PMLE practice questions 241–250

1. Question 241 · 364 in bank

   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 AI Platform notebook. What should you do?

   • AUse AI Platform Notebooks' BigQuery cell magic to query the data, and ingest the results as a pandas dataframe.
   • BExport 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.
   • CDownload your table from BigQuery as a local CSV file, and upload it to your AI Platform notebook instance. Use pandas.read_csv to ingest he file as a pandas dataframe.
   • DFrom a bash cell in your AI Platform notebook, use the bq extract command to export the table as a CSV file to Cloud Storage, and then use gsutil cp to copy the data into the notebook. Use pandas.read_csv to ingest the file as a pandas dataframe.

   Show answer

   Answer: A

   Explanation

   Answer : A . Refer to this link for details: https://cloud.google.com/bigquery/docs/bigquery-storage-python-pandas First 2 points talks about querying the data. Download query results to a pandas DataFrame by using the BigQuery Storage API from the IPython magics for BigQuery in a Jupyter notebook. Download query results to a pandas DataFrame by using the BigQuery client library for Python. Download BigQuery table data to a pandas DataFrame by using the BigQuery client library for Python. Download BigQuery table data to a pandas DataFrame by using the BigQuery Storage API client library for Python.

   Source: Adapted from community discussion — Sachin2360

   References

   • Google Cloud documentation
   • Google Cloud documentation

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2. Question 242 · 364 in bank

   You are an ML engineer at a global car manufacture. You need to build an ML model to predict car sales in different cities around the world. Which features or feature crosses should you use to train city-specific relationships between car type and number of sales?

   • AThee individual features: binned latitude, binned longitude, and one-hot encoded car type.
   • BOne feature obtained as an element-wise product between latitude, longitude, and car type.
   • COne feature obtained as an element-wise product between binned latitude, binned longitude, and one-hot encoded car type.
   • DTwo feature crosses as an element-wise product: the first between binned latitude and one-hot encoded car type, and the second between binned longitude and one-hot encoded car type.

   Show answer

   Answer: C

   Explanation

   Cannot be D, Despite Binning is a good idea because it enables the model to learn nonlinear relationships within a single feature; separate latitude and longitude in different feature crosses is not a good one, this separation will prevent the model from learning city-specific sales. A city is the conjunction of latitude and longitude. In that order of Ideas Crossing binned latitude with binned longitude enables the model to learn city-specific effects of car type. I will go for C, https://developers.google.com/machine-learning/crash-course/feature-crosses/check-your-understanding

   Source: Adapted from community discussion — jk73

   References

   • Google developer documentation
   • Google developer documentation

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3. Question 243 · 364 in bank

   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?

   • AUse the AI Platform Training built-in algorithms to create a custom model.
   • BUse AutoMlL Natural Language to extract custom entities for classification.
   • CUse the Cloud Natural Language API to extract custom entities for classification.
   • DBuild a custom model to identify the product keywords from the transcribed calls, and then run the keywords through a classification algorithm.

   Show answer

   Answer: B

   Explanation

   If anything, C is wrong because it tells you something that is not true: extract custom entities with Natural Language API it's not possible. That is something you can do only with AutoML. Look at this comparison table: https://cloud.google.com/natural-language#section-6 That's how they subtly point you at answer B.

   Source: Adapted from community discussion — giaZ

   References

   • Google Cloud documentation

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4. Question 244 · 364 in bank

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

   • ALoad the data into BigQuery, and read the data from BigQuery.
   • BLoad the data into Cloud Bigtable, and read the data from Bigtable.
   • CConvert the CSV files into shards of TFRecords, and store the data in Cloud Storage.
   • DConvert the CSV files into shards of TFRecords, and store the data in the Hadoop Distributed File System (HDFS).

   Show answer

   Answer: C

   Explanation

   BigQuery is designed for running large-scale analytical queries, not for serving input pipelines for machine learning models like TensorFlow. BigQuery's strength is in its ability to handle complex queries over vast amounts of data, but it may not provide the optimal performance for the specific task of feeding data into a TensorFlow model. On the other hand, converting the CSV files into shards of TFRecords and storing them in Cloud Storage (Option C) will provide better performance because TFRecords is a format designed specifically for TensorFlow. It allows for efficient storage and retrieval of data, making it a more suitable choice for improving the input/output execution performance. Additionally, Cloud Storage provides high throughput and low-latency data access, which is beneficial for training large-scale TensorFlow models.

   Source: Adapted from community discussion — tavva_prudhvi

   References

   • Google Cloud documentation
   • Google Cloud documentation

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5. Question 245 · 364 in bank

   As the lead ML Engineer for your company, you are responsible for building ML models to digitize scanned customer forms. You have developed a TensorFlow model that converts the scanned images into text and stores them in Cloud Storage. You need to use your ML model on the aggregated data collected at the end of each day with minimal manual intervention. What should you do?

   • AUse the batch prediction functionality of AI Platform.
   • BCreate a serving pipeline in Compute Engine for prediction.
   • CUse Cloud Functions for prediction each time a new data point is ingested.
   • DDeploy the model on AI Platform and create a version of it for online inference.

   Show answer

   Answer: A

   Explanation

   Because aggregated data can be sent at the end of the day for batch prediction and AI platform is managed so satisfy minimal intervention requirement Not B as violates minimal intervention requirement Not C and D as real-time or online inference is not needed since data is aggregated at the end of the day

   Source: Adapted from community discussion — koakande

   References

   • Google Cloud documentation

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6. Question 246 · 364 in bank

   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?

   • AUse Data Catalog to search the BigQuery datasets by using keywords in the table description.
   • BTag each of your model and version resources on AI Platform with the name of the BigQuery table that was used for training.
   • CMaintain 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.
   • DExecute 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.

   Show answer

   Answer: A

   Explanation

   A should be the way to go for large datasets --This is also good but it is legacy way of checking:- NFORMATION_SCHEMA contains these views for table metadata: TABLES and TABLE_OPTIONS for metadata about tables. COLUMNS and COLUMN_FIELD_PATHS for metadata about columns and fields. PARTITIONS for metadata about table partitions (Preview)

   Source: Adapted from community discussion — NamitSehgal

   References

   • Google Cloud documentation

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7. Question 247 · 364 in bank

   You started working on a classification problem with time series data and achieved an area under the receiver operating characteristic curve (AUC ROC) value of 99% for training data after just a few experiments. You haven't explored using any sophisticated algorithms or spent any time on hyperparameter tuning. What should your next step be to identify and fix the problem?

   • AAddress the model overfitting by using a less complex algorithm.
   • BAddress data leakage by applying nested cross-validation during model training.
   • CAddress data leakage by removing features highly correlated with the target value.
   • DAddress the model overfitting by tuning the hyperparameters to reduce the AUC ROC value.

   Show answer

   Answer: B

   Explanation

   Ans: B (Ref: https://towardsdatascience.com/time-series-nested-cross-validation-76adba623eb9) (C) High correlation doesn't mean leakage. The question may suggest target leakage and the defining point of this leakage is the availability of data after the target is available.(https://www.kaggle.com/dansbecker/data-leakage)

   Source: Adapted from community discussion — Paul_Dirac

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8. Question 248 · 364 in bank

   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?

   • AEmbed the client on the website, and then deploy the model on AI Platform Prediction.
   • BEmbed the client on the website, deploy the gateway on App Engine, and then deploy the model on AI Platform Prediction.
   • CEmbed 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.
   • DEmbed 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.

   Show answer

   Answer: C

   Explanation

   Security => not A. B: doesn't handle processing with banner inventory. D: deployment on GKE is less simple than on AI Platform. Besides, MemoryStore is in-memory while banners are stored persistently. Ans: C

   Source: Adapted from community discussion — Paul_Dirac

   References

   • Google Cloud documentation
   • Google Cloud documentation

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9. Question 249 · 364 in bank

   Your team is building a convolutional neural network (CNN)-based architecture from scratch. The preliminary experiments running on your on-premises CPU-only infrastructure were encouraging, but have slow convergence. You have been asked to speed up model training to reduce time-to-market. You want to experiment with virtual machines (VMs) on Google Cloud to leverage more powerful hardware. Your code does not include any manual device placement and has not been wrapped in Estimator model-level abstraction. Which environment should you train your model on?

   • AAVM on Compute Engine and 1 TPU with all dependencies installed manually.
   • BAVM on Compute Engine and 8 GPUs with all dependencies installed manually.
   • CA Deep Learning VM with an n1-standard-2 machine and 1 GPU with all libraries pre-installed.
   • DA Deep Learning VM with more powerful CPU e2-highcpu-16 machines with all libraries pre-installed.

   Show answer

   Answer: C

   Explanation

   Code without manual device placement => default to CPU if TPU is present or to the lowest order GPU if multiple GPUs are present. => Not A, B. D: already using CPU and needing GPU for CNN. Ans: C

   Source: Adapted from community discussion — Paul_Dirac

   References

   • Google Cloud documentation
   • Google Cloud documentation
   • developers.google.com documentation

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10. Question 250 · 364 in bank

    You work on a growing team of more than 50 data scientists who all use AI Platform. You are designing a strategy to organize your jobs, models, and versions in a clean and scalable way. Which strategy should you choose?

    • ASet up restrictive IAM permissions on the AI Platform notebooks so that only a single user or group can access a given instance.
    • BSeparate each data scientist's work into a different project to ensure that the jobs, models, and versions created by each data scientist are accessible only to that user.
    • CUse labels to organize resources into descriptive categories. Apply a label to each created resource so that users can filter the results by label when viewing or monitoring the resources.
    • DSet up a BigQuery sink for Cloud Logging logs that is appropriately filtered to capture information about AI Platform resource usage. In BigQuery, create a SQL view that maps users to the resources they are using

    Show answer

    Answer: C

    Explanation

    ans: c https://cloud.google.com/ai-platform/prediction/docs/resource-labels#overview_of_labels You can add labels to your AI Platform Prediction jobs, models, and model versions, then use those labels to organize resources into categories when viewing or monitoring the resources. For example, you can label jobs by team (such as engineering or research) and development phase (prod or test), then filter the jobs based on the team and phase. Labels are also available on operations, but these labels are derived from the resource to which the operation applies. You cannot add or update labels on an operation. A label is a key-value pair, where both the key and the value are custom strings that you supp

    Source: Adapted from community discussion — celia20200410

    References

    • Google Cloud documentation
    • Google Cloud documentation

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PMLE practice, question 241. 364 questions available.