using TensorFlow and is stored in SavedModel format in Cloud Storage You need to apply the model to a historical dataset containing 10 TB of data that is stored in a BigQuery table How should you perform the inference?

Accepted Answer

Configure a Vertex Al batch prediction job to apply the model to the historical data in BigQuery

Answer

Export the historical data to Cloud Storage in Avro format. Configure a Vertex Al batch prediction job to generate predictions for the exported data.

Answer

Import the TensorFlow model by using the create model statement in BigQuery ML Apply the historical data to the TensorFlow model.

Answer

Export the historical data to Cloud Storage in CSV format Configure a Vertex Al batch prediction job to generate predictions for the exported data.

Question 40

You recently deployed a model to a Vertex Al endpoint Your data drifts frequently so you have enabled request-response logging and created a Vertex Al Model Monitoring job. You have observed that your model is receiving higher traffic than expected. You need to reduce the model monitoring cost while continuing to quickly detect drift. What should you do?

Accepted Answer

Decrease the sample_rate parameter in the Randomsampleconfig of the monitoring job.

Answer

Replace the monitoring job with a DataFlow pipeline that uses TensorFlow Data Validation (TFDV).

Answer

Replace the monitoring job with a custom SQL scnpt to calculate statistics on the features and predictions in BigQuery.

Answer

Increase the monitor_interval parameter in the scheduieconfig of the monitoring job.

Google Professional Machine Learning Engineer Practice Test 4

Google Professional Machine Learning Engineer Practice Tests