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?

A. Poor data quality

B. Lack of model retraining

C. Too few layers in the model for capturing information

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

You have been asked to develop an input pipeline for an ML training model that processes images from disparate sources at a low latency. You discover that your input data does not fit in memory. How should you create a dataset following Google-recommended best practices?

A. Create a tf.data.Dataset.prefetch transformation.

B. Convert the images to tf.Tensor objects, and then run Dataset.from_tensor_slices().

C. Convert the images to tf.Tensor objects, and then run tf.data.Dataset.from_tensors().

D. Convert the images into TFRecords, store the images in Cloud Storage, and then use the tf.data API to read the images for training.

You have a demand forecasting pipeline in production that uses Dataflow to preprocess raw data prior to model training and prediction. During preprocessing, you employ Z-score normalization on data stored in BigQuery and write it back to BigQuery. New training data is added every week. You want to make the process more efficient by minimizing computation time and manual intervention. What should you do?

A. Normalize the data using Google Kubernetes Engine.

B. Translate the normalization algorithm into SQL for use with BigQuery.

C. Use the normalizer_fn argument in TensorFlow's Feature Column API.

D. Normalize the data with Apache Spark using the Dataproc connector for BigQuery.

You have a functioning end-to-end ML pipeline that involves tuning the hyperparameters of your ML model using AI 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 two.)

A. Decrease the number of parallel trials.

B. Decrease the range of floating-point values.

C. Set the early stopping parameter to TRUE.

D. Change the search algorithm from Bayesian search to random search.

E. Decrease the maximum number of trials during subsequent training phases.

You recently joined an enterprise-scale company that has thousands of datasets. You know that there are accurate descriptions for each table in BigQuery, and you are searching for the proper BigQuery table to use for a model you are building on AI Platform. How should you find the data that you need?

A. Use Data Catalog to search the BigQuery datasets by using keywords in the table description.

B. Tag each of your model and version resources on AI Platform with the name of the BigQuery table that was used for training.

C. Maintain a lookup table in BigQuery that maps the table descriptions to the table ID. Query the lookup table to find the correct table ID for the data that you need.

D. Execute a query in BigQuery to retrieve all the existing table names in your project using the INFORMATION_SCHEMA metadata tables that are native to BigQuery. Use the result o find the table that you need.

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?

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

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

C. 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.

D. 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.

You have deployed a model on Vertex AI for real-time inference. During an online prediction request, you get an "Out of Memory" error. What should you do?

A. Use batch prediction mode instead of online mode.

B. Send the request again with a smaller batch of instances.

C. Use base64 to encode your data before using it for prediction.

D. Apply for a quota increase for the number of prediction requests.

You have successfully deployed to production a large and complex TensorFlow model trained on tabular data. You want to predict the lifetime value (LTV) field for each subscription stored in the BigQuery table named subscription. subscriptionPurchase in the project named my-fortune500-company-project.

You have organized all your training code, from preprocessing data from the BigQuery table up to deploying the validated model to the Vertex AI endpoint, into a TensorFlow Extended (TFX) pipeline. You want to prevent prediction drift, i.e., a situation when a feature data distribution in production changes significantly over time. What should you do?

A. Implement continuous retraining of the model daily using Vertex AI Pipelines.

B. Add a model monitoring job where 10% of incoming predictions are sampled 24 hours.

C. Add a model monitoring job where 90% of incoming predictions are sampled 24 hours.

D. Add a model monitoring job where 10% of incoming predictions are sampled every hour.

You work for a toy manufacturer that has been experiencing a large increase in demand. You need to build an ML model to reduce the amount of time spent by quality control inspectors checking for product defects. Faster defect detection is a priority. The factory does not have reliable Wi-Fi. Your company wants to implement the new ML model as soon as possible. Which model should you use?

A. AutoML Vision Edge mobile-high-accuracy-1 model

B. AutoML Vision Edge mobile-low-latency-1 model

C. AutoML Vision model

D. AutoML Vision Edge mobile-versatile-1 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?

A. 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.

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

C. Package your code with Setuptools, and use a pre-built container. Train your model with Vertex AI using a custom tier that contains the required GPUs.

D. Configure a Compute Engine VM with all the dependencies that launches the training. Train your model with Vertex AI using a custom tier that contains the required GPUs.