Question # 1
Which of the following statements about the Wide & Deep Learning model are true? (Select 2 answers.)
| | A. The wide model is used for memorization, while the deep model is used for generalization.
| | B. A good use for the wide and deep model is a recommender system.
| | C. The wide model is used for generalization, while the deep model is used for memorization.
| | D. A good use for the wide and deep model is a small-scale linear regression problem. |
A. The wide model is used for memorization, while the deep model is used for generalization.
B. A good use for the wide and deep model is a recommender system.
Explanation
Can we teach computers to learn like humans do, by combining the power of memorization and
generalization? It's not an easy question to answer, but by jointly training a wide linear model (for
memorization) alongside a deep neural network (for generalization), one can combine the strengths of both to
bring us one step closer. At Google, we call it Wide & Deep Learning. It's useful for generic large-scale
regression and classification problems with sparse inputs (categorical features with a large number of possible
feature values), such as recommender systems, search, and ranking problems.
Question # 2
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. |
B. The CSV data has invalid rows that were skipped on import.
Question # 3
You work for a large fast food restaurant chain with over 400,000 employees. You store
employee information in Google BigQuery in a Users table consisting of a FirstName field
and a LastName field. A member of IT is building an application and asks you to modify the
schema and data in BigQuery so the application can query a FullName field consisting of
the value of the FirstName field concatenated with a space, followed by the value of the
LastName field for each employee. How can you make that data available while minimizing
cost?
| | A. Create a view in BigQuery that concatenates the FirstName and LastName field values
to produce the FullName.
| | B. Add a new column called FullName to the Users table. Run an UPDATE statement that
updates the FullName column for each user with the concatenation of the FirstName and
LastName values.
| | C. Create a Google Cloud Dataflow job that queries BigQuery for the entire Users table,
concatenates the FirstName value and LastName value for each user, and loads the
proper values for FirstName, LastName, and FullName into a new table in BigQuery.
| | D. Use BigQuery to export the data for the table to a CSV file. Create a Google Cloud
Dataproc job to process the CSV file and output a new CSV file containing the proper
values for FirstName, LastName and FullName. Run a BigQuery load job to load the new
CSV file into BigQuery. |
C. Create a Google Cloud Dataflow job that queries BigQuery for the entire Users table,
concatenates the FirstName value and LastName value for each user, and loads the
proper values for FirstName, LastName, and FullName into a new table in BigQuery.
Question # 4
Flowlogistic wants to use Google BigQuery as their primary analysis system, but they still
have Apache Hadoop and Spark workloads that they cannot move to BigQuery.
Flowlogistic does not know how to store the data that is common to both workloads. What
should they do?
| | A. Store the common data in BigQuery as partitioned tables.
| | B. Store the common data in BigQuery and expose authorized views.
| | C. Store the common data encoded as Avro in Google Cloud Storage.
| | D. Store he common data in the HDFS storage for a Google Cloud Dataproc cluster. |
B. Store the common data in BigQuery and expose authorized views.
Question # 5
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. |
A. Rewrite the job in Pig.
Question # 6
By default, which of the following windowing behavior does Dataflow apply to unbounded data sets?
| | A. Windows at every 100 MB of data
| | B. Single, Global Window
| | C. Windows at every 1 minute
| | D. Windows at every 10 minutes |
Dataflow's default windowing behavior is to assign all elements of a PCollection to a single, global window, even for unbounded PCollections
Question # 7
When you store data in Cloud Bigtable, what is the recommended minimum amount of stored data?
| | A. 500 TB
| | B. 1 GB
| | C. 1 TB
500 GB | | D. 500 GB |
Explanation
Cloud Bigtable is not a relational database. It does not support SQL queries, joins, or multi-row transactions. It
is not a good solution for less than 1 TB of data.
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