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You are an ML engineer in the contact center of a large enterprise. You need to build a sentiment analysis tool that predicts customer sentiment from recorded phone conversations. You need to identify the best approach to building a model while ensuring that the gender, age, and cultural differences of the customers who called the contact center do not impact any stage of the model development pipeline and results. What should you do?

A.

Extract sentiment directly from the voice recordings

B.

Convert the speech to text and build a model based on the words

C.

Convert the speech to text and extract sentiments based on the sentences

D.

Convert the speech to text and extract sentiment using syntactical analysis

You have trained an XGBoost model that you plan to deploy on Vertex Al for online prediction. You are now uploading your model to Vertex Al Model Registry, and you need to configure the explanation method that will serve online prediction requests to be returned with minimal latency. You also want to be alerted when feature attributions of the model meaningfully change over time. What should you do?

A.

1 Specify sampled Shapley as the explanation method with a path count of 5.

2 Deploy the model to Vertex Al Endpoints.

3. Create a Model Monitoring job that uses prediction drift as the monitoring objective.

B.

1 Specify Integrated Gradients as the explanation method with a path count of 5.

2 Deploy the model to Vertex Al Endpoints.

3. Create a Model Monitoring job that uses prediction drift as the monitoring objective.

C.

1. Specify sampled Shapley as the explanation method with a path count of 50.

2. Deploy the model to Vertex Al Endpoints.

3. Create a Model Monitoring job that uses training-serving skew as the monitoring objective.

D.

1 Specify Integrated Gradients as the explanation method with a path count of 50.

2. Deploy the model to Vertex Al Endpoints.

3 Create a Model Monitoring job that uses training-serving skew as the monitoring objective.

You work for a pharmaceutical company based in Canada. Your team developed a BigQuery ML model to predict the number of flu infections for the next month in Canada Weather data is published weekly and flu infection statistics are published monthly. You need to configure a model retraining policy that minimizes cost What should you do?

A.

Download the weather and flu data each week Configure Cloud Scheduler to execute a Vertex Al pipeline to retrain the model weekly.

B.

Download the weather and flu data each month Configure Cloud Scheduler to execute a Vertex Al pipeline to retrain the model monthly.

C.

Download the weather and flu data each week Configure Cloud Scheduler to execute a Vertex Al pipeline to retrain the model every month.

D.

Download the weather data each week, and download the flu data each month Deploy the model to a Vertex Al endpoint with feature drift monitoring. and retrain the model if a monitoring alert is detected.

You have trained a model by using data that was preprocessed in a batch Dataflow pipeline Your use case requires real-time inference. You want to ensure that the data preprocessing logic is applied consistently between training and serving. What should you do?

A.

Perform data validation to ensure that the input data to the pipeline is the same format as the input data to the endpoint.

B.

Refactor the transformation code in the batch data pipeline so that it can be used outside of the pipeline Use the same code in the endpoint.

C.

Refactor the transformation code in the batch data pipeline so that it can be used outside of the pipeline Share this code with the end users of the endpoint.

D.

Batch the real-time requests by using a time window and then use the Dataflow pipeline to preprocess the batched requests. Send the preprocessed requests to the endpoint.

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?

A.

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

B.

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

C.

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

D.

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

You need to use TensorFlow to train an image classification model. Your dataset is located in a Cloud Storage directory and contains millions of labeled images Before training the model, you need to prepare the data. You want the data preprocessing and model training workflow to be as efficient scalable, and low maintenance as possible. What should you do?

A.

1 Create a Dataflow job that creates sharded TFRecord files in a Cloud Storage directory.

2 Reference tf .data.TFRecordDataset in the training script.

3. Train the model by using Vertex Al Training with a V100 GPU.

B.

1 Create a Dataflow job that moves the images into multiple Cloud Storage directories, where each directory is named according to the corresponding label.

2 Reference tfds.fclder_da-asst.imageFclder in the training script.

3. Train the model by using Vertex AI Training with a V100 GPU.

C.

1 Create a Jupyter notebook that uses an n1-standard-64, V100 GPU Vertex Al Workbench instance.

2 Write a Python script that creates sharded TFRecord files in a directory inside the instance

3. Reference tf. da-a.TFRecrrdDataset in the training script.

4. Train the model by using the Workbench instance.

D.

1 Create a Jupyter notebook that uses an n1-standard-64, V100 GPU Vertex Al Workbench instance.

2 Write a Python scnpt that copies the images into multiple Cloud Storage directories, where each directory is named according to the corresponding label.

3 Reference tf ds. f older_dataset. imageFolder in the training script.

4. Train the model by using the Workbench instance.

You are building a real-time prediction engine that streams files which may contain Personally Identifiable Information (Pll) to Google Cloud. You want to use the Cloud Data Loss Prevention (DLP) API to scan the files. How should you ensure that the Pll is not accessible by unauthorized individuals?

A.

Stream all files to Google CloudT and then write the data to BigQuery Periodically conduct a bulk scan of the table using the DLP API.

B.

Stream all files to Google Cloud, and write batches of the data to BigQuery While the data is being written to BigQuery conduct a bulk scan of the data using the DLP API.

C.

Create two buckets of data Sensitive and Non-sensitive Write all data to the Non-sensitive bucket Periodically conduct a bulk scan of that bucket using the DLP API, and move the sensitive data to the Sensitive bucket

D.

Create three buckets of data: Quarantine, Sensitive, and Non-sensitive Write all data to the Quarantine bucket.

E.

Periodically conduct a bulk scan of that bucket using the DLP API, and move the data to either the Sensitive or Non-Sensitive bucket

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

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

A.

501*256+257*128+2 = 161154

B.

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

C.

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

D.

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

You received a training-serving skew alert from a Vertex Al Model Monitoring job running in production. You retrained the model with more recent training data, and deployed it back to the Vertex Al endpoint but you are still receiving the same alert. What should you do?

A.

Update the model monitoring job to use a lower sampling rate.

B.

Update the model monitoring job to use the more recent training data that was used to retrain the model.

C.

Temporarily disable the alert Enable the alert again after a sufficient amount of new production traffic has passed through the Vertex Al endpoint.

D.

Temporarily disable the alert until the model can be retrained again on newer training data Retrain the model again after a sufficient amount of new production traffic has passed through the Vertex Al endpoint

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