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    Exam Name: Databricks Certified Data Engineer Professional Exam
    Certification Name: Databricks Certification
    Exam Code: Databricks Certified Data Engineer Professional Question Answers
    Total Questions: 120
    Updates: Three Months Free Updates
    Guarantee: 100% Passing Assurance

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    Check Our Recently Added Professional-Data-Engineer Practice Exam Questions

    Question #1
    Your company has hired a new data scientist who wants to perform complicated analyses across very large datasets stored in Google Cloud Storage and in a Cassandra cluster on Google Compute Engine. The scientist primarily wants to create labelled data sets for machine learning projects, along with some visualization tasks. She reports that her laptop is not powerful enough to perform her tasks and it is slowing her down. You want to help her perform her tasks. What should you do?
    • A. Run a local version of Jupiter on the laptop.
    • B. Grant the user access to Google Cloud Shell.
    • C. Host a visualization tool on a VM on Google Compute Engine.
    • D. Deploy Google Cloud Datalab to a virtual machine (VM) on Google Compute Engine.
    Correct Answer(s):

    D. Deploy Google Cloud Datalab to a virtual machine (VM) on Google Compute Engine.

    Question #2
    You want to use a database of information about tissue samples to classify future tissue samples as either normal or mutated. You are evaluating an unsupervised anomaly detection method for classifying the tissue samples. Which two characteristic support this method? (Choose two.)
    • A. There are very few occurrences of mutations relative to normal samples.
    • B. There are roughly equal occurrences of both normal and mutated samples in the database.
    • C. You expect future mutations to have different features from the mutated samples in the database.
    • D. You expect future mutations to have similar features to the mutated samples in the database.
    • E. You already have labels for which samples are mutated and which are normal in the database.
    Correct Answer(s):

    B. There are roughly equal occurrences of both normal and mutated samples in the database.

    C. You expect future mutations to have different features from the mutated samples in the database.

    Question #3

    You are designing the database schema for a machine learning-based food ordering
    service that will predict what users want to eat. Here is some of the information you need to
    store:
    The user profile: What the user likes and doesn’t like to eat
    The user account information: Name, address, preferred meal times
    The order information: When orders are made, from where, to whom
    The database will be used to store all the transactional data of the product. You want to
    optimize the data schema. Which Google Cloud Platform product should you use?

    • A.

      BigQuery

    • B.

      Cloud SQL

    • C.

      Cloud Bigtable

    • D.

      Cloud Datastore

    Correct Answer(s):

    A. <p>BigQuery<br /><br /></p>

    Question #4

    Your company has recently grown rapidly and now ingesting data at a significantly higher
    rate than it was previously. You manage the daily batch MapReduce analytics jobs in
    Apache Hadoop. However, the recent increase in data has meant the batch jobs are falling
    behind. You were asked to recommend ways the development team could increase the
    responsiveness of the analytics without increasing costs. What should you recommend
    they do?

    • A.

      Rewrite the job in Pig.

    • B.

      Rewrite the job in Apache Spark.

    • C.

      Increase the size of the Hadoop cluster.

    • D.

      Decrease the size of the Hadoop cluster but also rewrite the job in Hive.

    Correct Answer(s):

    A. <p>Rewrite the job in Pig.<br /><br /></p>

    Question #5

    Your company is loading comma-separated values (CSV) files into Google BigQuery. The
    data is fully imported successfully; however, the imported data is not matching byte-to-byte
    to the source file. What is the most likely cause of this problem?

    • A.

      The CSV data loaded in BigQuery is not flagged as CSV.

    • B.

      The CSV data has invalid rows that were skipped on import.

    • C.

       The CSV data loaded in BigQuery is not using BigQuery’s default encoding.

    • D.

      The CSV data has not gone through an ETL phase before loading into BigQuery.

    Correct Answer(s):

    B. <p>The CSV data has invalid rows that were skipped on import.<br /><br /></p>