Latest Google Professional Machine Learning Engineer Exam Questions With Free Update

Jonnie Karnath2023-05-26T06:21:25+00:00

Becoming a certified Professional Machine Learning Engineer can open up many career opportunities in the technology industry. However, passing the Google certification exam requires extensive preparation and practice. Certspots is a platform that provides the latest exam questions and updates for the Google Professional Machine Learning Engineer certification exam, enabling individuals to prepare effectively and confidently take the exam. With Certspots, individuals can increase their chances of passing the exam and advancing their careers in the field of machine learning.

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1. Your organization's call center has asked you to develop a model that analyzes customer sentiments in each call. The call center receives over one million calls daily, and data is stored in Cloud Storage. The data collected must not leave the region in which the call originated, and no Personally Identifiable Information (Pll) can be stored or analyzed. The data science team has a third-party tool for visualization and access which requires a SQL ANSI-2011 compliant interface. You need to select components for data processing and for analytics.

How should the data pipeline be designed?

1 = Dataflow, 2 = BigQuery

1 = Pub/Sub, 2 = Datastore

1 = Dataflow, 2 = Cloud SQL

1 = Cloud Function, 2 = Cloud SQL

2. You are building a model to predict daily temperatures. You split the data randomly and then transformed the training and test datasets. Temperature data for model training is uploaded hourly. During testing, your model performed with 97% accuracy; however, after deploying to production, the model's accuracy dropped to 66%.

How can you make your production model more accurate?

Normalize the data for the training, and test datasets as two separate steps.

Split the training and test data based on time rather than a random split to avoid leakage

Add more data to your test set to ensure that you have a fair distribution and sample for testing

Apply data transformations before splitting, and cross-validate to make sure that the transformations are applied to both the training and test sets.

3. You are responsible for building a unified analytics environment across a variety of on-premises data marts. Your company is experiencing data quality and security challenges when integrating data across the servers, caused by the use of a wide range of disconnected tools and temporary solutions. You need a fully managed, cloud-native data integration service that will lower the total cost of work and reduce repetitive work. Some members on your team prefer a codeless interface for building Extract, Transform, Load (ETL) process.

Which service should you use?

Dataflow

Dataprep

Apache Flink

Cloud Data Fusion

4. Your company manages an application that aggregates news articles from many different online sources and sends them to users. You need to build a recommendation model that will suggest articles to readers that are similar to the articles they are currently reading.

Which approach should you use?

Create a collaborative filtering system that recommends articles to a user based on the user’s past behavior.

Encode all articles into vectors using word2vec, and build a model that returns articles based on vector similarity.

Build a logistic regression model for each user that predicts whether an article should be recommended to a user.

Manually label a few hundred articles, and then train an SVM classifier based on the manually classified articles that categorizes additional articles into their respective categories.

5. You are an ML engineer at a bank that has a mobile application. Management has asked you to build an ML-based biometric authentication for the app that verifies a customer's identity based on their fingerprint. Fingerprints are considered highly sensitive personal information and cannot be downloaded and stored into the bank databases.

Which learning strategy should you recommend to train and deploy this ML model?

Differential privacy

Federated learning

MD5 to encrypt data

Data Loss Prevention API

6. Your data science team needs to rapidly experiment with various features, model architectures, and hyperparameters. They need to track the accuracy metrics for various experiments and use an API to query the metrics over time.

What should they use to track and report their experiments while minimizing manual effort?

Use Kubeflow Pipelines to execute the experiments Export the metrics file, and query the results using the Kubeflow Pipelines AP

Use Al Platform Training to execute the experiments Write the accuracy metrics to BigQuery, and query the results using the BigQueryAP

Use Al Platform Training to execute the experiments Write the accuracy metrics to Cloud Monitoring, and query the results using the Monitoring AP

Use Al Platform Notebooks to execute the experiments. Collect the results in a shared Google Sheets file, and query the results using the Google Sheets API

7. You are an ML engineer at a manufacturing company. You need to build a model that identifies defects in products based on images of the product taken at the end of the assembly line. You want your model to preprocess the images with lower computation to quickly extract features of defects in products.

Which approach should you use to build the model?

Reinforcement learning

Recommender system

Recurrent Neural Networks (RNN)

Convolutional Neural Networks (CNN)

8. You work as an ML engineer at a social media company, and you are developing a visual filter for users’ profile photos. This requires you to train an ML model to detect bounding boxes around human faces. You want to use this filter in your company’s iOS-based mobile phone application. You want to minimize code development and want the model to be optimized for inference on mobile phones.

What should you do?

Train a model using AutoML Vision and use the “export for Core ML” option.

Train a model using AutoML Vision and use the “export for Coral” option.

Train a model using AutoML Vision and use the “export for TensorFlow.js” option.

Train a custom TensorFlow model and convert it to TensorFlow Lite (TFLite).

9. 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?

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

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

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

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

10. 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?

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

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

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

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

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11. You are developing a Kubeflow pipeline on Google Kubernetes Engine. The first step in the pipeline is to issue a query against BigQuery. You plan to use the results of that query as the input to the next step in your pipeline. You want to achieve this in the easiest way possible.

What should you do?

Use the BigQuery console to execute your query and then save the query results Into a new BigQuery table.

Write a Python script that uses the BigQuery API to execute queries against BigQuery Execute this script as the first step in your Kubeflow pipeline

Use the Kubeflow Pipelines domain-specific language to create a custom component that uses the Python BigQuery client library to execute queries

Locate the Kubeflow Pipelines repository on GitHub Find the BigQuery Query Component, copy that component's URL, and use it to load the component into your pipeline. Use the component to execute queries against BigQuery

12. You work on an operations team at an international company that manages a large fleet of on-premises servers located in few data centers around the world. Your team collects monitoring data from the servers, including CPU/memory consumption. When an incident occurs on a server, your team is responsible for fixing it. Incident data has not been properly labeled yet. Your management team wants you to build a predictive maintenance solution that uses monitoring data from the VMs to detect potential failures and then alerts the service desk team.

What should you do first?

Train a time-series model to predict the machines’ performance values. Configure an alert if a machine’s actual performance values significantly differ from the predicted performance values.

Implement a simple heuristic (e.g., based on z-score) to label the machines’ historical performance data. Train a model to predict anomalies based on this labeled dataset.

Develop a simple heuristic (e.g., based on z-score) to label the machines’ historical performance data. Test this heuristic in a production environment.

Hire a team of qualified analysts to review and label the machines’ historical performance data. Train a model based on this manually labeled dataset.

13. You work for a large technology company that wants to modernize their contact center. You have been asked to develop a solution to classify incoming calls by product so that requests can be more quickly routed to the correct support team. You have already transcribed the calls using the Speech-to-Text API. You want to minimize data preprocessing and development time.

How should you build the model?

Use the Al Platform Training built-in algorithms to create a custom model

Use AutoML Natural Language to extract custom entities for classification

Use the Cloud Natural Language API to extract custom entities for classification

Build a custom model to identify the product keywords from the transcribed calls, and then run the keywords through a classification algorithm

14. During batch training of a neural network, you notice that there is an oscillation in the loss.

How should you adjust your model to ensure that it converges?

Increase the size of the training batch

Decrease the size of the training batch

Increase the learning rate hyperparameter

Decrease the learning rate hyperparameter

15. You need to build an ML model for a social media application to predict whether a user’s submitted profile photo meets the requirements. The application will inform the user if the picture meets the requirements.

How should you build a model to ensure that the application does not falsely accept a non-compliant picture?

Use AutoML to optimize the model’s recall in order to minimize false negatives.

Use AutoML to optimize the model’s F1 score in order to balance the accuracy of false positives and false negatives.

Use Vertex AI Workbench user-managed notebooks to build a custom model that has three times as many examples of pictures that meet the profile photo requirements.

Use Vertex AI Workbench user-managed notebooks to build a custom model that has three times as many examples of pictures that do not meet the profile photo requirements.

16. Your team has been tasked with creating an ML solution in Google Cloud to classify support requests for one of your platforms. You analyzed the requirements and decided to use TensorFlow to build the classifier so that you have full control of the model's code, serving, and deployment. You will use Kubeflow pipelines for the ML platform. To save time, you want to build on existing resources and use managed services instead of building a completely new model.

How should you build the classifier?

Use the Natural Language API to classify support requests

Use AutoML Natural Language to build the support requests classifier

Use an established text classification model on Al Platform to perform transfer learning

Use an established text classification model on Al Platform as-is to classify support requests

17. You lead a data science team at a large international corporation. Most of the models your team trains are large-scale models using high-level TensorFlow APIs on AI Platform with GPUs. Your team usually

takes a few weeks or months to iterate on a new version of a model. You were recently asked to review your team’s spending.

How should you reduce your Google Cloud compute costs without impacting the model’s performance?

Use AI Platform to run distributed training jobs with checkpoints.

Use AI Platform to run distributed training jobs without checkpoints.

Migrate to training with Kuberflow on Google Kubernetes Engine, and use preemptible VMs with checkpoints.

Migrate to training with Kuberflow on Google Kubernetes Engine, and use preemptible VMs without checkpoints.

18. You have trained a model on a dataset that required computationally expensive preprocessing operations. You need to execute the same preprocessing at prediction time. You deployed the model on Al Platform for high-throughput online prediction.

Which architecture should you use?

• Validate the accuracy of the model that you trained on preprocessed data • Create a new model that uses the raw data and is available in real time • Deploy the new model onto Al Platform for online prediction

• Send incoming prediction requests to a Pub/Sub topic • Transform the incoming data using a Dataflow job • Submit a prediction request to Al Platform using the transformed data • Write the predictions to an outbound Pub/Sub queue

• Stream incoming prediction request data into Cloud Spanner • Create a view to abstract your preprocessing logic. • Query the view every second for new records • Submit a prediction request to Al Platform using the transformed data • Write the predictions to an outbound Pub/Sub queue.

• Send incoming prediction requests to a Pub/Sub topic • Set up a Cloud Function that is triggered when messages are published to the Pub/Sub topic. • Implement your preprocessing logic in the Cloud Function • Submit a prediction request to Al Platform using the transformed data • Write the predictions to an outbound Pub/Sub queue

19. 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?

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

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

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.

20. 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?

Import the model into BigQuery M

Make predictions using batch reading data from BigQuery, and push the data to Cloud SQL

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

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 SQ

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 SQ

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21. You work for a company that is developing a new video streaming platform. You have been asked to create a recommendation system that will suggest the next video for a user to watch. After a review by an AI Ethics team, you are approved to start development. Each video asset in your company’s catalog has useful metadata (e.g., content type, release date, country), but you do not have any historical user event data.

How should you build the recommendation system for the first version of the product?

Launch the product without machine learning. Present videos to users alphabetically, and start collecting user event data so you can develop a recommender model in the future.

Launch the product without machine learning. Use simple heuristics based on content metadata to recommend similar videos to users, and start collecting user event data so you can develop a recommender model in the future.

Launch the product with machine learning. Use a publicly available dataset such as MovieLens to train a model using the Recommendations AI, and then apply this trained model to your data.

Launch the product with machine learning. Generate embeddings for each video by training an autoencoder on the content metadata using TensorFlow. Cluster content based on the similarity of these embeddings, and then recommend videos from the same cluster.

22. 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?

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

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

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

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

23. You have a functioning end-to-end ML pipeline that involves tuning the hyperparameters of your ML model using Al Platform, and then using the best-tuned parameters for training. Hypertuning is taking longer than expected and is delaying the downstream processes. You want to speed up the tuning job without significantly compromising its effectiveness.

Which actions should you take? Choose 2 answers

Decrease the number of parallel trials

Decrease the range of floating-point values

Set the early stopping parameter to TRUE

Change the search algorithm from Bayesian search to random search.

Decrease the maximum number of trials during subsequent training phases.

24. You are designing an ML recommendation model for shoppers on your company's ecommerce website. You will use Recommendations Al to build, test, and deploy your system.

How should you develop recommendations that increase revenue while following best practices?

Use the "Other Products You May Like" recommendation type to increase the click-through rate

Use the "Frequently Bought Together' recommendation type to increase the shopping cart size for each order.

Import your user events and then your product catalog to make sure you have the highest quality event stream

Because it will take time to collect and record product data, use placeholder values for the product catalog to test the viability of the model.

25. You have a large corpus of written support cases that can be classified into 3 separate categories: Technical Support, Billing Support, or Other Issues. You need to quickly build, test, and deploy a service that will automatically classify future written requests into one of the categories.

How should you configure the pipeline?

Use the Cloud Natural Language API to obtain metadata to classify the incoming cases.

Use AutoML Natural Language to build and test a classifier. Deploy the model as a REST AP

Use BigQuery ML to build and test a logistic regression model to classify incoming requests. Use BigQuery ML to perform inference.

Create a TensorFlow model using Google’s BERT pre-trained model. Build and test a classifier, and deploy the model using Vertex A

26. You need to execute a batch prediction on 100 million records in a BigQuery table with a custom TensorFlow DNN regressor model, and then store the predicted results in a BigQuery table. You want to minimize the effort required to build this inference pipeline.

What should you do?

Import the TensorFlow model with BigQuery ML, and run the ml.predict function.

Use the TensorFlow BigQuery reader to load the data, and use the BigQuery API to write the results to BigQuery.

Create a Dataflow pipeline to convert the data in BigQuery to TFRecords. Run a batch inference on Vertex AI Prediction, and write the results to BigQuery.

Load the TensorFlow SavedModel in a Dataflow pipeline. Use the BigQuery I/O connector with a custom function to perform the inference within the pipeline, and write the results to BigQuery.

27. You are working on a binary classification ML algorithm that detects whether an image of a classified scanned document contains a company’s logo. In the dataset, 96% of examples don’t have the logo, so the dataset is very skewed.

Which metrics would give you the most confidence in your model?

F-score where recall is weighed more than precision

RMSE

F1 score

F-score where precision is weighed more than recall

28. You are training an LSTM-based model on Al Platform to summarize text using the following job submission script:

You want to ensure that training time is minimized without significantly compromising the accuracy of your model.

What should you do?

Modify the 'epochs' parameter

Modify the 'scale-tier' parameter

Modify the batch size' parameter

Modify the 'learning rate' parameter

29. 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?

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

Remove the model and replace it with human moderation.

Replace your model with a different text classifier.

Raise the threshold for comments to be considered toxic or harmful

30. One of your models is trained using data provided by a third-party data broker. The data broker does not reliably notify you of formatting changes in the data. You want to make your model training pipeline more robust to issues like this.

What should you do?

Use TensorFlow Data Validation to detect and flag schema anomalies.

Use TensorFlow Transform to create a preprocessing component that will normalize data to the expected distribution, and replace values that don’t match the schema with 0.

Use tf.math to analyze the data, compute summary statistics, and flag statistical anomalies.

Use custom TensorFlow functions at the start of your model training to detect and flag known formatting errors.

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31. You work for a large hotel chain and have been asked to assist the marketing team in gathering predictions for a targeted marketing strategy. You need to make predictions about user lifetime value (LTV) over the next 30 days so that marketing can be adjusted accordingly. The customer dataset is in BigQuery, and you are preparing the tabular data for training with AutoML Tables. This data has a time signal that is spread across multiple columns.

How should you ensure that AutoML fits the best model to your data?

Manually combine all columns that contain a time signal into an array Allow AutoML to interpret this array appropriately Choose an automatic data split across the training, validation, and testing sets

Submit the data for training without performing any manual transformations Allow AutoML to handle the appropriate transformations Choose an automatic data split across the training, validation, and testing sets

Submit the data for training without performing any manual transformations, and indicate an appropriate column as the Time column Allow AutoML to split your data based on the time signal provided, and reserve the more recent data for the validation and testing sets

Submit the data for training without performing any manual transformations Use the columns that have a time signal to manually split your data Ensure that the data in your validation set is from 30 days after the data in your training set and that the data in your testing set is from 30 days after your validation set

32. 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.)

501*256+257*128+2 = 161154

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

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

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

33. You need to design a customized deep neural network in Keras that will predict customer purchases based on their purchase history. You want to explore model performance using multiple model architectures, store training data, and be able to compare the evaluation metrics in the same dashboard.

What should you do?

Create multiple models using AutoML Tables

Automate multiple training runs using Cloud Composer

Run multiple training jobs on Al Platform with similar job names

Create an experiment in Kubeflow Pipelines to organize multiple runs

34. 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?

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

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

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

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

35. You are building a TensorFlow model for a financial institution that predicts the impact of consumer spending on inflation globally. Due to the size and nature of the data, your model is long-running across all types of hardware, and you have built frequent checkpointing into the training process. Your organization has asked you to minimize cost.

What hardware should you choose?

A Vertex AI Workbench user-managed notebooks instance running on an n1-standard-16 with 4 NVIDIA P100 GPUs

A Vertex AI Workbench user-managed notebooks instance running on an n1-standard-16 with an NVIDIA P100 GPU

A Vertex AI Workbench user-managed notebooks instance running on an n1-standard-16 with a non-preemptible v3-8 TPU

A Vertex AI Workbench user-managed notebooks instance running on an n1-standard-16 with a preemptible v3-8 TPU

36. 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?

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

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

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

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

37. You recently built the first version of an image segmentation model for a self-driving car. After deploying the model, you observe a decrease in the area under the curve (AUC) metric. When analyzing the video recordings, you also discover that the model fails in highly congested traffic but works as expected when there is less traffic.

What is the most likely reason for this result?

The model is overfitting in areas with less traffic and underfitting in areas with more traffic.

AUC is not the correct metric to evaluate this classification model.

Too much data representing congested areas was used for model training.

Gradients become small and vanish while backpropagating from the output to input nodes.

38. You work for an advertising company and want to understand the effectiveness of your company's latest advertising campaign. You have streamed 500 MB of campaign data into BigQuery. You want to query the table, and then manipulate the results of that query with a pandas dataframe in an Al Platform notebook.

What should you do?

Use Al Platform Notebooks' BigQuery cell magic to query the data, and ingest the results as a pandas dataframe

Export your table as a CSV file from BigQuery to Google Drive, and use the Google Drive API to ingest the file into your notebook instance

Download your table from BigQuery as a local CSV file, and upload it to your Al Platform notebook instance Use pandas. read_csv to ingest the file as a pandas dataframe

From a bash cell in your Al Platform notebook, use the bq extract command to export the table as a CSV file to Cloud Storage, and then use gsutii cp to copy the data into the notebook Use pandas. read_csv to ingest the file as a pandas dataframe

39. You were asked to investigate failures of a production line component based on sensor readings. After receiving the dataset, you discover that less than 1% of the readings are positive examples representing failure incidents. You have tried to train several classification models, but none of them converge.

How should you resolve the class imbalance problem?

Use the class distribution to generate 10% positive examples

Use a convolutional neural network with max pooling and softmax activation

Downsample the data with upweighting to create a sample with 10% positive examples

Remove negative examples until the numbers of positive and negative examples are equal

40. You work for an online retail company that is creating a visual search engine. You have set up an end-to-end ML pipeline on Google Cloud to classify whether an image contains your company's product. Expecting the release of new products in the near future, you configured a retraining functionality in the pipeline so that new data can be fed into your ML models. You also want to use Al Platform's continuous evaluation service to ensure that the models have high accuracy on your test data set.

What should you do?

Keep the original test dataset unchanged even if newer products are incorporated into retraining

Extend your test dataset with images of the newer products when they are introduced to retraining

Replace your test dataset with images of the newer products when they are introduced to retraining.

Update your test dataset with images of the newer products when your evaluation metrics drop below a pre-decided threshold.

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41. 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?

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

Replace the missing values with the feature’s mean.

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

Move the rows with missing values to your validation dataset.

42. You work for an online travel agency that also sells advertising placements on its website to other companies.

You have been asked to predict the most relevant web banner that a user should see next. Security is

important to your company. The model latency requirements are 300ms@p99, the inventory is thousands of web banners, and your exploratory analysis has shown that navigation context is a good predictor. You want to Implement the simplest solution.

How should you configure the prediction pipeline?

Embed the client on the website, and then deploy the model on AI Platform Prediction.

Embed the client on the website, deploy the gateway on App Engine, and then deploy the model on AI Platform Prediction.

Embed the client on the website, deploy the gateway on App Engine, deploy the database on Cloud Bigtable for writing and for reading the user’s navigation context, and then deploy the model on AI Platform Prediction.

Embed the client on the website, deploy the gateway on App Engine, deploy the database on Memorystore for writing and for reading the user’s navigation context, and then deploy the model on Google Kubernetes Engine.

43. You need to train a regression model based on a dataset containing 50,000 records that is stored in BigQuery. The data includes a total of 20 categorical and numerical features with a target variable that can include negative values. You need to minimize effort and training time while maximizing model performance.

What approach should you take to train this regression model?

Create a custom TensorFlow DNN model.

Use BQML XGBoost regression to train the model

Use AutoML Tables to train the model without early stopping.

Use AutoML Tables to train the model with RMSLE as the optimization objective

44. You built and manage a production system that is responsible for predicting sales numbers. Model accuracy is crucial, because the production model is required to keep up with market changes. Since being deployed to production, the model hasn't changed; however the accuracy of the model has steadily deteriorated.

What issue is most likely causing the steady decline in model accuracy?

Poor data quality

Lack of model retraining

Too few layers in the model for capturing information

Incorrect data split ratio during model training, evaluation, validation, and test

45. You have trained a deep neural network model on Google Cloud. The model has low loss on the training data, but is performing worse on the validation data. You want the model to be resilient to overfitting.

Which strategy should you use when retraining the model?

Apply a dropout parameter of 0 2, and decrease the learning rate by a factor of 10

Apply a 12 regularization parameter of 0.4, and decrease the learning rate by a factor of 10.

Run a hyperparameter tuning job on Al Platform to optimize for the L2 regularization and dropout parameters

Run a hyperparameter tuning job on Al Platform to optimize for the learning rate, and increase the number of neurons by a factor of 2.

46. You have written unit tests for a Kubeflow Pipeline that require custom libraries. You want to automate the execution of unit tests with each new push to your development branch in Cloud Source Repositories.

What should you do?

Write a script that sequentially performs the push to your development branch and executes the unit tests on Cloud Run

Using Cloud Build, set an automated trigger to execute the unit tests when changes are pushed to your development branch.

Set up a Cloud Logging sink to a Pub/Sub topic that captures interactions with Cloud Source Repositories Configure a Pub/Sub trigger for Cloud Run, and execute the unit tests on Cloud Run.

Set up a Cloud Logging sink to a Pub/Sub topic that captures interactions with Cloud Source Repositories. Execute the unit tests using a Cloud Function that is triggered when messages are sent to the Pub/Sub topic

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