What Real Professional-Machine-Learning-Engineer Brain Dumps Is

NEW QUESTION 1
You are training a TensorFlow model on a structured data set with 100 billion records stored in several CSV files. You need to improve the input/output execution performance. What should you do?

• A. Load the data into BigQuery and read the data from BigQuery.
• B. Load the data into Cloud Bigtable, and read the data from Bigtable
• C. Convert the CSV files into shards of TFRecords, and store the data in Cloud Storage
• D. Convert the CSV files into shards of TFRecords, and store the data in the Hadoop Distributed File System (HDFS)

Answer: B

NEW QUESTION 2
You are an ML engineer at a regulated insurance company. You are asked to develop an insurance approval model that accepts or rejects insurance applications from potential customers. What factors should you consider before building the model?

• A. Redaction, reproducibility, and explainability
• B. Traceability, reproducibility, and explainability
• C. Federated learning, reproducibility, and explainability
• D. Differential privacy federated learning, and explainability

Answer: B

NEW QUESTION 3
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?

• A. Use the class distribution to generate 10% positive examples
• B. Use a convolutional neural network with max pooling and softmax activation
• C. Downsample the data with upweighting to create a sample with 10% positive examples
• D. Remove negative examples until the numbers of positive and negative examples are equal

Answer: D

NEW QUESTION 4
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?

• A. Increase the size of the training batch
• B. Decrease the size of the training batch
• C. Increase the learning rate hyperparameter
• D. Decrease the learning rate hyperparameter

Answer: C

NEW QUESTION 5
You are building a linear regression model on BigQuery ML to predict a customer's likelihood of purchasing your company's products. Your model uses a city name variable as a key predictive component. In order to train and serve the model, your data must be organized in columns. You want to prepare your data using the least amount of coding while maintaining the predictable variables. What should you do?

• A. Create a new view with BigQuery that does not include a column with city information
• B. Use Dataprep to transform the state column using a one-hot encoding method, and make each city a column with binary values.
• C. Use Cloud Data Fusion to assign each city to a region labeled as 1, 2, 3, 4, or 5r and then use that number to represent the city in the model.
• D. Use TensorFlow to create a categorical variable with a vocabulary list Create the vocabulary file, and upload it as part of your model to BigQuery ML.

Answer: C

NEW QUESTION 6
You work with a data engineering team that has developed a pipeline to clean your dataset and save it in a Cloud Storage bucket. You have created an ML model and want to use the data to refresh your model as soon as new data is available. As part of your CI/CD workflow, you want to automatically run a Kubeflow Pipelines training job on Google Kubernetes Engine (GKE). How should you architect this workflow?

• A. Configure your pipeline with Dataflow, which saves the files in Cloud Storage After the file is saved, start the training job on a GKE cluster
• B. Use App Engine to create a lightweight python client that continuously polls Cloud Storage for new files As soon as a file arrives, initiate the training job
• C. Configure a Cloud Storage trigger to send a message to a Pub/Sub topic when a new file is available in a storage bucke
• D. Use a Pub/Sub-triggered Cloud Function to start the training job on a GKE cluster
• E. Use Cloud Scheduler to schedule jobs at a regular interva
• F. For the first step of the jo
• G. check the timestamp of objects in your Cloud Storage bucket If there are no new files since the last run, abort the job.

Answer: A

NEW QUESTION 7
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?

• A. Normalize the data for the training, and test datasets as two separate steps.
• B. Split the training and test data based on time rather than a random split to avoid leakage
• C. Add more data to your test set to ensure that you have a fair distribution and sample for testing
• D. Apply data transformations before splitting, and cross-validate to make sure that the transformations are applied to both the training and test sets.

Answer: C

NEW QUESTION 8
You developed an ML model with Al Platform, and you want to move it to production. You serve a few thousand queries per second and are experiencing latency issues. Incoming requests are served by a load balancer that distributes them across multiple Kubeflow CPU-only pods running on Google Kubernetes Engine (GKE). Your goal is to improve the serving latency without changing the underlying infrastructure. What should you do?

• A. Significantly increase the max_batch_size TensorFlow Serving parameter
• B. Switch to the tensorflow-model-server-universal version of TensorFlow Serving
• C. Significantly increase the max_enqueued_batches TensorFlow Serving parameter
• D. Recompile TensorFlow Serving using the source to support CPU-specific optimizations Instruct GKE to choose an appropriate baseline minimum CPU platform for serving nodes

Answer: A

NEW QUESTION 9
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?

• A. Create multiple models using AutoML Tables
• B. Automate multiple training runs using Cloud Composer
• C. Run multiple training jobs on Al Platform with similar job names
• D. Create an experiment in Kubeflow Pipelines to organize multiple runs

Answer: C

NEW QUESTION 10
You are training a Resnet model on Al Platform using TPUs to visually categorize types of defects in automobile engines. You capture the training profile using the Cloud TPU profiler plugin and observe that it is highly input-bound. You want to reduce the bottleneck and speed up your model training process. Which modifications should you make to the tf .data dataset?
Choose 2 answers

• A. Use the interleave option for reading data
• B. Reduce the value of the repeat parameter
• C. Increase the buffer size for the shuffle option.
• D. Set the prefetch option equal to the training batch size
• E. Decrease the batch size argument in your transformation

Answer: AD

NEW QUESTION 11
You need to train a computer vision model that predicts the type of government ID present in a given image using a GPU-powered virtual machine on Compute Engine. You use the following parameters:
• Optimizer: SGD
• Image shape = 224x224
• Batch size = 64
• Epochs = 10
• Verbose = 2
During training you encounter the following error: ResourceExhaustedError: out of Memory (oom) when allocating tensor. What should you do?

• A. Change the optimizer
• B. Reduce the batch size
• C. Change the learning rate
• D. Reduce the image shape

Answer: A

NEW QUESTION 12
You recently designed and built a custom neural network that uses critical dependencies specific to your organization's framework. You need to train the model using a managed training service on Google Cloud. However, the ML framework and related dependencies are not supported by Al Platform Training. Also, both your model and your data are too large to fit in memory on a single machine. Your ML framework of choice uses the scheduler, workers, and servers distribution structure. What should you do?

• A. Use a built-in model available on Al Platform Training
• B. Build your custom container to run jobs on Al Platform Training
• C. Build your custom containers to run distributed training jobs on Al Platform Training
• D. Reconfigure your code to a ML framework with dependencies that are supported by Al Platform Training

Answer: C

NEW QUESTION 13
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 Datase
• C. from_tensor_slices{).
• D. Convert the images to tf .Tensor Objects, and then run t
• E. dat
• F. Datase
• G. from_tensors ().
• H. Convert the images Into TFRecords, store the images in Cloud Storage, and then use the t
• I. data API to read the images for training

Answer: D

NEW QUESTION 14
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?

• A. • 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
• B. • 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
• C. • 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.
• D. • 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

Answer: D

NEW QUESTION 15
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-team, and custom libraries. What should you do?

• A. Use the Al Platform custom containers feature to receive training jobs using any framework
• B. Configure Kubeflow to run on Google Kubernetes Engine and receive training jobs through TFJob
• C. Create a library of VM images on Compute Engine; and publish these images on a centralized repository
• D. Set up Slurm workload manager to receive jobs that can be scheduled to run on your cloud infrastructure.

Answer: D

NEW QUESTION 16
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?

• A. Use Kubeflow Pipelines to execute the experiments Export the metrics file, and query the results using the Kubeflow Pipelines API.
• B. Use Al Platform Training to execute the experiments Write the accuracy metrics to BigQuery, and query the results using the BigQueryAPI.
• C. Use Al Platform Training to execute the experiments Write the accuracy metrics to Cloud Monitoring, and query the results using the Monitoring API.
• D. Use Al Platform Notebooks to execute the experiment
• E. Collect the results in a shared Google Sheetsfile, and query the results using the Google Sheets API

Answer: A

NEW QUESTION 17
......